🧩 Software Engineering Agents Overview¶

🧠 Overview¶

The Software Engineering Agent Cluster is the execution layer of the ConnectSoft AI Software Factory. These agents are responsible for transforming architectural specifications and domain models into executable, observable, and production-ready systems.

They serve as the intelligent automation layer for software development, handling everything from backend services and frontend applications to infrastructure configuration, library generation, code versioning, and pull request orchestration — all driven by contracts defined upstream.

🧱 Engineering Agents in the Factory Lifecycle¶

Software engineering agents operate at the intersection between architecture and delivery. They take validated outputs from architecture agents — such as ports, use case contracts, transport configurations, and domain models — and implement them as fully functional software systems.

This includes:

Generating service scaffolding from templates
Writing controller, handler, adapter, repository, and client code
Configuring runtime infrastructure
Committing, reviewing, and merging code
Closing the loop with test coverage and build traceability

They are supported by generator agents that automate boilerplate creation and workflow agents that manage Git interactions and feedback cycles.

🧭 Strategic Layer Positioning¶

The Software Engineering phase is one of five primary execution layers within the ConnectSoft Agent System:

flowchart LR
    ProductVision --> ArchitectureDesign
    ArchitectureDesign --> SoftwareEngineering
    SoftwareEngineering --> DevOpsAutomation
    DevOpsAutomation --> QualityAssurance
    QualityAssurance --> ProductionRelease

Hold "Alt" / "Option" to enable pan & zoom

🎯 Objectives of Software Engineering Agents¶

The core goals of this agent cluster include:

Automated Implementation
Turn abstract ports, models, and architectural rules into concrete codebases.
Execution Traceability
Every code artifact carries trace IDs and metadata connecting it to upstream intent and downstream builds.
Modular, Observable Services
Ensure all services are layer-separated, span-traced, test-covered, and ready for CI/CD.
Collaboration Across Roles
Integrate tightly with architecture, DevOps, and QA agents in a continuous loop of delivery and feedback.

🧩 Role of Engineering in Agentic Development¶

Software Engineering Agents are not standalone. They collaborate across the ConnectSoft Factory:

Stage	Responsibility
Before	Accept contracts and templates from architects
During	Scaffold, write, generate, commit, and submit code
After	Pass artifacts to QA agents, CI/CD pipelines, and runtime orchestrators

These agents ensure that no architectural design remains unimplemented, no business rule untested, and no service unversioned.

✅ Deliverables Produced¶

The outputs of Software Engineering Agents include:

Source code (*.cs, *.ts, *.html, *.bicep)
Controllers, handlers, repositories, and adapters
Microservice scaffolds with DI and entrypoints
Pull requests with traceable links to features
Test harnesses and coverage summaries
Infrastructure scripts and Helm charts
Library SDKs and API clients for reuse

All deliverables are traceable, tagged, and Git-integrated with full observability support.

🔄 Connected Agents¶

Software Engineering Agents interoperate with:

Architect Agents → provide contracts, domain rules, API surface
DevOps Agents → consume commits, PRs, and manifests
Testing Agents → validate service correctness
Security Agents → enforce runtime safety policies
Documentation Agents → document endpoints and technical decisions

🚀 In short:
The Software Engineering Agent Cluster represents the automated workforce of ConnectSoft — bringing ideas to execution, and execution to production. These agents are the first ones to write code and the last ones to verify it before release. They are essential for building composable, scalable, and autonomous delivery pipelines, and ensure that engineering never becomes a bottleneck in the software factory lifecycle.

🎯 The Strategic Importance of Software Engineering Agents¶

In the ConnectSoft AI Software Factory, Software Engineering Agents are the key enablers of:

Autonomous system realization
Code-level traceability
Velocity and scalability of development
Execution of Clean Architecture boundaries
Consistent delivery of domain-aligned, production-grade services

They convert architectural structure into actual software — acting as agentic developers, generators, and workflow orchestrators in a system where code is not just written, but produced, validated, and committed automatically.

🔧 Execution Layer of the Factory¶

Engineering agents are the first to touch code and the last to submit it to CI/CD before DevOps agents take over.

They fulfill the “build” phase in the following end-to-end execution sequence:

sequenceDiagram
    participant Arch as Architecture Agents
    participant Eng as Software Engineering Agents
    participant DevOps as DevOps Agents
    participant QA as QA & Testing Agents
    participant Release as Deployment/Runtime Agents

    Arch->>Eng: Deliver use cases, ports, API specs, models
    Eng->>DevOps: Submit code, PRs, manifests
    Eng->>QA: Pass adapter/service for testing
    DevOps->>Release: Package and deploy validated builds

Hold "Alt" / "Option" to enable pan & zoom

📈 Without Engineering Agents¶

If this cluster were missing:

❌ Microservices would not be scaffolded or connected
❌ No adapters would be generated to implement ports
❌ Git commits would not be traceable to upstream intent
❌ Developers would need to hand-code from architectural contracts
❌ Velocity and coverage would degrade rapidly

✅ With Engineering Agents¶

By contrast, their presence enables:

Capability	Impact
Automated microservice creation	Teams can generate full services in seconds from Clean Architecture specs
Traceable implementation	Commits, PRs, and code all include `trace_id` and link to upstream features
Standardized PR pipelines	Pull requests are uniform, CI-ready, and include artifacts/tests
Testable adapters and use cases	Code follows DI patterns, has span injection, is unit-testable
Collaborative execution	Each engineer persona can be scaled independently or augmented with AI/human pairing

🧠 Connective Value in the Platform¶

Software Engineering Agents connect three major strategic layers:

From Architects: Input ports, models, use case contracts
To DevOps: Git repositories, build triggers, pipelines
To QA Agents: Testable implementations with mocks and trace spans

They are the linchpin of implementation — where thought becomes artifact, and system design becomes executable code.

🧩 Core Engineering Pillars¶

Software Engineering Agents enforce:

✅ Repeatability: Every new service is built the same way
✅ Observability: Every adapter and handler emits OTEL spans
✅ Composability: Shared libraries, DTOs, and mappers are reused
✅ Security: Generated clients and endpoints follow RBAC, auth, and TLS defaults
✅ Traceability: Git tags, trace IDs, PR titles, and build metadata are aligned

🔐 Compliance Alignment¶

Through traceable commits, decorated ports, and policy-injected infrastructure, these agents enforce:

SOC 2 and ISO 27001 compliance
Encrypted token usage for outbound clients
Git auditability for SDLC operations
Code quality scanning before merge (via QA/Static analysis agents)

🚀 In short:
The Software Engineering Agent Cluster is not optional — it is the core execution pipeline that turns ConnectSoft's architectural contracts into real-world systems. Without it, code would remain a manual task.
With it, code becomes autonomously generated, validated, and continuously delivered.

✅ Cycle 2 Completed

Shall we continue to Cycle 3 — Types of Engineering Agents?

🧩 Software Engineering Agents Overview¶

Cycle 3 — Types of Engineering Agents
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🧠 Purpose of This Cycle¶

This section introduces and classifies the various Software Engineering Agents that power the execution layer of the ConnectSoft AI Software Factory.

Each engineering agent acts as a role-specific software worker, with a clearly defined mission, set of inputs, output artifacts, and collaboration protocols. Collectively, they enable the full delivery lifecycle — from code creation to pull request submission — and are traceable, modular, and orchestratable.

🧱 Core Categories of Engineering Agents¶

The Software Engineering Agents can be grouped into the following role-based classifications:

1️⃣ Developer Role Agents¶

These simulate human developers writing code across layers.

Agent	Role
`Tech Lead Agent`	Oversees engineering direction, ensures adherence to architecture
`Backend Developer Agent`	Implements application logic, services, adapters
`Frontend Developer Agent`	Builds SPAs, UI components, state managers
`Mobile Developer Agent`	Constructs mobile features, handles device APIs and offline modes

2️⃣ Infrastructure-Integrated Agents¶

Agents that handle executable runtime configurations.

Agent	Role
`Infrastructure Engineer Agent`	Generates Helm charts, secrets, Bicep, Pulumi, YAML
`Microservice Generator Agent`	Produces full Clean Architecture microservice skeletons from templates
`API Gateway Generator Agent`	Creates gateway configs, route forwarding, CORS/security enforcement
`Library Generator Agent`	Emits modular, reusable code libraries (logging, DTOs, observability)
`API Library Generator Agent`	Produces strongly-typed API clients from OpenAPI/gRPC contracts

3️⃣ GitOps and Workflow Agents¶

Handle change management, source control hygiene, and pipeline interfacing.

Agent	Role
`Code Committer Agent`	Commits traceable, formatted, validated code
`Pull Request Creator Agent`	Builds PRs with metadata, links, test references, and trace IDs

4️⃣ Defect Response Agents¶

Agent	Role
`Bug Resolver Agent`	Responds to alerts, test failures, or QA signals; patches and validates code defects

🔄 Generator Agents as Execution Accelerators¶

In addition to the Developer Role Agents, ConnectSoft uses code generator agents to accelerate project velocity and ensure consistent scaffolding across services.

Generator	Description
`Microservice Generator`	Builds full Clean Architecture layout with solution(and projects) files, DI, controller scaffolds
`Adapter Generator` (cross-listed)	Implements infrastructure ports for persistence, messaging, outbound APIs
`Library Generator`	Supports common service needs (e.g., telemetry, mapping, validation, utilities)

These generators often collaborate with role agents (e.g., Backend Developer Agent) to insert logic into pre-generated scaffolds.

📦 Output Responsibility by Agent Type¶

Agent Type	Expected Output
Backend Developer	Use case handlers, validation, domain service adapters
Infrastructure Engineer	Helm charts, Kubernetes manifests, service bindings
Code Committer	`git commit`, tag, changelog fragment
PR Creator	Pull request body, branch diff, CI metadata
Mobile/Frontend Dev	SPA pages, components, API bindings
Bug Resolver	Patch + retest bundle with PR attached

All output is accompanied by trace metadata, including:
- trace_id
- agent_version
- artifact_type
- generated_at

🔁 Collaboration Summary¶

Software Engineering Agents operate independently, but collaborate through:

Shared input manifests (ports.json, transport-config.yaml)
Event signaling (CommitReady, PRCreated, BugFixRequired)
Traceable artifact registries (adapters-manifest, test-matrix, CI coverage reports)
Git workflows, handled by commit/pull agents

📌 Summary¶

Software Engineering Agents represent the human-free execution layer of the AI Software Factory.
Each one is responsible for a precise, traceable, and composable portion of code implementation, configuration, and delivery.

Their modular structure allows:

Plug-and-play evolution
Agent specialization
Scalability across large teams and complex projects

✅ Cycle 3 Completed

Shall we proceed to Cycle 4 — Agent Group Classifications?

🧩 Software Engineering Agents Overview¶

Cycle 4 — Agent Group Classifications
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section classifies the Software Engineering Agents into clear, functional groups that define their execution domain, collaboration responsibilities, and scope of impact within the ConnectSoft AI Software Factory.

Grouping agents by domain improves orchestration, traceability, and execution continuity — especially when generating or coordinating services at scale.

🧱 Engineering Agent Groups¶

Engineering agents in ConnectSoft are grouped into four primary categories:

🛠️ 1. Builders¶

Agents who create application logic and code artifacts based on architectural contracts.

Agent	Role
Backend Developer Agent	Implements use case handlers, input/output ports, validators, and domain logic
Frontend Developer Agent	Develops SPA components, route managers, API connectors, UI logic
Mobile Developer Agent	Builds cross-platform views, native device integrations, and offline sync layers
Infrastructure Engineer Agent	Produces IaC (Terraform, Bicep, Pulumi), Helm charts, service mesh, and DNS

These agents consume input models and templates and write actual service code, infrastructure config, or frontend UI artifacts.

🔁 2. Workflow Operators¶

Agents who manage change propagation through Git and automation pipelines.

Agent	Role
Code Committer Agent	Prepares `git commit`, formats staged changes, tags trace ID
Pull Request Creator Agent	Packages commits, creates PR with link to upstream epic/task
Bug Resolver Agent	Applies defect patches, triggers retest, and documents resolution path

Workflow operators are key to merging engineering artifacts into CI/CD, ensuring code is reviewed, validated, and testable before deployment.

🧪 3. Generators¶

Agents who rapidly scaffold projects, libraries, and contracts from templates.

Agent	Role
Microservice Generator Agent	Creates full Clean Architecture service folder structure
API Gateway Generator Agent	Generates gateway project with routing, rate-limiting, CORS
Library Generator Agent	Builds reusable domain/shared/internal libraries
API Library Generator Agent	Creates typed REST/gRPC clients from OpenAPI/gRPC contracts

Generators are fast, consistent, and parameterized — enabling engineers to build new modules or services in seconds, reducing manual effort and onboarding time.

⚙️ 4. Infrastructure Executors¶

Specialized engineers who produce runtime scaffolds and cloud infrastructure.

These are a bridge between engineering and DevOps layers.

Agent	Role
Infrastructure Engineer Agent	Outputs Kubernetes manifests, volume definitions, VNet rules, secrets injection, config maps
Adapter Generator Agent	Implements outbound ports (e.g., database repositories, blob storage, HTTP clients) for service interaction

Infrastructure Executors ensure that all service-level code has a working, observable runtime environment, and that adapters to external systems are implemented cleanly and safely.

🧬 Cross-Group Collaboration¶

These agent groups are not isolated. They often:

Invoke each other through events and input manifests
(e.g., Microservice Generator triggers Backend Developer and Adapter Generator Agents)
Share the same execution span trace ID
(e.g., trace_id: invoice-service-init-58322)
Publish and subscribe to Git, CI/CD, and runtime updates
(e.g., Pull Request Creator triggers CI pipelines consumed by QA Agent)

📦 Sample Role Collaboration Flow¶

flowchart TD
    ArchPortDefs --> MicroserviceGen
    MicroserviceGen --> BackendDev
    BackendDev --> AdapterGen
    AdapterGen --> Committer
    Committer --> PullRequestCreator
    PullRequestCreator --> CI

Hold "Alt" / "Option" to enable pan & zoom

📌 Summary¶

Software Engineering Agents are modular but cooperative — grouped by whether they:

Create code (Builders)
Move code (Workflow Operators)
Accelerate production (Generators)
Prepare runtime (Infrastructure Executors)

These groups work in sync to implement services at scale, while maintaining clean architecture, full traceability, and autonomous continuity.

✅ Cycle 4 Completed

Would you like to continue with Cycle 5 — Clean Architecture Enforcement?

🧩 Software Engineering Agents Overview¶

Cycle 5 — Clean Architecture Enforcement
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section details how Software Engineering Agents enforce and preserve Clean Architecture principles across all generated and implemented artifacts within the ConnectSoft AI Software Factory.

These agents ensure that the boundaries between domain, application, and infrastructure layers are never violated — even under high automation and generation pressure.

🧱 Enforcing Layered Architecture¶

Clean Architecture in ConnectSoft is modeled using three main concentric layers:

graph TD
    Domain[Domain Layer]
    Application[Application Layer]
    Infrastructure[Infrastructure Layer]
    UI[UI/API Layer]

    Domain --> Application
    Application --> Infrastructure
    Infrastructure --> UI

Hold "Alt" / "Option" to enable pan & zoom

Software Engineering Agents are responsible for:

Creating and maintaining these boundaries
Ensuring dependencies always point inward
Making sure external concerns are injected, not hardcoded
Guaranteeing that domain logic is decoupled from transport or persistence

🧩 Layer Responsibility Matrix¶

Layer	Agent Responsibilities
Domain Layer	Created by `Domain Modeler Agent`, extended by `Backend Developer Agent` with value objects, aggregates
Application Layer	Use case handlers (`HandleAsync`), orchestrated by `Backend Developer Agent`, consuming ports
Infrastructure Layer	Implemented by `Adapter Generator Agent`, supported by `Infrastructure Engineer Agent`
UI / External Interface	Constructed by `Frontend`, `Mobile`, and `API Gateway Generator Agents`

All generated and handwritten adapters implement input or output ports from the Application Layer — with no direct domain dependencies.

🔐 Enforced Rules¶

Enforcement	Description
🔁 No inward leaks	Infrastructure must not reference Application or Domain logic
🧪 Ports-only communication	Adapters talk through ports; they never "know" the domain
📦 DTO isolation	Internal DTOs are not leaked across the boundaries unless transformed
📁 File placement consistency	All code placed in `Domain/`, `Application/`, or `Infrastructure/Adapters/` by convention
🧠 Span and trace boundaries respected	Each layer emits separate spans, linked by parent context but scoped correctly

🛠️ Agent Roles in Clean Architecture¶

Agent	Clean Arch Role
Backend Developer Agent	Maintains application and domain layer integrity
Adapter Generator Agent	Wraps infrastructure concerns using well-defined output ports
Microservice Generator Agent	Scaffolds clean architecture layout with layer-separated projects and folders
Code Committer Agent	Ensures trace IDs, commits, and PRs reflect correct layering in message and file tree

📋 Span-Linked Layer Execution¶

Each adapter, handler, or controller emits its own OpenTelemetry span, scoped by layer:

Domain.ProcessUserLogic
Application.RegisterUserHandler.HandleAsync
Infrastructure.UserRepository.FindById
UI.UsersController.CreateUser

This not only helps debugging, but validates architectural separation through observability tooling.

🚫 Violation Handling¶

If an Engineering Agent detects violations, it:

Flags the code with a lint failure
Emits an ArchitectureViolationDetected event
Suggests correction via RefactorPlanIssued (future optional agent)

Example: A controller that directly accesses a repository instead of a use case handler will be auto-corrected to pass through the Application Layer.

📌 Summary¶

Software Engineering Agents are Clean Architecture guardians. They ensure:

That domain logic is pure and independent
That ports act as strict boundaries
That all external interactions are wrapped in adapters
That no shortcut or service-layer leak ever pollutes the codebase

This guarantees long-term maintainability, testability, and agentic interoperability across all services and systems.

✅ Cycle 5 Completed

Shall we proceed with Cycle 6 — Collaboration with Architecture Agents?

🧩 Software Engineering Agents Overview¶

Cycle 6 — Collaboration with Architecture Agents
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section explores the handshake between Architecture Agents and Software Engineering Agents, outlining how software engineers inherit design intent, apply Clean Architecture principles, and collaborate with modeling outputs to produce traceable, executable code.

In the ConnectSoft AI Software Factory, this collaboration is not optional — it is the central thread of continuity between vision and implementation.

🔗 Architecture → Engineering Handshake¶

Engineering Agents begin their work by consuming structured outputs from Architecture Agents, such as:

From	Artifact	Used By
`Solution Architect Agent`	`transport-config.yaml`, system blueprints	Microservice & Infra Engineers
`Application Architect Agent`	`input-ports.json`, `output-ports.json`	Backend Developer & Adapter Generator
`Domain Modeler Agent`	`domain-model.cs`, aggregates, enums	Backend Developer
`API Designer Agent`	`openapi.yaml`, `grpc.proto`	API Library Generator, Frontend Developer
`Security Architect Agent`	`auth-policies.yaml`, OAuth/OIDC flows	Backend, Frontend, Gateway Agents
`Event-Driven Architect Agent`	`event-map.yaml`, integration contracts	Adapter Generator, Messaging Consumers

These agents do not generate raw code themselves — instead, they define contracts, structures, and constraints that Engineering Agents execute against.

🧬 Inputs Consumed by Engineering Agents¶

Input File	Consuming Agents
`input-ports.json`	Backend Developer Agent, Microservice Generator
`output-ports.json`	Adapter Generator Agent
`transport-config.yaml`	Microservice Generator, Infrastructure Engineer
`domain-model.cs`	Backend Developer Agent
`adapter-policies.yaml`	Adapter Generator, Security-Compliance Hooks
`otel-agent-config.yaml`	All agents emitting spans
`openapi.yaml`, `grpc.proto`	API Library Generator Agent, UI Agents

These files are centrally versioned and traced with trace_id, ensuring that code artifacts always point back to the architectural decision or vision that originated them.

📋 Traceability Example¶

# backend/Handlers/RegisterUserHandler.cs
// trace_id: register-user-3182
// input_port: RegisterUserCommand
// domain_model: User.cs
// use_case: CreateUserHandler.cs

All Engineering Agent output must include a trace ID that links it to:

Product epic → Feature → Architecture spec
Domain model → Port → Use case → Adapter
Commit → Pull Request → CI Build

🤝 Examples of Direct Collaboration¶

Architectural Agent	Engineering Response
`Domain Modeler Agent` defines `User` with `Email`	`Backend Developer Agent` implements email validator
`Application Architect Agent` defines `RegisterUserCommand` input port	`Backend Developer Agent` creates handler scaffold with constructor injection
`API Designer Agent` publishes `POST /users`	`API Library Generator Agent` emits `UsersApiClient.cs` for use by Frontend Agent
`Security Architect Agent` declares `scope: user.write`	Backend and Gateway agents apply `[Authorize("user.write")]` attributes
`Event-Driven Architect Agent` maps `UserRegisteredEvent`	`Adapter Generator Agent` creates `UserRegisteredConsumer.cs` with tracing and retry logic

🧠 Engineering Respect for Architecture Constraints¶

Engineering Agents are responsible for:

Respecting architectural layering rules
Not leaking domain logic into infrastructure
Not bypassing ports/interfaces defined in application models
Not altering field or method semantics outside allowed overrides
Using only the templates, contracts, and topologies permitted for a given environment

If a violation is detected, a correction or escalation is issued via lifecycle events (ArchitectureViolationDetected, RefactorRequired, HumanEscalationProposed).

📌 Summary¶

Collaboration between Architecture and Engineering Agents in ConnectSoft ensures:

That every line of code originates from a structural design
That implementation always maps cleanly to planned features
That traceability is preserved across systems, teams, and tools

Software Engineering Agents are guardians of design fidelity — enforcing that architectural contracts are not only followed but reflected in every aspect of the code they produce.

✅ Cycle 6 Completed

Would you like to proceed with Cycle 7 — Tech Lead Agent Role?

🧩 Software Engineering Agents Overview¶

Cycle 7 — Tech Lead Agent Role
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the Tech Lead Agent — a pivotal engineering persona that acts as the technical coordinator, enforcement gatekeeper, and cross-functional integrator across all Software Engineering Agents within the ConnectSoft AI Software Factory.

🧠 Role Summary¶

The Tech Lead Agent represents the cross-cutting intelligence layer in the execution phase. Unlike developer agents that focus on specific output artifacts (e.g., handlers, charts, PRs), the Tech Lead Agent operates at a meta-layer, validating that all outputs:

Respect architectural constraints
Align with the intended epic, story, or feature
Are fully traceable, testable, and deployable
Integrate cleanly with CI/CD, observability, and compliance pipelines

🧩 Key Responsibilities¶

Domain	Responsibility
🎯 Validation	Review backend/frontend/microservice outputs for port, DTO, trace, span, and layering correctness
🤝 Orchestration	Coordinate workflows between Backend Developer, Infrastructure Engineer, Generator, Committer, and PR Agents
🔍 Inspection	Scan outputs for architecture violations, missing tests, incorrect layering, or policy drift
🔐 Security & Compliance Hooking	Ensure scopes, authentication, and adapter usage comply with security guidelines
🧪 Test Enforcement	Enforce test harness generation for handlers, clients, repositories, and UI logic
📦 Trace Completion	Verify trace_id presence across code, commits, manifests, PRs, and CI artifacts

🧬 Agent-to-Agent Collaboration¶

flowchart TD
    Backend --> TechLead
    Frontend --> TechLead
    MicroGen --> TechLead
    AdapterGen --> TechLead
    InfraEng --> TechLead
    CodeCommitter --> TechLead
    TechLead --> PullRequestCreator

Hold "Alt" / "Option" to enable pan & zoom

Reviews and verifies adapter correctness
Confirms that Microservice Generator output matches blueprint
Ensures test coverage is included for every commit
Flags cross-agent coordination issues (e.g., overlapping ports, missing domain references)

🧾 Input Artifacts¶

Artifact	Role
`domain-model.cs`	Validates entity consistency across handlers/repos
`input-ports.json` / `output-ports.json`	Cross-checks if all ports are implemented
`adapters-manifest.json`	Verifies adapter consistency with application layer
`test-matrix.json`	Ensures required test cases exist and are linked to commits
`trace-metadata.json`	Validates full execution trace exists per feature

📋 Decision Authority¶

The Tech Lead Agent has authority to:

Approve/reject PRs submitted by Pull Request Creator Agent
Block release pipeline via PullRequestRejectedDueToMissingTrace event
Emit HumanEscalationSuggested if gaps in architecture or modeling are detected
Override template-based generation logic only if conflict resolution requires custom adapter logic or DTO rewiring

🧠 Example Behaviors¶

🛑 Missing handler for defined port → reject PR
❌ Hardcoded repository inside controller → flag Clean Architecture violation
🔐 No [Authorize] attribute on protected API → issue Security Policy Mismatch
📭 Unmapped event consumer → mark service as incomplete
📦 Trace IDs missing from adapters and commits → regenerate commits with trace_id: X

📌 Summary¶

The Tech Lead Agent is not just a reviewer — it is the technical governance agent within Software Engineering. It ensures:

Fidelity to architectural and security policy
Full traceability of engineering artifacts
Coordination and sequencing across execution agents
Enforcement of test, span, and compliance completeness

In ConnectSoft, the Tech Lead Agent is the autonomous equivalent of a real-world lead engineer — enforcing standards, unblocking issues, and maintaining the integrity of service development.

✅ Cycle 7 Completed

Shall we continue to Cycle 8 — Backend Developer Agent Role?

🧩 Software Engineering Agents Overview¶

Cycle 8 — Backend Developer Agent Role
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section details the mission, responsibilities, and execution flow of the Backend Developer Agent — a foundational software engineering persona in the ConnectSoft AI Software Factory. This agent is responsible for building the core logic and behaviors of each microservice, while strictly adhering to Clean Architecture principles.

🧠 Role Summary¶

The Backend Developer Agent is the primary implementer of service-side logic. It transforms input and output port definitions into executable handlers, applies domain models, and integrates use cases, validators, and interaction boundaries.

It operates directly on architecture-generated scaffolds and is responsible for:

Writing use case handlers for commands and queries
Binding ports to domain logic
Implementing service-side validation
Producing OTEL-instrumented, testable, and layered application logic
Ensuring all service logic is encapsulated, composable, and independently testable

📦 Core Responsibilities¶

Domain	Responsibility
⚙️ Use Case Implementation	Generate `HandleAsync(...)` logic for input ports using injected domain services
📑 Validation Layer	Attach validation via FluentValidation or data annotations
🧩 DTO and Mapper Integration	Transform inbound DTOs to domain entities and back
🧠 Domain Model Consumption	Interact with `User`, `Invoice`, or other aggregate roots from Domain Modeler Agent
📎 Port Binding	Implement defined input and output ports exactly as declared in `input-ports.json` / `output-ports.json`
🔄 Span Injection	Emit spans around core application logic for observability
🧪 Test Hooking	Write logic in a testable format, inject dependencies via constructor, follow `IUseCase<TIn, TOut>` signature patterns

📁 Output Artifacts¶

File	Description
`Handlers/RegisterUserHandler.cs`	Command handler for use case defined by `RegisterUserCommand`
`Validators/RegisterUserValidator.cs`	Request validator generated via FluentValidation
`Mappers/UserMapper.cs`	Maps domain → DTO and back
`Application/UseCases/*.cs`	Layered logic tied to input ports
`Application/Ports/*.cs`	Interfaces for interaction boundaries
`Tests/Application/Handlers/*Tests.cs`	Unit tests (generated in partnership with Test Generator Agent)

🔄 Collaboration with Other Agents¶

Agent	Collaboration Flow
`Tech Lead Agent`	Reviews handler correctness, DTO use, and layering
`Domain Modeler Agent`	Supplies domain entities and invariants
`Adapter Generator Agent`	Implements outbound dependencies (repos, clients) consumed by backend logic
`Microservice Generator Agent`	Provides scaffolding into which the Backend Developer Agent injects logic
`Code Committer Agent`	Packages backend changes and formats commit message with trace ID

📌 Clean Architecture Enforcement¶

All handler and use case logic must:

Live in the Application Layer
Depend only on ports and domain entities
Be constructor-injected, with no static calls or hard references to infrastructure
Include OTEL spans and cancellation token support
Be covered by a test harness if the port is non-trivial

Any violation triggers a CleanArchitectureViolationDetected signal.

🔍 Example Workflow¶

Given:

{
  "port": "RegisterUserCommand",
  "input": "RegisterUserDto",
  "output": "UserDto"
}

The agent produces:

RegisterUserHandler.cs with HandleAsync(dto, ct)
Span-injected, policy-decorated, and domain-compliant handler logic
DTO validation and mapping
Matching test class and registration in DI

📌 Summary¶

The Backend Developer Agent is the heart of application logic realization in the ConnectSoft factory.
It takes abstract architecture and turns it into compiled, tested, layered software logic — ready for CI/CD and integration into the greater system.

Its strict enforcement of architecture and its cooperation with adapters, mappers, validators, and test scaffolds makes it a central contributor in the autonomous development pipeline.

✅ Cycle 8 Completed

Shall we proceed with Cycle 9 — Frontend Developer Agent Role?

🧩 Software Engineering Agents Overview¶

Cycle 9 — Frontend Developer Agent Role
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the mission and responsibilities of the Frontend Developer Agent, the persona responsible for building user-facing web applications in the ConnectSoft AI Software Factory. This agent translates UI designs, interaction flows, and API definitions into interactive, accessible, and responsive web interfaces.

🧠 Role Summary¶

The Frontend Developer Agent implements the presentation layer of SaaS platforms generated within the Factory. Working from specs defined by the UI/UX cluster and contract surfaces generated by API Designer and API Library Generator Agents, it creates reusable components, forms, dashboards, and workflows — with direct links to backend services.

The agent ensures that:

UI logic is reactive, modular, and framework-consistent
All inputs/outputs are strongly typed and DTO-aligned
Security hooks (e.g., auth tokens) are respected
Layout and behavior match UI designs provided by design agents
Code is testable, observable, and responsive to state changes

📦 Core Responsibilities¶

Domain	Responsibility
🧱 Component Development	Generate SPAs using frameworks like Blazor, Angular, or Vue
🔗 API Integration	Bind API clients (from API Library Generator Agent) into data layers
🧮 State Management	Manage reactive forms, input validation, and user state contexts
📐 Layout Rendering	Build page templates, dashboards, step-based flows, and modals
🧪 UI Test Hooks	Emit test IDs and telemetry identifiers for QA automation agents
📡 Telemetry Injection	Add span emitters for user interactions and screen transitions
🔒 Security Compliance	Ensure authenticated calls include scopes, tokens, and sanitized inputs

📁 Output Artifacts¶

File/Folder	Description
`Pages/`	Page-level components (e.g., `UserRegistrationPage.razor`)
`Components/`	Reusable UI blocks (e.g., `InputTextField.razor`, `NotificationCard.vue`)
`Services/`	API interaction classes (`UsersApi.ts`, `InvoicesApiClient.cs`)
`State/`	State containers, contexts, stores
`wwwroot/`	Static assets, CSS, i18n bundles
`Tests/`	Unit or E2E tests (generated with Test Generator Agent)

🧬 Agent Collaboration¶

Agent	Collaboration
`UI Designer Agent`	Provides layout components and interaction behavior specs
`UX Designer Agent`	Supplies accessibility guidelines, form flows, and mobile breakpoints
`API Designer Agent`	Defines endpoints and methods for integration
`API Library Generator Agent`	Supplies auto-generated clients for typed integration
`Security Architect Agent`	Injects authentication and authorization rules
`Test Generator Agent`	Scaffolds test hooks and mock environments for UI testing
`Code Committer Agent`	Packages UI changes into commits with linked trace IDs and ticket refs

🎨 Framework Agnosticism with Template Support¶

Frontend Developer Agent supports:

Framework	Notes
Blazor	Component-based .NET frontend — deep integration with backend logic and shared models
Angular	TypeScript-heavy SPAs — supports forms, services, DI, RxJS
Vue.js	Lightweight, state-driven apps with component-first composition
(Optional: React support can be added via Generator Plugins)

Framework choice is influenced by the Microservice Generator Agent or Self-Service Portal Builder Agent through frontend-framework.yaml.

🔐 Security & Token Handling¶

The agent respects frontend-side responsibilities:

Attaching OAuth2 Bearer tokens
Managing refresh tokens and auth state
Applying roles/scopes to UI rendering (e.g., hiding admin-only features)

Tokens are retrieved through auth-context.ts or .NET AuthenticationStateProvider, depending on stack.

🧪 UI Telemetry and Testability¶

Every page and interaction includes:

data-testid hooks
span("PageViewed") OTEL emitter
Log-level instrumentation for form errors, latency, or invalid navigation

These are consumed by:

Observability Engineer Agent
QA Automation Agent
Resiliency & Chaos Engineer Agent (for user-level error handling tests)

📌 Summary¶

The Frontend Developer Agent is the key implementer of the visual experience and interaction logic in ConnectSoft’s Factory-generated apps. It collaborates with design, architecture, and backend agents to ensure that:

UI reflects feature intent
Code is testable and secured
State flows are observable and DTO-aligned
The user experience is not just aesthetic — but production-ready and event-aware

✅ Cycle 9 Completed

Shall we continue to Cycle 10 — Mobile Developer Agent Role?

🧩 Software Engineering Agents Overview¶

Cycle 10 — Mobile Developer Agent Role
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the role and scope of the Mobile Developer Agent — the autonomous persona responsible for building mobile applications in the ConnectSoft ecosystem, including cross-platform, native, and hybrid experiences.

🧠 Role Summary¶

The Mobile Developer Agent creates native or cross-platform mobile interfaces that:

Align with backend APIs, domain logic, and authentication flows
Respect design language provided by UX/UI agents
Implement mobile-specific features like push notifications, camera access, offline sync
Integrate with the shared libraries and typed API clients generated elsewhere in the system

Its work is consistent with Clean Architecture principles and observability-first execution — ensuring that all UI interactions, events, and background tasks are traced, testable, and policy-compliant.

📦 Core Responsibilities¶

Domain	Responsibility
📱 UI Construction	Build screens, flows, and components for onboarding, forms, dashboards
🔌 API Integration	Use typed mobile clients (from API Library Generator Agent) for backend interaction
🌐 State and Sync Logic	Handle caching, offline storage, background sync with retry policies
📡 Telemetry Injection	Add spans and event tracking to screen views and actions
🔐 Authentication	Integrate OAuth2/OIDC flows using mobile-friendly login methods
🎯 Platform-Specific Logic	Handle camera, geolocation, biometric auth, and local notifications
🧪 Testability	Structure views and services with test IDs and E2E test support (via Detox/Appium)

📁 Output Artifacts¶

Artifact	Description
`screens/`	Views (e.g., `RegisterScreen.tsx`, `DashboardView.kt`)
`components/`	Shared UI blocks (buttons, forms, lists)
`services/`	API interaction (e.g., `InvoiceService`, `UserService`)
`auth/`	Token handling and session management
`state/`	Contexts or stores for persistent/shared state
`native/`	Platform-specific logic (Android/iOS modules)
`tests/`	Unit + E2E test scaffolds

🤝 Agent Collaboration¶

Agent	Role in Collaboration
`UX Designer Agent`	Supplies interaction flows, mobile layouts, tap zones
`API Library Generator Agent`	Provides typed client SDKs for use in services
`Security Architect Agent`	Defines token strategies and scope bindings
`Observability Engineer Agent`	Consumes spans emitted from user actions and network layers
`Test Generator Agent`	Builds unit/E2E tests and coverage map
`Frontend Developer Agent`	Shares components and styling tokens if hybrid approach is used

📱 Framework and Stack Support¶

Stack	Support
Xamarin / MAUI	Native .NET integration — ideal for code reuse with backend logic
React Native	Cross-platform mobile with JS/TS support, ideal for shared component libraries
Flutter (future support)	Potential plugin via Generator Agents
Native Swift/Kotlin	Supported where domain requires strict platform-level control (e.g., biometric auth, push security)

Framework choice is determined by the mobile-framework.yaml provided by Tech Lead or Self-Service Portal Builder Agents.

🔐 Security and Auth Handling¶

Mobile Developer Agent must ensure:

Tokens are stored securely (Keychain/EncryptedStorage)
Background syncs are permission-bound
Login state is persistent across app sessions
OAuth2 flows are compatible with mobile constraints (e.g., WebView fallback, code exchange)

📈 Observability and Mobile Events¶

Instrumentation includes:

Spans for screen views (e.g., ScreenViewed:RegisterScreen)
Events for user interaction (e.g., ButtonTapped:SubmitInvoice)
Errors (network timeouts, auth failures, validation issues)
Retry attempts for background workers

All logs and traces are exportable to OTLP, App Center, or Firebase depending on platform config.

📌 Summary¶

The Mobile Developer Agent makes ConnectSoft SaaS features accessible anywhere — executing mobile logic that’s consistent with backend contracts, frontend patterns, and design standards.

Its mobile-first capabilities ensure:

Offline tolerance
Real-time telemetry
Seamless API integration
Secure and observable user flows

Whether generating screens or syncing state, it is a first-class engineering persona in the factory ecosystem.

✅ Cycle 10 Completed

Shall we proceed with Cycle 11 — Infrastructure Engineer Agent Role?

🧩 Software Engineering Agents Overview¶

Cycle 11 — Infrastructure Engineer Agent Role
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the role and scope of the Infrastructure Engineer Agent, the software engineering persona responsible for creating and configuring runtime environments, deployment manifests, and infrastructure as code (IaC) in the ConnectSoft AI Software Factory.

🧠 Role Summary¶

The Infrastructure Engineer Agent builds the runtime foundation for each microservice and application — ensuring that services:

Are deployable via CI/CD pipelines
Conform to security and observability policies
Integrate seamlessly with cloud-native environments (e.g., AKS, EKS, GKE)
Include Kubernetes manifests, Helm charts, sidecars, volume mounts, secret management
Comply with Clean Architecture deployment boundaries and platform runtime standards

It is the agent that bridges the application layer and the execution layer — ensuring what’s built can run, scale, and be operated reliably.

📦 Core Responsibilities¶

Domain	Responsibility
🏗️ Kubernetes Manifests	Generate `Deployment`, `Service`, `Ingress`, `ConfigMap`, and `Secret` YAMLs
🧰 Helm Chart Generation	Create Helm charts for reusable microservice deployment patterns
☁️ IaC Scripts	Emit Bicep, Terraform, or Pulumi templates for infrastructure provisioning
🔐 Secrets & Certificates	Configure secret mounts, environment variable encryption, TLS cert paths
🧭 Service Mesh & Ingress	Integrate with Istio, Linkerd, NGINX Ingress, or Azure App Gateway
📊 Observability Setup	Add OTEL sidecars, exporters, Prometheus annotations, liveness/readiness probes
🔁 Scalability & Auto-Recovery	Configure `HorizontalPodAutoscaler`, restart policies, probes

📁 Output Artifacts¶

File	Description
`k8s/deployment.yaml`	Base deployment config with image, env, probes
`k8s/service.yaml`	Exposes internal service to cluster
`k8s/ingress.yaml`	Maps domain → service routes with TLS
`helm/Chart.yaml`	Helm chart metadata
`helm/templates/*.yaml`	Parameterized templates for shared modules
`iac/service.bicep` or `main.tf`	Cloud resource provisioning (AKS, PostgreSQL, Redis, etc.)
`otel/agent-config.yaml`	OpenTelemetry config for trace/log export
`config/runtime-env.yaml`	Service-specific settings for environment-specific values

🤝 Agent Collaboration¶

Agent	Collaboration Flow
`Microservice Generator Agent`	Provides project layout and service metadata
`Security Architect Agent`	Supplies authentication, RBAC, and encryption rules
`Observability Engineer Agent`	Defines OTEL exporters, scraping configs
`DevOps Architect Agent`	Validates infra patterns, injects pipeline rules
`Cloud Provisioner Agent`	Uses IaC to spin up environments from templates
`Tech Lead Agent`	Approves service mesh, secrets, and cluster scope behaviors
`Code Committer Agent`	Commits and tags infrastructure changes as part of coordinated PRs

🧠 Runtime Integration Features¶

Area	Feature
Security	API key rotation, Azure Key Vault binding, cert manager integration
Service Discovery	DNS naming via `.svc.cluster.local`, mesh-bound endpoints
Multi-Tenancy	Namespace-based isolation or label-based routing
Resilience	Crash loops, readiness probes, health checks, restart policies
Cost Optimization	Annotations for resource limits, scaling hints, spot VM pools

🔧 Environment-Aware Behavior¶

Manifests and IaC outputs are parameterized based on:

environment: dev | staging | production
region: westeurope | eastus
tier: internal | customer-facing
service_class: standard | critical | background

This ensures the same codebase can be deployed into multiple cloud-native environments without rewriting infrastructure logic.

📌 Summary¶

The Infrastructure Engineer Agent is the key to bringing software to life inside the cloud.

It produces infrastructure blueprints, manages runtime contracts, and ensures every service is securely, observably, and scalably deployed in accordance with platform standards.

Without it, services would remain unshipped — with it, services become production-grade, cloud-ready workloads in seconds.

✅ Cycle 11 Completed

Shall we proceed to Cycle 12 — Code Committer and Pull Request Creator Agents?

🧩 Software Engineering Agents Overview¶

Cycle 12 — Code Committer and Pull Request Creator Agents
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the responsibilities of two tightly coordinated workflow agents in the ConnectSoft AI Software Factory:
- The Code Committer Agent
- The Pull Request Creator Agent

These agents represent the GitOps interface for the entire Software Engineering execution chain, handling traceable source control operations, branch hygiene, and delivery readiness.

🧠 Agent Role Summaries¶

✅ Code Committer Agent¶

This agent is responsible for finalizing and staging source code changes, ensuring:

Proper formatting and linting
Commit messages that include trace IDs
Clean layer alignment in file structure
Enforcement of architectural tags, component-level ownership, and feature refs
Git commit consistency across services, libraries, and infrastructure

It prepares the final commit after all logic, configurations, or templates have been applied by other engineering agents.

✅ Pull Request Creator Agent¶

This agent packages commits into PRs, ensuring that:

PR titles and descriptions are well-formed and semantically tagged
All required reviewers are listed based on affected modules
Related test coverage and CI status are attached
Git branches align with release policy and branching strategy
Links to architectural source (trace_id, epic_id, ticket_ref) are embedded

The PR created by this agent acts as the handoff point from Engineering to DevOps and QA.

📦 Commit Output Format¶

feat(auth): implement RegisterUserHandler and SMTP adapter

Includes:
- input port: RegisterUserCommand
- use case: RegisterUserHandler.cs
- adapter: SmtpEmailSender.cs
- trace_id: register-user-3182
- layer: application + infrastructure
- test: RegisterUserTests.cs

All commits use semantic versioning prefixes (feat, fix, refactor, chore, etc.), and include:

trace_id: (required)
layer: (application, infrastructure, ui, etc.)
affected_ports: if relevant
auth_scope: if touching security-restricted features

📁 PR Metadata Structure¶

Each pull request includes:

Field	Description
Title	`feat(auth): register user via SMTP handler`
Description	Bullet-point summary of ports, adapters, and use cases
Branch	`feature/register-user-3182`
Trace ID	Reference to architecture and product planning (`register-user-3182`)
CI Metadata	GitHub Actions or Azure DevOps build link, status badge
Test Coverage Link	Test results attached if test generation was triggered
Required Reviewers	Derived from changed modules or scopes

🔄 Agent Collaboration¶

Agent	Integration
`Backend Developer Agent`	Provides files to be committed
`Adapter Generator Agent`	Outputs infrastructure adapters included in commit
`Infrastructure Engineer Agent`	Supplies k8s/IaC files
`Test Generator Agent`	Ensures test stubs are staged alongside features
`Tech Lead Agent`	Reviews commits and PRs before final approval
`DevOps Engineer Agent`	Triggers CI/CD on PR submission or merge

🛡️ Compliance & Validation¶

These agents enforce:

📌 Traceability — every change maps to a trace_id linked to upstream architecture
✅ Code Hygiene — commit linting, formatted diffs, and signed commits
🔐 Security Zones — PRs touching sensitive scopes (auth, payments, analytics) require additional reviewers
🧪 Test Attachments — auto-detect if test coverage is missing and raise a flag
🔁 PR Retry Flow — if build fails or violations occur, the PR is marked with failure metadata and flagged for correction

📊 Observability for Commit/PR Agents¶

Metric	Description
`commits_per_trace`	Average number of commits per feature/trace ID
`prs_per_service_per_week`	PR cadence across services
`unmerged_pr_age`	Aging PRs pending review
`testless_commit_ratio`	Commits missing attached tests

These metrics feed back into DevOps and QA planning cycles.

📌 Summary¶

The Code Committer Agent and Pull Request Creator Agent are the delivery pipeline activators in the ConnectSoft Factory. They take validated implementation and make it versioned, visible, and verifiable in Git — the universal source of truth.

Their work ensures that:

Every engineering output becomes traceable history
CI/CD integration is seamless
Approvals and reviews are automatable
Nothing enters production without a full execution lineage

✅ Cycle 12 Completed

Shall we continue to Cycle 13 — Bug Resolver Agent Role?

🧩 Software Engineering Agents Overview¶

Cycle 13 — Bug Resolver Agent Role
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section outlines the mission and execution flow of the Bug Resolver Agent — the Software Engineering persona in the ConnectSoft AI Software Factory responsible for analyzing and fixing issues detected during testing, integration, runtime, or observability stages.

It operates as part of the corrective feedback loop, ensuring that defects, regressions, or runtime failures are resolved efficiently, traceably, and with full quality assurance alignment.

🧠 Role Summary¶

The Bug Resolver Agent acts in response to:

Failed test executions (unit, integration, load)
Observability alerts (errors, retries, spans marked failed)
Quality Assurance Agent signals
PR reviewer feedback (from the Tech Lead Agent or Code Reviewer Agent)

It locates the root cause of the issue — within service logic, adapter boundaries, validation, or infrastructure config — and emits a tested, traceable patch.

🔁 Typical Execution Triggers¶

Trigger	Description
❌ `TestFailureDetected`	A test failed during CI, triggering the need for investigation
❗ `SpanErrorDetected`	A service span is marked with `status: error` and stack trace
🧪 `QaAgentValidationFailed`	Manual or automated QA reported a defect
🔁 `BugReportLinkedToPR`	PR feedback includes an issue reference

🛠️ Core Responsibilities¶

Domain	Responsibility
🔍 Root Cause Analysis	Read error messages, trace spans, test logs, stack traces
🧰 Code Correction	Fix logic, null checks, DTO mismatches, or configuration issues
🧪 Regression Test Creation	Add or modify tests to prove the issue is fixed
🔗 Trace & Commit Linking	Patch is tagged with `bug_id`, `trace_id`, and associated test result
🔁 Patch Commit and PR	Collaborates with Committer and PR Creator to submit fix
🧠 Feedback Loop Participation	Posts resolution metadata back to QA or Observability agents

📁 Output Artifacts¶

Artifact	Description
`Fix/UserEmailSanitizer.cs`	Bug fix within domain validation logic
`Tests/UserEmailValidatorTests.cs`	Regression test for the fixed case
`FixMetadata/bug-54321.yaml`	Details of the bug root cause, location, and resolution timestamp
`git commit`	Commit tagged with `fix(bug-54321): sanitize email input from form`

🔁 Agent Collaboration¶

Agent	Role
`QA Engineer Agent`	Initiates or links defects from test results
`Observability Engineer Agent`	Signals failed spans or runtime errors
`Tech Lead Agent`	Verifies severity and ownership zone
`Test Automation Agent`	Ensures test coverage is updated post-fix
`Code Committer Agent`	Commits the fix into the correct release branch
`Pull Request Creator Agent`	Packages the fix into a labeled PR for merge & validation

🧪 Bug Lifecycle Traceability¶

Each bug fix is linked back to:

A trace_id
A bug_id (internal or from external systems like Jira)
Associated test failures or error logs
Upstream ports or modules involved

Example:

bug_id: BUG-54321
trace_id: register-user-3182
affected_module: RegisterUserHandler.cs
test_covered: true
resolved_by: Bug Resolver Agent v2.1
timestamp: 2025-05-02T12:38:00Z

📊 Metrics & Oversight¶

Metric	Purpose
`mean_bug_resolution_time`	Average time to close a reported defect
`bugs_per_module`	Frequency of issues in services, adapters, or infrastructure
`testless_fixes`	Ratio of bug fixes missing tests (should be 0%)
`reopened_bugs`	Bugs that reoccur post-merge (indicates regression issue)

These metrics feed into quality scoring for modules and platform evolution planning.

📌 Summary¶

The Bug Resolver Agent plays a critical role in maintaining the health and trustworthiness of ConnectSoft-generated software systems.

Its structured, traceable, and test-driven fix loop ensures that:

Every defect has a lifecycle
Every patch is tied to observable evidence
Every resolution includes a validation artifact

It closes the loop between engineering, QA, observability, and runtime — making the platform self-healing and production-grade.

✅ Cycle 13 Completed

Shall we continue with Cycle 14 — Microservice Generator Agent?

🧩 Software Engineering Agents Overview¶

Cycle 14 — Microservice Generator Agent
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the Microservice Generator Agent, a pivotal generator agent that autonomously produces fully structured, clean-architecture-compliant microservices within the ConnectSoft AI Software Factory. It serves as the foundational automation engine that translates architectural blueprints and service definitions into ready-to-implement engineering scaffolds.

🧠 Role Summary¶

The Microservice Generator Agent is triggered when a new service is initiated based on:

Business capability boundaries
Application Architect Agent outputs (input-ports.json, output-ports.json)
Domain Modeler Agent artifacts (domain-model.cs)
Transport and infrastructure config (transport-config.yaml)

It creates a full working solution structure, ready for engineering agents to inject logic, register adapters, and apply runtime and deployment integrations.

🧱 Output Structure¶

The agent produces:

/services/UserService
├── Domain/
│   └── Entities, ValueObjects, Enums
├── Application/
│   └── UseCases, Ports, DTOs, Validators
├── Infrastructure/
│   └── Adapters, Repositories, Clients
├── Api/
│   └── Controllers, REST/gRPC Entry Points
├── Tests/
│   └── Unit and Integration Stubs
├── Program.cs, Startup.cs
├── Microservice.sln
└── .editorconfig, Dockerfile, .gitignore

All files include metadata headers such as:

// generated_by: MicroserviceGeneratorAgent
// trace_id: user-service-2025-05-02
// generated_at: 2025-05-02T13:05:00Z

🛠️ Responsibilities¶

Area	Action
🧱 Solution/Project Initialization	Creates `.sln`, `.csproj`, DI config, and folder layout
🧩 Port Injection	Generates interface stubs for each input/output port
🧠 Domain Model Integration	Embeds domain entities from Domain Modeler Agent
🔁 Template Composition	Combines selected backend/frontend/infrastructure templates
🔧 Starter Configuration	Sets default transport, serializer, healthcheck, logging, tracing
📦 Test Ready	Generates test folders and entry-point specs with placeholders

🤝 Collaboration Flow¶

Agent	Collaborates With
`Application Architect Agent`	Provides service shape, contracts
`Domain Modeler Agent`	Supplies aggregates and value objects
`Adapter Generator Agent`	Builds out input/output adapter implementations
`Backend Developer Agent`	Injects use case logic inside the scaffolded handlers
`Tech Lead Agent`	Reviews structure compliance, scope correctness
`Infrastructure Engineer Agent`	Integrates Helm, manifests, and config templates
`Code Committer Agent`	Finalizes generated structure for versioning and PR creation

🧩 Template System and Composition¶

The agent uses a rich library of templated modules, parameterized by:

service_name, trace_id, environment
transports: REST, gRPC, messaging
db_driver: NHibernate, EF Core, Dapper
auth_model: OAuth2, API Key
layering_scheme: Clean Architecture enforced

Template types include:

Template Category	Examples
`HandlerTemplate`	`CommandHandler.cs`, `QueryHandler.cs`
`ControllerTemplate`	`UsersController.cs`, `InvoicesGrpcService.cs`
`RepositoryTemplate`	`NHibernateUserRepository.cs`
`ClientAdapterTemplate`	`CrmHttpClient.cs`
`DTOStubTemplate`	`CreateUserDto.cs`
`StartupTemplate`	`Program.cs`, `Startup.cs`
`DockerTemplate`	`Dockerfile`, `.dockerignore`
`TestStubTemplate`	`UserServiceTests.cs`

📁 Integration with Git & Traceability¶

Generates Git branch naming convention: feature/{service-name}-{trace-id}
Prepares commit message template
Registers service in services-manifest.yaml
Emits MicroserviceScaffoldCreated lifecycle event

📊 Metrics and Observability¶

Metric	Description
`scaffold_time_ms`	Time to generate microservice
`template_version`	Version of template used
`input_ports_scaffolded`	Total input ports included
`test_files_generated`	Stubs created for handler and controller layers
`architecture_compliance_passed`	Whether layers and structure match ConnectSoft blueprint

📌 Summary¶

The Microservice Generator Agent is the assembly line starter in the Software Engineering phase. It lays the foundation for all other engineering agents to:

Build on structured layers
Maintain Clean Architecture
Write testable logic
Deploy with confidence
Trace execution back to architecture

It ensures every service begins consistently, compliantly, and ready for automation.

✅ Cycle 14 Completed

Would you like to continue with Cycle 15 — API Gateway / Library / API Library Generator Agents?

🧩 Software Engineering Agents Overview¶

Cycle 15 — API Gateway / Library / API Library Generator Agents
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section introduces three key generator agents that support composability, reuse, and integration across the ConnectSoft platform:

API Gateway Generator Agent
Library Generator Agent
API Library Generator Agent

These agents specialize in generating infrastructure glue, shared code modules, and typed API clients, respectively — all critical for enabling inter-service communication, reducing duplication, and maintaining consistency.

🧠 Role Summaries¶

🌐 API Gateway Generator Agent¶

Responsible for producing gateway services that expose and route external traffic to internal microservices.

Responsibilities
Generate gateway project with OpenAPI-first routing
Configure reverse proxies and CORS policies
Inject authentication filters (API key, OAuth2, RBAC)
Add rate limiting, request tracing, and tenant separation
Emit observability hooks (request spans, metrics, headers)
Register service metadata and gateway mappings into mesh configs or ingress controllers

📦 Library Generator Agent¶

Creates shared internal libraries used across services. Libraries are built as composable .NET or TS projects and contain:

Types of Libraries
Domain libraries (e.g., `ConnectSoft.Billing.Domain`)
Utilities (e.g., `ConnectSoft.Logging`, `ConnectSoft.Resilience`)
Validation logic (e.g., shared FluentValidation rule sets)
Mapping libraries (e.g., AutoMapper profiles for DTO ↔ Domain)
Shared configuration providers (e.g., tracing, retry policies, auth scopes)

🔌 API Library Generator Agent¶

Produces typed client SDKs for REST and gRPC services by consuming:

openapi.yaml from the API Designer Agent
proto files from the gRPC Definition Provider
Environment and trace metadata from architectural config

Generated libraries are usable by:

Frontend/Mobile Developer Agents
Other microservices (as typed internal clients)
Test Generator Agent (for integration test scaffolds)

📁 Output Artifacts¶

Agent	Output
API Gateway Generator	`gateway.yaml`, `routes.json`, `Startup.cs`, `RequestLoggingMiddleware.cs`
Library Generator	`ConnectSoft.Shared.Validation.csproj`, `DtoValidator.cs`, `ValueObjectExtensions.cs`
API Library Generator	`UsersApiClient.cs`, `InvoicesGrpcClient.cs`, `openapi-client.nuspec`

All outputs are:

Packaged into .nupkg, .tgz, or project references
Traceable with trace_id, generated_by, generated_at headers
Automatically registered in the platform manifest

🤝 Collaboration Flows¶

Collaborating Agent	Shared Flow
`API Designer Agent`	Provides OpenAPI and transport specs
`Frontend Developer Agent`	Uses API Libraries to bind data layers
`Mobile Developer Agent`	Integrates client SDK into mobile service classes
`Tech Lead Agent`	Approves API exposure scopes and gateway routes
`Security Architect Agent`	Validates token strategy, scopes, and credential management
`Observability Agent`	Adds tracing and logging middleware during gateway generation
`DevOps Architect Agent`	Packages and publishes libraries to internal registry (e.g., Azure Artifacts, GitHub Packages)

🧬 Policy Compliance¶

All generator outputs adhere to:

ConnectSoft naming standards (Company.Feature.Type)
Semantic versioning with changelogs
Auth policy hooks ([Authorize("scope")], token decorators)
Resilience decorators (Polly, RetryPolicy, CircuitBreaker)
Span injection via OTEL libraries

🧪 Test-Ready Outputs¶

Libraries include test harnesses and sample usage stubs
SDKs are auto-tested using schema validation + mock transport
Gateway endpoints include Swagger with automated contract tests (if test agent is enabled)

📊 Generator Metrics¶

Metric	Description
`generated_libraries_total`	Count of `.nupkg` or `.csproj` emitted
`gateway_routes_created`	Total REST/gRPC endpoints exposed
`api_clients_generated`	Number of typed SDKs per OpenAPI/proto
`versioned_libraries_published`	Successful semantic releases
`consuming_agents_linked`	Downstream usage registered (frontend, mobile, test)

📌 Summary¶

These three Generator Agents play a modularization and enablement role in the Software Engineering cluster. They allow:

Code reuse across dozens of services
Faster integration of UI and test systems
Scalable API governance and rollout via gateway control

Without them, the ConnectSoft platform would face fragmentation, redundancy, and slower cross-team velocity.

✅ Cycle 15 Completed

Shall we continue to Cycle 16 — Outputs, Artifacts, and CI/CD Handoffs?

🧩 Software Engineering Agents Overview¶

Cycle 16 — Outputs, Artifacts, and CI/CD Handoffs
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section provides a unified view of all code-level outputs, configuration artifacts, and delivery handoffs generated by Software Engineering Agents, and how these are passed into the DevOps and CI/CD layers of the ConnectSoft AI Software Factory.

It ensures that every code artifact has:

A deterministic structure
A traceable commit path
A known lifecycle stage
A clear consumer in downstream pipelines

📦 Output Artifact Categories¶

Artifact Type	Example Files	Producer Agent(s)
Source Code	`RegisterUserHandler.cs`, `UserService.sln`	Backend Developer, Microservice Generator
Adapters	`UserRepository.cs`, `EmailSenderClient.cs`	Adapter Generator Agent
DTOs / Ports	`RegisterUserDto.cs`, `IUserRepository.cs`	Microservice Generator, Backend Developer
Validators	`RegisterUserValidator.cs`	Backend Developer Agent
Frontend/Mobile UI	`RegisterPage.razor`, `RegisterScreen.tsx`	Frontend, Mobile Developer Agents
API Clients	`UsersApiClient.cs`, `InvoicesGrpcClient.ts`	API Library Generator Agent
Libraries	`ConnectSoft.Validation.csproj`, `.nupkg`	Library Generator Agent
Infrastructure Manifests	`deployment.yaml`, `helm/`, `config.bicep`	Infrastructure Engineer Agent
Gateway Configs	`gateway.yaml`, `routes.json`	API Gateway Generator Agent
Tests	`RegisterUserTests.cs`, `UsersControllerTests.cs`	Backend Developer, Test Generator Agent
Telemetry Configs	`otel-agent-config.yaml`	Observability Engineer, Infra Engineer
Git Commits & PRs	`feat(users): add registration use case`	Code Committer, Pull Request Creator
Metadata	`adapters-manifest.json`, `services-manifest.yaml`, `trace-metadata.json`	All agents collectively

📁 Repository Layout and File Organization¶

All outputs conform to the ConnectSoft repository structure:

/src
  /Application
  /Domain
  /Infrastructure
  /Api
/tests
  /Application.Tests
  /Infrastructure.Tests
/deploy
  /k8s
  /helm
  /iac
/libraries
  /Shared.Logging
  /Shared.Validation

Each file includes traceable headers such as:

// generated_by: BackendDeveloperAgent
// trace_id: register-user-3182
// generated_at: 2025-05-02T14:12:00Z

🧬 CI/CD Handoff Points¶

Software Engineering Agents prepare all necessary artifacts for pipeline execution:

Handoff Stage	Consuming Layer
✅ Git Commit	DevOps Agent (CI trigger via Git webhook)
✅ Pull Request	CI Validator Agent + Tech Lead Agent
✅ Test Files	QA Automation Agent
✅ Helm/K8s Configs	Deployment Orchestrator Agent
✅ Manifests (`services-manifest.yaml`)	Runtime Inventory & Cost Optimization Agents
✅ Library Package	Internal NuGet/npm registry (via Release Manager Agent)
✅ OTEL Config	Observability Collector Agent

📋 Trace ID Lifecycle¶

All artifacts generated include a trace_id, which enables:

PR → Feature → Epic linkage
CI → Commit mapping
Runtime span → Source file origin
Cost and telemetry attribution
QA validation to assert coverage

The trace_id follows a request/response-style flow between agents, scoped to the lifecycle of a feature, port, or microservice.

📊 Artifact Verification Metrics¶

Metric	Description
`artifacts_per_service`	Total generated code/config items per service
`missing_trace_ratio`	% of files without trace_id annotation
`unlinked_test_files`	Tests not attached to a CI-validated feature
`orphaned_gateway_routes`	Gateway configs not attached to backend handlers
`commit_to_ci_latency`	Time between engineering PR and pipeline execution

📌 Summary¶

The ConnectSoft Software Engineering Agents produce not just source code, but deployment-ready, fully traceable artifacts that plug into the wider autonomous delivery lifecycle.

Their structured output enables:

Seamless DevOps automation
Accurate cost attribution
Clean observability
Reliable testing and QA verification
Continuous improvement of traceability, security, and release performance

✅ Cycle 16 Completed

Would you like to continue to Cycle 17 — Agent Collaboration Patterns?

🧩 Software Engineering Agents Overview¶

Cycle 17 — Agent Collaboration Patterns
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines the collaboration patterns used by Software Engineering Agents in the ConnectSoft AI Software Factory. These patterns describe how engineering personas interact, hand off responsibilities, and synchronize execution across microservice development, infrastructure provisioning, UI assembly, and delivery workflows.

🔄 Collaboration in Agentic Engineering¶

Unlike traditional linear dev pipelines, Software Engineering Agents operate using:

Event-driven handoffs
Traceable output passing
Template-based coordination
Contract-to-code generation chains
Pull-based triggers (based on artifact availability)

This ensures scalable, distributed, autonomous workflows with minimal contention or human gating.

🤝 Core Collaboration Models¶

1️⃣ Sequential Composition (Port → Handler → Adapter)¶

sequenceDiagram
    participant Arch as Application Architect Agent
    participant MicroGen as Microservice Generator Agent
    participant Backend as Backend Developer Agent
    participant Adapter as Adapter Generator Agent
    participant Commit as Code Committer Agent

    Arch->>MicroGen: Generate input/output ports
    MicroGen->>Backend: Provide use case scaffolds
    Backend->>Adapter: Request repo/client for port
    Adapter->>Backend: Returns infrastructure adapter
    Backend->>Commit: Finalize implementation

Hold "Alt" / "Option" to enable pan & zoom

This pattern is typical for use case-driven services: a command or query port is defined, implemented, and backed with infrastructure logic.

2️⃣ Parallel Execution (Frontend, Backend, API Library)¶

flowchart LR
    Backend -->|defines| OpenAPI
    Backend -->|generates| DTOs
    Backend -->|tags| trace_id

    APIClient[API Library Generator] -->|consumes| OpenAPI
    Frontend -->|binds| APIClient
    Mobile -->|binds| APIClient

Hold "Alt" / "Option" to enable pan & zoom

This model allows UI and service developers to proceed in parallel, using auto-generated API clients and DTOs to ensure contract alignment.

3️⃣ Generator-Initiated Flows (Service Bootstrapping)¶

flowchart TD
    MicroGen --> Backend
    MicroGen --> InfraEng
    MicroGen --> AdapterGen
    MicroGen --> TestGen

Hold "Alt" / "Option" to enable pan & zoom

A single Microservice Generator Agent initiates a coordinated effort across multiple agents, each contributing one layer or capability (e.g., logic, infra, tests, telemetry).

4️⃣ Feedback Loop (QA / Bug Resolver / Committer)¶

sequenceDiagram
    participant QA as QA Agent
    participant BugFixer as Bug Resolver Agent
    participant Commit as Code Committer
    participant PR as PR Creator

    QA->>BugFixer: Emit failed test report
    BugFixer->>Commit: Patch + test case
    Commit->>PR: Pushes updated PR
    QA->>PR: Revalidates outcome

Hold "Alt" / "Option" to enable pan & zoom

This ensures that bug resolution is automated, test-validated, and trace-linked back into the SDLC.

🔧 Coordination via Metadata and Events¶

Mechanism	Description
`trace_id`	Shared across all artifacts, commits, and PRs in a feature
`lifecycle_event`	Triggers action (e.g., `AdapterReady`, `HandlerMissing`, `TestPassed`)
`manifest.yaml`	Describes what each agent produced, consumed, or validated
`agent_version`	Indicates which AI persona performed each contribution
`code_tag`	Files are tagged (`layer: application`, `scope: security`) to guide PR review and CI behavior

🧠 Common Event-Driven Patterns¶

Event	Responding Agent
`HandlerScaffoldReady`	Backend Developer Agent
`MissingAdapterDetected`	Adapter Generator Agent
`TestStubMissing`	Test Generator Agent
`SecurityPolicyRequired`	Security Architect Agent
`PullRequestCreated`	Tech Lead Agent (for review)
`CIBuildFailed`	Bug Resolver Agent
`ServiceReadyToDeploy`	DevOps Engineer Agent

These patterns allow for decentralized orchestration, where agents can self-activate in response to context-specific signals.

📌 Summary¶

Software Engineering Agents in ConnectSoft don’t act in isolation — they:

Collaborate through events and contracts
Respect traceable handoffs
Build on common inputs like ports, domain models, and transport configs
Form repeatable workflows that scale to 100s of services and personas

This creates an agentic mesh of cooperation, enabling fully autonomous, continuously evolving software delivery.

✅ Cycle 17 Completed

Shall we proceed to Cycle 18 — Metrics and Observability?

🧩 Software Engineering Agents Overview¶

Cycle 18 — Metrics and Observability
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section details how Software Engineering Agents in the ConnectSoft AI Software Factory are made observable, measurable, and improvable through structured metrics, trace IDs, and OpenTelemetry-based instrumentation.

Observability ensures every action — from code generation to pull request creation — is monitored, auditable, and feedback-driven.

📡 Observability Principles in Engineering Agents¶

All engineering agents must:

Emit structured OpenTelemetry spans for their actions
Inject trace_id, agent_id, component, and operation metadata
Log key decision points (e.g., template applied, port mapped, commit created)
Register metrics that can be consumed by dashboards, alerts, and KPIs
Link test execution, commit, and runtime telemetry to their originating service or agent

📈 Core Metrics Collected¶

🔨 Code Generation & Output¶

Metric	Description
`handlers_generated_total`	Total use case handlers created per agent
`adapters_created_total`	Number of output/infrastructure adapters generated
`dto_transformations_applied`	Count of mapping layers inserted
`openapi_clients_emitted`	API SDKs created by the API Library Generator

🧪 Testability and Quality¶

Metric	Description
`test_coverage_ratio`	% of use cases with test scaffolds or coverage
`testless_commit_ratio`	Commits made without associated test updates
`regression_tests_added`	Test cases added after bug resolution
`test_stub_autogen_success`	Success rate of Test Generator Agent triggers

💾 Git and Delivery¶

Metric	Description
`commits_per_trace`	Number of commits per feature implementation (`trace_id`)
`prs_per_week_per_agent`	PR throughput by engineering persona
`unmerged_pr_age_seconds`	Aging PRs needing review or fixes
`code_review_cycle_time`	Time between PR creation and merge

🧠 Runtime Instrumentation¶

Each agent's contribution includes:

ActivitySource.StartActivity(...) span around key methods
ILogger<T> logs with trace context
ExecutionContext propagation across files, services, and commits

Span tags include:

Tag	Value
`agent.name`	`BackendDeveloperAgent`
`component.type`	`handler` / `adapter` / `test`
`layer`	`application`, `infrastructure`, `ui`, etc.
`trace_id`	UUID linked to feature/epic
`template_version`	Template used to generate output
`build_id`	CI build that consumed the output

🖥️ Dashboard Integration¶

Metrics and spans are sent to:

Grafana + Prometheus dashboards
Azure Application Insights / Log Analytics
OpenTelemetry collectors (OTLP)
DevOps Boards (via work item or PR annotation)

Sample views:

⚠️ PR latency heatmaps
📦 Services missing observability hooks
🔁 Bug resolution flow cycle times
🔍 Adapter output span coverage per service

📊 Cross-Cycle Traceability¶

Each engineering activity links to:

✅ Upstream source: Port, domain model, API definition
✅ Internal artifact: Handler, adapter, client, test, commit
✅ Downstream signal: PR, CI result, deployment status
✅ Runtime footprint: Spans, logs, failures

This supports retrospective analysis, compliance reporting, and continuous engineering quality improvement.

📌 Summary¶

In ConnectSoft, Software Engineering Agents are observable by design. They don’t just generate code — they emit a measurable trace of their behavior, contributing to a larger system of trust, automation, and platform resilience.

This turns software engineering into a transparent, adaptive, and continuously optimizable function of the autonomous software factory.

✅ Cycle 18 Completed

Would you like to proceed with the final cycle — Cycle 19: Security and Compliance in Dev Agents?

🧩 Software Engineering Agents Overview¶

Cycle 19 — Security and Compliance in Dev Agents
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Cycle¶

This section defines how Software Engineering Agents in ConnectSoft enforce security and compliance throughout the development lifecycle — not as an afterthought, but as a first-class concern embedded in code, configuration, infrastructure, and observability.

Security responsibilities are distributed across all dev personas, guided by inputs from the Security Architect Agent, and verified through continuous policy enforcement.

🧱 Key Security Responsibilities by Agent¶

Agent	Security Enforcement Scope
`Backend Developer Agent`	Ensures use cases enforce scope-based authorization and avoid sensitive data leaks
`Frontend / Mobile Developer Agents`	Manage OAuth2 flows, refresh tokens, role-based UI rendering
`API Gateway Generator Agent`	Applies auth filters, CORS policies, and rate limiting
`Adapter Generator Agent`	Adds token decorators, scopes, and credentials to external clients
`Infrastructure Engineer Agent`	Injects TLS certs, encrypted secrets, and secure environment variables
`Library Generator Agent`	Generates standard `AuthorizationHandler`, RBAC helpers, token providers

🔐 Security Features Applied¶

Feature	Applied By
`[Authorize("scope")]` on controller or gRPC methods	Backend / Gateway Agents
OAuth2 Bearer token injection	Adapter Generator + API Client Libraries
Token refresh and session management	Frontend / Mobile Developer Agents
TLS and HTTPS enforcement	Infrastructure Engineer Agent
Secret rotation via Key Vault or ENV	Infrastructure + DevOps Agents
Request throttling and anti-abuse	API Gateway Generator Agent

📜 Compliance Alignment¶

ConnectSoft engineering artifacts are aligned with:

✅ SOC 2 Type II — traceability, access control, audit trails
✅ ISO 27001 — secure service design, secrets management, token handling
✅ GDPR — support for data minimization, pseudonymization, and trace-based deletion
✅ HIPAA (if enabled) — end-to-end encryption, session control, restricted access

These standards are enforced through:

Code templates with pre-applied auth wrappers
Secrets mounted from cloud vaults (not hardcoded)
Logging redaction logic in place (SensitiveFieldFilter)
Structured trace IDs and audit logs per user interaction
Role-based access enforcement in both backend and UI agents

🛡️ Secure Adapter Output¶

Every adapter that communicates with an external system:

Includes retry policies with backoff jitter
Applies token injection (IAuthenticatedHttpClient)
Respects scopes declared in adapter-policies.yaml
Logs outbound call context without leaking payloads
Is traced with span.kind=client and adapter.secured=true

🔍 Secure Frontend Behavior¶

Auth state managed via AuthenticationStateProvider or secure JWT session stores
UI elements are hidden or disabled based on user scope
API calls auto-include auth headers with auto-refresh fallback
Offline-first features enforce local encryption (if enabled)
Form fields with PII or sensitive input are tagged for redaction in logs

🧪 Test and Validation¶

Security-related coverage is enforced through:

SecurityPolicyTests.cs (for backend and gateway access rules)
AuthIntegrationTests.cs (for client-side session and API call flow)
Secret leakage scanners during PR review (secrets-scan.yaml)
AuthSpanValidation in observability pipelines (to ensure all spans carry user context)
Automatic rejection of PRs without [Authorize] where expected

📊 Metrics and Observability¶

Metric	Description
`unauthenticated_call_ratio`	% of exposed endpoints without access control
`secret_exposure_violations`	Commits or PRs flagged by secrets scanner
`authorization_span_coverage`	% of entrypoints emitting user/session trace context
`token_refresh_failures`	Frequency of client-side session/auth errors
`scope_mismatch_incidents`	Attempts to access features without sufficient privileges

📌 Summary¶

Software Engineering Agents in ConnectSoft are not only producers of logic — they are guardians of security and compliance by design.

Security is:

Baked into templates
Reinforced by generated patterns
Validated by agents before merge
Observed in runtime traces
Reviewed by Tech Lead and Security Architect personas

This ensures every service, client, and interaction is secure, accountable, and production-grade.

✅ Cycle 19 Completed

Would you like to continue with the final Cycle 20 — Conclusion + Future Roadmap?

🧩 Software Engineering Agents Overview¶

Cycle 20 — Conclusion + Future Roadmap
(ConnectSoft Agent Framework – Software Engineering Execution Layer)

🎯 Purpose of This Final Cycle¶

This concluding section synthesizes the purpose, impact, and operational strategy behind the Software Engineering Agent Cluster in the ConnectSoft AI Software Factory, and defines its future evolution and integration into other clusters (QA, DevOps, Observability, Runtime).

🧠 Recap: Why Engineering Agents Matter¶

Software Engineering Agents are the core execution layer that transforms structured architecture and product vision into:

💡 Working code
📦 Deployable artifacts
🔄 Integrated systems
🔍 Observable logic
✅ Testable, compliant modules

Without them, ConnectSoft’s software factory would remain theoretical. With them, it becomes self-building, traceable, and continuously improving.

🧱 Functional Pillars Revisited¶

Pillar	Enforced By
🧩 Clean Architecture	Backend Developer Agent, Microservice Generator Agent
🔐 Security	Adapter Generator Agent, Gateway Generator Agent, Infra Engineer
⚙️ Infrastructure	Infrastructure Engineer Agent, Code Committer Agent
🧪 Testability	Developer Agents + Test Generator Agents (collaboration)
🛠️ Observability	OTEL spans from all generated logic
🔁 Workflow Automation	Code Committer + Pull Request Creator Agents
🧠 Traceability	Every artifact includes `trace_id`, `agent_id`, `layer`, `timestamp`

🧩 Strategic Integration Points¶

Software Engineering Agents work in tight coordination with:

Architecture Agents
→ Consume models, contracts, transport/topology specs
DevOps Agents
→ Push code into build pipelines, deployments, cost tracking
QA & Test Agents
→ Validate correctness, coverage, and runtime behavior
Observability Agents
→ Consume spans, traces, logs for code coverage and issue detection
Security & Compliance Agents
→ Review scopes, secrets, auth logic, RBAC usage
Documentation Agents
→ Consume service manifests and OpenAPI outputs for API portals and internal docs

🧭 Future Roadmap for Engineering Agent Cluster¶

Roadmap Theme	Future Enhancements
🧠 Autonomous Refactoring Agents	Recommend cleanups and architectural drift correction
⚙️ Low-Code Extension Layer	Allow business users to extend services safely using code transformers
📦 Service Composition Agents	Automatically bind multiple microservices together into a workflow
🔄 Agent Retraining via Feedback	Use runtime errors and production spans to improve future generation
📜 PR Reviewer Agents	Perform static analysis, linting, architecture verification before merge
🧱 Service Registry Builders	Automatically link microservices, versions, and APIs into service discovery mesh
🧪 Advanced Test Scaffolding	Generate contract tests, integration tests, synthetic test traffic setups

🧩 Agent Maturity Model (Current State)¶

Capability	Status
Code generation and layering	✅ Mature
Trace and telemetry support	✅ Integrated
Security enforcement	✅ Embedded
Pull request automation	✅ Active
CI/CD readiness	✅ Observable
Test strategy alignment	✅ Partial (collaboration with QA cluster)
Runtime cost optimization hooks	🔄 Emerging
Cross-agent error correction	🔜 In development

🧬 Vision Summary¶

ConnectSoft’s Software Engineering Agent cluster is:

Modular
Scalable
Autonomous
Observability-first
Policy-enforced
Human-compatible

It forms the automation backbone that lets hundreds of services be built, tested, committed, reviewed, and deployed without manual bottlenecks.

It is the execution reality of the platform’s promise:

Software that builds software — safely, cleanly, and autonomously.

✅ Cycle 20 Completed – Engineering Agents Overview is now complete

Would you like to consolidate this into a single software-engineering-agents-overview.md document next?