🧠 Agentic System Design¶

🎯 Purpose & Scope¶

Agentic System Design refers to the architecture, orchestration, and operational behavior of autonomous software agents that collaboratively design, generate, test, and deploy complete SaaS solutions.

In the ConnectSoft AI Software Factory, these agents are:

Modular
Intelligent
Event-driven
Prompt-aware
DSL-integrated
Observability-first

Agentic systems transform static templates into living, self-executing workflows.

🛠️ Why Is It Needed?¶

Modern software development at scale requires:

Generating thousands of microservices across industries
Supporting multi-tenant SaaS platforms with different editions and domains
Enabling modular, reusable automation
Scaling horizontally with clustered execution flows

An agentic system solves these challenges by enabling distributed decision-making, autonomous task execution, and event-based coordination.

🔍 Scope of This Document¶

This document captures the full design of the ConnectSoft Agentic System, including:

Area	Included?	Description
Agent Types and Roles	✅	Product, Architecture, Engineering, QA, Testing, Orchestration, DevOps, etc.
Execution Lifecycle	✅	Initialization → Coordination → Generation → Delivery
Agent Communication Patterns	✅	Event-driven, file-based, memory-based, DSL-based
Orchestrator & Planner Roles	✅	How central coordinators manage agent clusters
DSL & Memory Integration	✅	How agents use DSLs and semantic memory to reason
Traceability & Observability	✅	End-to-end audit trail for each task and output
Extension & Composition	✅	How to build new agents, extend flows, or evolve safely
Security & Isolation	✅	Agent boundaries, tenancy, validation, and escalation
Human-Agent Collaboration	✅	Review checkpoints, escalation, decision loops
Real-World Examples	✅	Agent mesh walkthroughs per execution cluster

🏗️ System-Level Goals¶

The Agentic System aims to be:

Goal	Description
Autonomous	No human interaction required for most flows
Composable	Modular agents can be reused in different orchestration graphs
Traceable	All actions and artifacts linked to `trace_id`, `dslRef`, and `agentId`
Extensible	Easy to add new agents, clusters, and DSL types
Safe & Observable	Fault-tolerant with retries, validation, and telemetry
Aligned to Clean Architecture	Agents produce layered, decoupled artifacts

📘 Key Principles Reflected¶

Principle	Manifestation in Agentic Design
Clean Architecture	Agents emit use-case code, not framework glue
Domain-Driven Design	Agents operate within bounded contexts
Event-Driven Architecture	All agent coordination happens via events
Cloud-Native	Agents scale horizontally, containerized, stateless or stateful as needed
Security-First	Scoped by tenant, context, permission level
Modularity & Extensibility	Agent mesh evolves without platform rework

🧠 Agentic System Is Not...¶

Not a...	Why Not?
Monolithic AI Bot	Every agent is modular, focused, and disposable
Workflow Script	Plans are declarative, event-driven, and autonomous
Static Template Engine	DSLs + semantic memory + traceable execution = living system
Chatbot API	Agents have memory, planning, orchestration, not just dialogue

✅ Summary¶

The Agentic System Design is the core operating model of ConnectSoft — defining how intelligent, modular agents collaborate to autonomously build, evolve, and deploy full-scale, production-grade SaaS ecosystems.

It blends:

Multi-agent planning
Clean, domain-driven engineering
Full-stack DSL-driven generation
AI-first memory and prompting
Event-driven automation

… into a secure, scalable, and extensible autonomous platform.

🧩 Agent Types & Roles in ConnectSoft¶

In the ConnectSoft AI Software Factory, agents are organized into functional categories that reflect real-world software engineering roles — but enhanced with AI-native capabilities, DSL processing, and orchestration awareness.

Each agent has a specialized role, a set of input artifacts or events, and clearly defined outputs.

“Agents are intelligent team members. Each knows its mission, listens for the right signal, and executes autonomously.”

🏛️ Major Agent Categories¶

Category	Description
Vision & Planning Agents	Turn ideas into structured visions, BRDs, product plans
Architecture Agents	Decompose product plans into bounded contexts, services, and blueprints
Engineering Agents	Generate code, tests, adapters, pipelines
Testing & QA Agents	Ensure quality, validate behavior, run test suites
Coordination & Orchestration Agents	Govern execution flow, agent collaboration, task resolution
DevOps & Release Agents	Generate deployment infrastructure, monitor rollout, version releases
Knowledge & Memory Agents	Enrich memory, fetch related knowledge, index artifacts
UX/UI Agents	Generate wireframes, UI components, and design specifications
Security & Governance Agents	Enforce access rules, permissions, and compliance validation

🔍 Detailed Examples of Key Agent Types¶

🔹 Vision Architect Agent¶

Converts human prompts into structured vision.md + metadata
Triggers: VisionRequestReceived
Outputs: VisionDocumentCreated, personas, initial feature maps

🔹 Product Manager Agent¶

Converts vision + BRD into product plans, MVPs, edition maps, and roadmaps
Outputs: ProductPlanCreated, MVPDefined, EditionsDefined, FeatureBacklogReady
Consumed by: Product Owner Agent, Enterprise Architect Agent, UX Designer Agent

🔹 Enterprise Architect Agent¶

Decomposes product scope into service boundaries, blueprints, and modular architecture
Outputs: SystemBlueprint.yaml, MicroserviceCatalog, bounded contexts

🔹 Domain Modeler Agent¶

Generates ddd.yaml DSLs based on vision or feature scope
Outputs: Aggregates, Commands, Events (structure only)

🔹 Backend Developer Agent¶

Generates code for commands, event handlers, services
Inputs: ddd.yaml, event.yaml
Outputs: .cs, .json, pr-metadata.json

🔹 Test Generator Agent¶

Produces .feature or .spec tests from ddd.yaml, test.yaml, or code metadata
Emits: TestCaseGenerated, SpecFlowReady, TestCoverageReport

🔹 Orchestrator Agent¶

Listens for events and triggers correct agents based on current traceId, dslType, or blueprint phase
Executes finite state machine (FSM) per project lifecycle
Emits: AgentTaskStarted, AgentTaskCompleted, ExecutionBlocked

🤝 Agent Clusters (Execution Groups)¶

Cluster Name	Agents Included
Vision Cluster	Vision Architect, Business Analyst, Product Manager
Planning Cluster	Product Manager, Product Owner, Edition Strategist
Architecture Cluster	Enterprise Architect, Domain Modeler, API Designer
Engineering Cluster	Backend Developer, Adapter Generator, Frontend Developer
Quality Cluster	Test Generator, QA Engineer, Code Reviewer
Ops Cluster	DevOps Agent, Deployment Coordinator, Security Agent

✅ Each cluster can run in parallel, sequentially, or via conditional handoff depending on the execution plan.

🧠 Role vs Skill¶

Concept	Description
Agent Role	A persona or system component with a defined scope of responsibility
Skill	A named, reusable capability an agent can perform (e.g., `GenerateHandler`, `EmitOpenApi`)

An agent may own dozens of skills — but is always focused on its role-specific goals.

📦 Sample Agent Output Matrix¶

Agent	Input	Output
Product Manager Agent	`vision.md`, `brd.md`	`product-plan.yaml`, `mvp-list.json`
Backend Developer Agent	`ddd.yaml`, `event.yaml`	`BookAppointmentHandler.cs`, `Appointment.cs`
Test Generator Agent	`feature.yaml`, DSL	`.feature`, `.test.cs`
DevOps Agent	`pipeline.yaml`	Azure DevOps YAML pipelines, deployment manifests

✅ Summary¶

Agents in ConnectSoft are modular, role-based entities with AI-augmented capabilities.
Their responsibilities mirror human engineering workflows — but with automation-first execution.
Agent types are grouped into collaboration clusters, orchestrated via events and FSMs.
Each agent is observable, traceable, and drives the Software Factory forward autonomously.

🔄 Agent Lifecycle Phases in ConnectSoft¶

Each agent in the ConnectSoft AI Software Factory follows a well-defined lifecycle to ensure:

Predictable orchestration
Reliable execution
Recoverability on failure
Clear handoff to downstream agents
Observability at every step

“Agents in ConnectSoft behave like autonomous services — but with purpose-driven lifecycle control.”

🧬 Lifecycle Phases Overview¶

flowchart TD
    Created --> Assigned
    Assigned --> Initialized
    Initialized --> Activated
    Activated --> Executing
    Executing --> Reporting
    Reporting --> HandedOff
    Reporting --> Completed
    Completed --> Archived

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

🧩 Phases Defined¶

Phase	Description
Created	Agent instance is instantiated with metadata (`traceId`, `dslRef`, etc.) but not yet assigned
Assigned	Orchestrator assigns the agent a specific responsibility or task (e.g., generate handler)
Initialized	Agent loads memory, retrieves DSLs, prompts, or blueprint context
Activated	Agent’s main skill is triggered — entering processing mode
Executing	Agent performs work (e.g., code generation, artifact validation)
Reporting	Agent emits success/failure events, links outputs, telemetry, or errors
HandedOff	Agent dispatches structured outputs or events to trigger next agent(s)
Completed	Agent finalizes its lifecycle for the given task/traceId
Archived	Results, logs, and state are stored for traceability, audit, or future replay

🔁 Trigger Points for Lifecycle Transitions¶

From → To	Trigger
`Created → Assigned`	Orchestrator emits `AssignAgentTask`
`Assigned → Initialized`	Agent receives DSL references, memory context
`Initialized → Activated`	Agent confirms inputs and dependencies are ready
`Activated → Executing`	Skill is invoked
`Executing → Reporting`	Work is done, outputs generated
`Reporting → HandedOff`	Events emitted to event bus or DSL updated
`Reporting → Completed`	If terminal output (e.g., code committed)
`Completed → Archived`	Orchestrator ends session, persists state/logs

🔍 Observability Hooks Per Phase¶

Phase	Metrics/Logs/Events
`Initialized`	`agent_initialized_total`, `dsl_fetched_total`
`Executing`	`skill_execution_latency_seconds`, logs by `skillId`
`Reporting`	`ArtifactGenerated`, `DslSegmentConsumed`, `ErrorReported`
`HandedOff`	`AgentHandoffEvent`, `TriggerNextAgentRequest`
`Archived`	`TraceFinalized`, `AgentSessionArchived`

🔐 Isolation and Safety¶

Each phase is stateless unless explicitly scoped to context
Retry rules apply to Executing phase only (idempotent by traceId)
Memory writes only allowed during Executing and Reporting phases
Agent FSMs are observable and replayable

🧠 Example: Backend Developer Agent Lifecycle¶

Created by orchestrator after DslValidated (type: DDD)
Assigned to generate handler for BookAppointment
Initialized by downloading ddd.yaml and resolving commands[]
Activated → GenerateHandler skill invoked
Executing → generates .cs files + logs
Reporting → emits CodeGenerated + file URIs
HandedOff → emits PullRequestReady for CommitAgent
Completed → stores metadata snapshot
Archived → trace recorded in Studio

✅ Summary¶

Agents follow a predictable, observable lifecycle from creation to completion
Every phase is traceable via events and logs
The lifecycle ensures agents are modular, safe, retryable, and audit-ready
FSM models govern transitions and support multi-agent coordination at scale

🧠 Orchestration Layer Overview¶

The Orchestration Layer in the ConnectSoft AI Software Factory is responsible for controlling, sequencing, and coordinating agent execution using:

Event-driven triggers
Finite state machines (FSMs)
DSL metadata
Traceability contracts

It serves as the execution brain behind the agentic mesh — managing not just “what happens next,” but also “why,” “when,” and “in what context.”

“The Orchestrator is the project manager, dispatcher, and guardrail system for autonomous agents.”

🧬 Core Responsibilities¶

Area	Description
Agent Assignment	Dispatches agents to process specific DSLs, artifacts, or events
Trace Tracking	Maintains `traceId`, `projectId`, and `dslRef` lineage
FSM Execution	Evaluates rules and transitions to decide which agent/skill to trigger next
Dependency Management	Ensures prerequisite DSLs, memory, or agent outputs are available before continuing
Error Recovery & Retry	Retries failed steps deterministically or reroutes to fallback agents
Handoff Events	Emits downstream events for chaining agent activations
Completion Detection	Marks a trace/workflow as done when all required outputs exist and pass validation

🧱 Orchestration Architecture Layers¶

flowchart TD
    EventBus --> TriggerProcessor
    TriggerProcessor --> FSMEngine
    FSMEngine --> AgentInvoker
    FSMEngine --> MemoryStore
    AgentInvoker --> AgentSkill
    AgentSkill --> EventBus
    AgentSkill --> DSLRegistry

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

⚙️ Orchestration FSM Example¶

state: "ProductPlanning"
on:
  - event: VisionDocumentCreated
    do:
      agent: product-manager
      skill: EmitProductPlan
  - event: ProductPlanCreated
    do:
      agent: enterprise-architect
      skill: DecomposeIntoServices

→ Stored as execution-plan.yaml per project or template

📡 Inputs to the Orchestrator¶

Input Type	Description
`Event`	Structured platform events (e.g., `DslValidated`, `TestFailed`)
`Execution Plan`	FSM config for specific workflows
`DSL Metadata`	Context: `dslType`, `context`, `dslRef`, `version`
`Memory Snapshots`	Recall past agent output, trace artifacts
`Blueprints`	Extract paths and dependencies from project blueprints

📤 Outputs from the Orchestrator¶

Output	Description
`AgentInvocation`	Signal to specific agent with DSL + context
`AgentExecutionMetadata`	Recorded invocation, outputs, skills, trace
`NextEventTrigger`	Event emitted to activate the next agent(s)
`ExecutionTimeouts`	Fail-safe for agents that stall or go silent
`Audit Logs`	For all transitions and actions taken

🧩 Orchestration & DSLs¶

DSLs are central to orchestration:

FSMs dispatch agents based on dslType, context, and dslRef
Orchestrator uses schema validation to block invalid DSLs
Orchestrator routes partial DSL segments (e.g., just one command) to specialized agents
DSL generation triggers re-evaluation of the FSM

🔍 Observability Features¶

Signal	Example
`fsm_transition_total`	FSM moved from `Planning` → `Architecture`
`agent_invoked_total`	`backend-developer` called via FSM
`fsm_blocked_traces_total`	Waiting for missing DSL or failed dependency
`execution_latency_seconds`	Total time from first event to last success

Studio dashboards track execution graphs, timelines, and stuck states.

🧠 Coordinators vs Orchestrators¶

Term	Role
Orchestrator	Central flow manager (event-based FSM, shared per environment)
Coordinator Agent	A specialized agent that orchestrates a subcluster (e.g., `TestCoordinator`, `EditionCoordinator`) using internal delegation

✅ Coordinators live inside the mesh. The orchestrator lives outside the mesh and governs all agents globally.

✅ Summary¶

The orchestration layer is a declarative, event-driven flow engine for managing agent execution
It uses FSMs and DSL metadata to sequence and validate work
All transitions, outputs, and triggers are traceable via traceId
It ensures safe, observable, and recoverable execution at scale

🔁 Agentic Execution Flow in ConnectSoft¶

The execution flow defines how ConnectSoft agents collaborate to fulfill a complete software delivery lifecycle — from idea to deployed, tested, and validated system.

This flow is event-driven, traceable, and modular, with each agent operating on:

DSLs
Structured events
Contextual memory
Planner-driven task graphs
Observable trace identifiers

“Execution flow = blueprint for intelligent action — choreographed by orchestrators, performed by agents.”

🧬 High-Level Execution Flow Phases¶

flowchart TD
    Vision --> ProductPlanning
    ProductPlanning --> ArchitectureDesign
    ArchitectureDesign --> ServiceScaffolding
    ServiceScaffolding --> Engineering
    Engineering --> QA
    QA --> DevOps
    DevOps --> Release

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

🔹 1. Vision Ingestion¶

Trigger	Output
`VisionPromptSubmitted`	`vision.md`
Agent: Vision Architect	Emits `VisionDocumentCreated`

🔹 2. Product Planning¶

Agent	Output
Product Manager	`product-plan.yaml`, `feature-catalog.json`
Events: `ProductPlanCreated`, `MVPDefined`, `FeatureBacklogReady`

🔹 3. Architecture Decomposition¶

Agent	Output
Enterprise Architect	`system-blueprint.yaml`, `service-map.yaml`
Domain Modeler	`ddd.yaml`, `event.yaml`
Event Architect	`event-contracts.json`

🔹 4. Microservice Scaffolding¶

Agent	Output
Microservice Generator Agent	`Service.csproj`, `.sln`, infra bootstrap
Adapter Generator Agent	`adapter.yaml`, port bindings

🔹 5. Engineering Execution¶

Agent	Output
Backend Developer Agent	`.cs`, `.json`, `metadata.json`
Frontend Developer Agent	`.razor`, `.ts`, `.css`
API Designer Agent	`openapi.yaml`, `swagger.json`

🔹 6. QA and Testing¶

Agent	Output
Test Generator Agent	`.feature`, `.test.cs`
QA Engineer Agent	`test-matrix.yaml`, `coverage-report.json`
Bug Investigator Agent	`diagnostics.yaml`, logs, diffs

🔹 7. DevOps & Release¶

Agent	Output
DevOps Agent	`pipeline.yaml`, `deploy.bicep`, `monitor.yaml`
Deployment Coordinator	`release.json`, `rollout-plan.yaml`

📦 Flow Driver: `traceId` + `fsmState`¶

Each execution is scoped by a:

traceId: unique ID per product/service flow
fsmState: current logical phase in orchestrator FSM
dslRef: active DSL driving generation
agentId and skillId: contributors per artifact

✅ This ensures deterministic state transitions and full auditability.

🧠 Execution Patterns Supported¶

Pattern	Description
Linear Flow	Agent-to-agent straight sequence (e.g., Vision → Plan → Arch → Dev)
Parallelized Execution	Multiple agents generate components at once (e.g., handlers + tests + docs)
Conditional Branches	Optional flows (e.g., Skip UI if `softwareType: API`)
Feedback Loops	Re-execute or correct upstream DSLs if downstream validation fails

📊 Studio Trace View Example¶

traceId: trace-2025-05-17-001
fsmState: QA
agentChain:
  - vision-architect
  - product-manager
  - enterprise-architect
  - backend-developer
  - test-generator
outputs:
  - product-plan.yaml
  - ddd.yaml
  - BookAppointmentHandler.cs
  - Appointment.feature
  - pipeline.yaml

✅ Summary¶

The execution flow defines how agents are sequenced to deliver a working product
Driven by events, FSM, and metadata (traceId, dslRef)
Supports parallelism, feedback, and retries
Fully visible in Studio, traceable via logs, and auditable by design

🤝 Agent Collaboration Models¶

In the ConnectSoft AI Software Factory, agents don’t operate in silos — they collaborate in well-defined patterns to fulfill complex software tasks across the lifecycle.

These patterns are flexible, modular, and event-driven, enabling:

Parallel agent execution
Conditional handoffs
Feedback loops
Retryable validation chains
Emergent behavior through planner orchestration

“Collaboration patterns are the protocols that turn individual agents into autonomous teams.”

🔁 Core Collaboration Models¶

1. 🔗 Sequential Chain¶

One agent completes a task → emits event → triggers next
Example: Vision Architect → Product Manager → Enterprise Architect

sequenceDiagram
    participant A as Vision Agent
    participant B as Product Manager
    participant C as Enterprise Architect

    A->>B: VisionDocumentCreated
    B->>C: ProductPlanCreated

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

2. 🌿 Parallel Execution¶

Multiple agents are triggered simultaneously to work on independent tasks
Orchestrator waits for all to complete before continuing

graph TD
    A[ProductPlanReady] --> B[Backend Developer]
    A --> C[Test Generator]
    A --> D[API Designer]

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

✅ Improves throughput for large-scale codegen.

3. 🔄 Feedback Loop¶

Downstream agent detects invalid output
Reports issue and re-triggers upstream agent or planner fallback

sequenceDiagram
    participant QA as Test Agent
    participant DEV as Backend Agent
    participant COORD as Coordinator

    QA->>COORD: TestFailed
    COORD->>DEV: RetryHandlerGeneration

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

4. 🧠 Shared Context Cluster¶

Multiple agents work against shared DSLs, memory, and blueprint context
Example: Engineering Cluster working off ddd.yaml, event.yaml, and adapter.yaml

→ All agents read the same DSL snapshot and produce their respective artifacts.

5. 🪢 Cross-Cluster Handoff¶

Handoff between logically separate clusters
Example: From Architecture to Engineering to QA

→ Governed by the orchestrator’s FSM and project-level execution plan.

📁 Clustering Example¶

clusters/
  vision/
    vision-architect
    product-manager
  architecture/
    enterprise-architect
    domain-modeler
  engineering/
    backend-developer
    adapter-generator
  qa/
    test-generator
    qa-engineer

Each cluster may execute:

In parallel (within phase)
Sequentially (across phases)
Conditionally (based on trigger or DSL presence)

📦 Agent-to-Agent Communication Methods¶

Mode	Description
Events	`DslGenerated`, `TestFailed`, `ArtifactReady`
Files/Artifacts	`.cs`, `.feature`, `.yaml` stored in shared location
Memory Snapshots	Shared semantic memory context for prompts
Blueprint Links	Referenced DSLs, services, dependencies
Trace-Scoped Metadata	Common `traceId`, `dslRef`, `agentId` metadata embedded in all outputs

🤖 Role of the Planner¶

In advanced collaboration flows, the Planner Agent (optional) dynamically generates:

Agent execution chains
Skills to invoke per step
Context injection logic
Parallel branch construction

→ This allows for more flexible, AI-driven orchestration beyond FSM alone.

🔍 Collaboration in Studio¶

Feature	Description
Collaboration Timeline	Agent-by-agent execution trace
Agent Handoff Explorer	See who handed off to whom and why
Feedback Loop Viewer	Detect retries and reruns
Execution Graph Visualization	Node/edge graph of all agent dependencies

✅ Summary¶

Agents collaborate via sequential chains, parallel execution, feedback loops, and context clusters
Orchestration layer governs the execution logic, while agents remain independent and modular
Collaboration is powered by shared DSLs, memory, traceIds, and event streams
Studio provides full visibility into the agent mesh behavior

📡 Agent Protocols & Communication¶

In the ConnectSoft AI Software Factory, agents communicate using standardized, event-driven, and traceable protocols. These protocols ensure:

Seamless collaboration between agents
Deterministic triggering by orchestrators
Safe and observable artifact handoff
Multi-agent coordination across services, clusters, and lifecycles

“In ConnectSoft, agents don’t chat — they communicate through structured events, trace metadata, and shared artifacts.”

📦 Agent Communication Primitives¶

Mechanism	Description
Platform Events	JSON events on the internal message bus (`DslValidated`, `CodeGenerated`, etc.)
DSL Contracts	DSL files that encode the structural intent and input domain
Artifact References	Files written to shared storage with traceable metadata
Trace-Scoped Memory	Shared project context, DSL segments, blueprint references
Execution Metadata	JSON metadata attached to every agent step: `agentId`, `skillId`, `dslRef`, `outputChecksum`, etc.

🧬 Standard Event Contract¶

Event Envelope (Simplified)¶

{
  "event": "CodeGenerated",
  "traceId": "trace-2025-05-17-001",
  "dslRef": "dsl/Booking/ddd.yaml",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "artifact": "BookAppointmentHandler.cs",
  "status": "success",
  "timestamp": "2025-05-17T12:01:00Z"
}

✅ All messages are structured, typed, traceable, and logged in the event store.

🧠 Metadata Required for Every Message or File¶

Field	Purpose
`traceId`	Uniquely identifies the full execution flow
`dslRef`	Specifies which DSL this agent consumed or produced
`agentId`	ID of the agent emitting the event
`skillId`	Which skill was executed
`status`	Result of the operation (`success`, `error`, `partial`)
`outputs[]`	List of artifacts, files, or events produced
`timestamp`	UTC event time (for sequence ordering)

🔄 Communication Flow (Example)¶

sequenceDiagram
    participant ARCH as Enterprise Architect
    participant DEV as Backend Agent
    participant TEST as QA Agent

    ARCH->>DEV: DslValidated (ddd.yaml)
    DEV->>TEST: CodeGenerated (handler.cs)
    TEST->>DEV: TestFailed (BookAppointment.feature)

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

🧩 DSLs as Agent Interfaces¶

DSLs are shared declarative documents that:

Define agent inputs and expected outputs
Can be validated, diffed, versioned
Act as contracts across agent boundaries

Each DSL file includes:

$meta:
  dslType: DDD
  context: Booking
  agentId: backend-developer
  traceId: trace-xyz123

📚 Communication Pattern Types¶

Pattern Type	Description
Publish/Subscribe	Agents emit and listen to events via the internal bus
File-Based Handoff	Write/read DSLs and artifacts to/from versioned folders
Memory Injection	Semantic memory passed between agent prompts
Execution Contracts	Orchestrator defines expectations (agent + skill + output)

🔐 Security and Integrity¶

Events are validated against JSON schemas
DSLs must pass schema + semantic validation before being consumed
All output files are checksum-verified before handoff
Agents cannot directly trigger other agents — they emit events only

📊 Observability Features¶

Tool	Signal
Studio	Message logs per `traceId`, `agentId`, `dslType`
Metrics	`agent_communication_errors_total`, `event_dispatch_latency`
Tracing	Span correlation between emitter → consumer
Audit Log	Captures full end-to-end lifecycle with message headers

✅ Summary¶

Agents communicate using structured, event-based, and metadata-rich protocols
DSLs act as shared contracts and file-based APIs
All messages and files are scoped to a traceable execution ID
Communication is deterministic, observable, and validated by design

🧠 Coordinators and Planners¶

Coordinators and Planners are special roles in the ConnectSoft Agentic System responsible for:

Managing agent clusters
Generating or enforcing execution strategies
Balancing deterministic FSMs with adaptive planning
Handling cross-agent logic, retries, or escalation flows

“Coordinators lead agent teams. Planners decide what those teams should do.”

👥 Coordinator Agents¶

A Coordinator is an agent that manages other agents within a bounded cluster (e.g., QA, Engineering).

Responsibilities¶

Responsibility	Description
Monitor downstream agents	Observe execution, event outcomes, and coverage
Retry or redirect work	Detect failure or missing outputs and trigger rework
Manage internal agent flows	Define sequence, priorities, dependencies
Surface escalation events	Emit `CoordinatorEscalationRequested` or `InterventionRequired`
Apply domain rules	E.g., fail deployment if test coverage is below threshold

Examples¶

Coordinator	Managed Agents
Test Coordinator	`test-generator`, `qa-engineer`, `bug-investigator`
Release Coordinator	`devops-agent`, `deployment-coordinator`, `security-agent`
Edition Coordinator	`feature-mapper`, `pricing-agent`, `localization-agent`

✅ Coordinators are agents themselves, not orchestrator logic.

🧠 Planner Agents¶

A Planner is a reasoning engine or planning agent that creates execution graphs, skill chains, or task flows on the fly.

Planners use:

Semantic Kernel or LLM-based agents
Project memory + current trace state
Execution history or DSL deltas
Available agent/skill registry

Responsibilities¶

Function	Description
Compose agent skill plans	Select agents, skills, and DSLs per task
Decide parallel vs sequential flows	Analyze task graph and dependencies
React to partial completions	Modify plans if only some agents succeed
Handoff to orchestrator	Emit FSM-compatible plan steps or `AgentPlanReady` event

Example: Planner Output (Simplified)¶

execution:
  flowName: BookingServiceCodegen
  steps:
    - skill: GenerateHandler
      agent: backend-developer
      input:
        dslRef: dsl/Booking/ddd.yaml
    - skill: GenerateSpecFlow
      agent: test-generator
      input:
        dslRef: dsl/Booking/test.yaml
    - skill: CommitCode
      agent: code-committer

🔁 How Coordinators and Planners Work Together¶

sequenceDiagram
    participant PLAN as Planner
    participant COORD as Coordinator
    participant AGENT as Agent

    PLAN->>COORD: AgentPlanReady
    COORD->>AGENT: ExecuteSkill
    AGENT-->>COORD: ArtifactReady
    COORD->>PLAN: ReportSuccess

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

Planners decide
Coordinators manage
Agents execute

🔍 Use Cases¶

Scenario	Planner?	Coordinator?
Custom skill path for MVP module	✅	❌
Retry a failing test	❌	✅
Parallelize handler + test gen	✅	✅ (executes it)
Adjust flow based on feedback	✅	✅

🧠 Studio Support¶

Feature	Description
Coordinator View	See agent cluster status, retries, handoffs
Planner Trace	Visual execution graph built by planner
Coordination Timeline	See cluster-local FSMs and transitions
Skill Chain Explorer	Drill into multi-skill agent plans

✅ Summary¶

Coordinators govern clustered execution inside agent domains
Planners generate skill chains and execution paths dynamically
Together, they balance structure and adaptability across the agentic system
Both are fully observable, auditable, and governed by traceable events

🧠 Knowledge, DSLs, and Prompting in Agentic Systems¶

The core power of the ConnectSoft agentic system comes from the fusion of three knowledge types:

DSLs: Structured, declarative system design intent
Semantic Knowledge: Embedded memory, documents, definitions
Prompts: Instructional, contextualized triggers for skill execution

Together, they form a triad of intelligence that lets agents:

Act independently
Generate high-quality artifacts
Adapt based on context
Maintain alignment to vision and architecture

“DSLs define what to build. Memory explains why. Prompts decide how.”

🔹 1. DSLs (Domain-Specific Languages)¶

DSLs are YAML/JSON documents like:

ddd.yaml, event.yaml, pipeline.yaml, adapter.yaml, execution.yaml

They represent:

Aggregate structures
Commands and events
Pipelines and test plans
Infrastructure bindings

📍 Agents use DSLs as the contract and structured input.

Each DSL carries metadata:

$meta:
  traceId: trace-xyz123
  dslType: DDD
  context: Booking
  agentId: domain-modeler

✅ DSLs are versioned, validated, and routed via orchestrators.

🔹 2. Semantic Knowledge & Memory¶

Agents use memory to:

Understand feature purpose and constraints
Reuse naming, patterns, and logic from prior agents
Retrieve definitions, code snippets, past feedback, or test failures

📁 Memory is scoped by:

projectId, context, traceId, agentId
Long-term vector embeddings (for semantic search)
DSL-aligned knowledge entries (e.g., feature: BookAppointment)

✅ Accessed using semantic search or memoryScope field.

🔹 3. Prompts & Instructions¶

Prompts are:

Generated by orchestrators or planners
Injected with DSL and memory context
Tailored to each agent's persona and task

📘 Example Prompt:

“You are the Backend Developer Agent. Using the BookAppointment command in ddd.yaml, generate a Clean Architecture-compliant handler. Reference the aggregate Appointment. Inject test observability if required.”

📎 Prompt may include:

DSL snippet
Memory injection (relevant project knowledge)
Constraints (use CQRS, use NHibernate, etc.)

🧠 Combined Execution Context¶

Agent Invocation Package¶

{
  "traceId": "trace-2025-05-17-001",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "dslRef": "dsl/Booking/ddd.yaml",
  "prompt": "...",
  "memoryScope": "booking",
  "injectedMemory": [
    { "title": "BookAppointment domain rule", "content": "Only available slots can be booked." }
  ]
}

✅ This package ensures repeatable, observable, and AI-powered execution.

🔍 Orchestration Layer Role¶

The orchestrator:

Resolves DSLs from registry
Injects memory using memory-injection-agent or planner rules
Generates prompts using prompt-template.yaml or LLM skills
Passes the package to the selected agent/skill for execution

📊 Observability¶

Tracked	Description
DSL Traceability	`dslRef`, `dslVersion`, `context`
Prompt Logs	Prompt input/output logged (opt-in)
Memory Hits	Log which memory entries were used
Skill Outcome	What was generated, and was it valid?

✅ Summary¶

DSLs define what the agent needs to build
Semantic memory explains why and how it was done before
Prompts guide the agent’s execution with DSL + memory context
This triad makes the system intelligent, reusable, and context-aware

⚙️ Agent Skill Loading & Kernel Functions¶

Every agent in ConnectSoft is composed of skills — modular, reusable capabilities that are:

Registered in the platform
Executed via a Semantic Kernel (SK) runtime
Observed and traced via skillId, traceId, and outputs

Agents use a skill execution engine (based on Microsoft Semantic Kernel) that combines:

Native C# functions
Prompt templates with memory/DSL inputs
Planner-chained operations
Output routing to artifact stores, PRs, DSLs, or events

“Skills are the programmable muscle of agents — composable, observable, and context-aware.”

🔹 What Is a Skill?¶

A skill is a named executable unit that:

Accepts structured input (dslRef, prompt, memory)
Performs codegen, analysis, validation, or planning
Emits a result: file(s), events, or metadata

Each skill is registered under an agent:

{
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "description": "Generates C# command handler from DDD DSL"
}

🛠️ Skill Types¶

Type	Example	Used By
Native Function	`GenerateCsClass`, `MergeJsonSchema`	API Designer, Domain Modeler
Prompt Template	`GenerateHandler`, `ExplainFeature`	Backend Developer, Product Manager
Planner Chain	`EmitTestSuiteAndRun`	QA Agent, Planner Agent
Validation Skill	`ValidateDslSchema`, `CheckCoverage`	DSL Validator, QA Agent
Routing Skill	`TriggerNextAgent`, `RouteArtifact`	Coordinator Agent

🧠 How Skills Are Loaded¶

Agents load skills during initialization based on:

Their agent ID
Task description
Matching dslType, context, or skillId

The orchestrator or planner sends:

dslRef
memoryScope
executionPlan.step[]
Optional: DSL snippet, code sample, prior output

📘 Example Skill Manifest¶

agentId: backend-developer
skills:
  - skillId: GenerateHandler
    type: prompt
    input: ddd.yaml (command)
    output: .cs file
    requires:
      - dslRef
      - prompt
      - contextMemory
  - skillId: EmitEventContract
    type: native
    input: event.yaml
    output: C# DomainEvent

🔄 Prompt + Memory + DSL = Skill Input¶

Skills use a combination of:

promptTemplate: Prompt file with variables
dslRef: Path to a specific DSL input
memoryScope: Knowledge chunks for context injection
systemContext: Project-wide info like architecture style, tenant config

The Semantic Kernel runtime composes these inputs into a skill execution context.

⚙️ Skill Invocation Example¶

{
  "skillId": "GenerateHandler",
  "agentId": "backend-developer",
  "traceId": "trace-001",
  "dslRef": "dsl/Booking/ddd.yaml",
  "prompt": "Generate handler for BookAppointment...",
  "memoryScope": "booking",
  "outputExpected": [ "BookAppointmentHandler.cs" ]
}

→ Skill runs, result logged, files written, event emitted: CodeGenerated

📊 Observability Signals¶

Signal	Description
`skill_execution_total`	Count per `skillId`, `agentId`
`skill_execution_latency`	Timing for each skill run
`skill_error_total`	Failure count per skill
`skill_output_checksum`	Digest of file/DSL results
`skill_memory_injected`	Log of memory used in execution

🧩 Planner Role (Optional)¶

A planner agent may compose skill chains dynamically
Planner can decide:
- skillId
- agentId
- promptTemplate
- dslInput and memory segments
Result: multi-step autonomous execution with trace linkage

✅ Summary¶

Skills are modular, traceable actions that agents use to produce artifacts
They use Semantic Kernel runtime to combine:
- DSLs
- Prompts
- Memory
- Planner logic
Skills are observable, retryable, versioned, and auditable
They are the foundation of agent intelligence and automation

📊 Traceability and Observability in Agentic Systems¶

In the ConnectSoft Agentic System, every DSL, agent execution, skill invocation, and generated artifact is fully traceable and observable.

This ensures:

Reproducibility of outcomes
Accountability for every agent and skill
Debuggability across multi-agent flows
Visibility into how decisions were made and what was produced

“In ConnectSoft, every code file can explain where it came from, who generated it, and why.”

🧬 Core Traceability Identifiers¶

Field	Description
`traceId`	Unique execution identifier for a complete agent flow (e.g., product delivery lifecycle)
`dslRef`	Path to the source DSL that triggered or guided the action
`dslVersion`	Version of the DSL used (semver)
`agentId`	Identifier for the agent that executed the task
`skillId`	The specific skill executed by the agent
`outputChecksum`	Digest of generated file or artifact
`executionState`	Phase of the FSM lifecycle when the agent acted

📦 Where Metadata Appears¶

Layer	Metadata Used
DSL Headers	`traceId`, `dslType`, `agentId`, `version`
Agent Invocation	`skillId`, `dslRef`, `memoryScope`, `prompt`
Artifact Files	File-level metadata in comments or headers
Events	All events (e.g., `CodeGenerated`, `TestFailed`) contain trace tags
Studio	Filters, timelines, and artifact linkage views
Audit Log	Full historical timeline per project or service

📘 Sample Artifact Metadata¶

BookAppointmentHandler.cs

// Generated by: backend-developer
// Skill: GenerateHandler
// DSL: dsl/Booking/ddd.yaml
// Trace ID: trace-2025-05-17-001
// DSL Version: 1.0.2
// Timestamp: 2025-05-17T13:02:45Z

🔍 Example Event Log Entry¶

{
  "event": "CodeGenerated",
  "traceId": "trace-2025-05-17-001",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "dslRef": "dsl/Booking/ddd.yaml",
  "artifact": "BookAppointmentHandler.cs",
  "status": "success"
}

📈 Observability Signals¶

Metric/Event	Description
`agent_execution_total`	Per agent, per skill
`dsl_validation_total`	Valid/invalid DSLs
`skill_execution_latency_seconds`	How long each skill took
`artifact_output_checksum_mismatch_total`	Diff between expected and actual code
`trace_execution_duration_seconds`	Duration of a full lifecycle
`fsm_state_transition_total`	Movement between execution phases

🧠 Studio Views for Traceability¶

View	Capability
Trace Timeline	View every agent action in sequence
Artifact Explorer	Click any file to see DSL + skill + agent
Event History Viewer	Filter by event type, traceId, or agentId
Skill Chain Viewer	See skill-to-skill execution flow
Test Coverage Trace	Map DSL features to tests to agents

🔐 Compliance & Audit¶

All trace logs are immutable and versioned
Every DSL, plan, and output can be exported for audit or rollback
Optional integration with SIEM/logging platforms (Elastic, Azure Monitor)

✅ Summary¶

ConnectSoft's agent system is fully traceable and observable
Every file, event, and decision is linked to:
- DSL
- Agent
- Skill
- Execution trace
Studio enables end-to-end visibility for developers, architects, and QA
This guarantees accountability, explainability, and debuggability at scale

📣 Agent Events and Bus Contracts¶

In the ConnectSoft AI Software Factory, all agent activity is coordinated through a shared internal event bus using typed, traceable events. These event contracts:

Trigger agent activation
Enable agent-to-agent collaboration
Communicate success/failure and outputs
Drive orchestration FSMs
Power full observability and replayability

“In ConnectSoft, agents don’t call each other — they speak through events on the shared bus.”

📦 Event Bus Overview¶

Property	Description
Transport	In-memory, MassTransit, Azure Service Bus, or Kafka
Format	Strict JSON schema per event type
Scope	Project-level trace ID (`traceId`)
Routing Keys	Based on `dslType`, `eventType`, `context`, or `agentId`
Persistence	Retained for auditing, replay, and debugging

🧩 Common Agent Events¶

Event Type	Purpose
`VisionDocumentCreated`	Vision phase output ready
`DslGenerated`	DSL file produced (e.g., `ddd.yaml`)
`DslValidated`	DSL passed validation, safe to consume
`AgentTaskStarted`	Agent is beginning execution
`CodeGenerated`	Artifact (e.g., `.cs` file) emitted
`TestCaseGenerated`	`.feature` or `.test.cs` produced
`ArtifactCommitted`	Artifact committed to Git or package registry
`TestFailed`	QA agent signals a broken flow
`ExecutionBlocked`	Missing dependency or validation failure
`ExecutionComplete`	Final state reached for the execution trace

📘 Sample Event Payload¶

{
  "event": "CodeGenerated",
  "traceId": "trace-2025-05-17-001",
  "dslRef": "dsl/Booking/ddd.yaml",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "artifact": "BookAppointmentHandler.cs",
  "status": "success",
  "timestamp": "2025-05-17T13:02:00Z"
}

🔐 Event Schema Validation¶

Each event type is validated against a strict JSON Schema.

For example:

`DslGenerated` Schema¶

{
  "type": "object",
  "required": ["event", "traceId", "dslRef", "agentId", "dslType"],
  "properties": {
    "event": { "const": "DslGenerated" },
    "traceId": { "type": "string" },
    "dslRef": { "type": "string" },
    "agentId": { "type": "string" },
    "dslType": { "type": "string" }
  }
}

✅ Ensures event integrity and routing safety.

🔄 Agent Event Flow (Example)¶

sequenceDiagram
    participant ARCH as Enterprise Architect
    participant DEV as Backend Agent
    participant QA as Test Generator

    ARCH->>Bus: DslGenerated (ddd.yaml)
    Bus->>DEV: DslValidated (trigger execution)
    DEV->>Bus: CodeGenerated (BookAppointmentHandler.cs)
    Bus->>QA: ArtifactReady
    QA->>Bus: TestCaseGenerated (BookAppointment.feature)

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

🧠 Event Bus + FSM Integration¶

The orchestration FSM listens for event types like:

on:
  - event: DslValidated
    if: dslType == "DDD"
    then:
      agent: backend-developer
      skill: GenerateHandler

→ Events determine who to trigger, when, and with what context.

📊 Observability Metrics¶

Metric	Description
`events_published_total`	Count by type and `agentId`
`event_dispatch_latency`	Time from emit → handle
`event_failure_total`	Events that failed schema validation
`trace_event_count`	Event volume per `traceId`
`orphaned_event_detected`	Events that didn’t trigger downstream agents

✅ Summary¶

Events are the lifeblood of agent coordination
Every action — from code generation to PR creation — emits traceable events
Events are structured, validated, routed, and logged
Orchestrators and agents subscribe to events, not each other
Enables full decoupling, replay, audit, and observability

🧑‍💻 Human-Agent Collaboration¶

While the ConnectSoft Agentic System is designed for autonomous software generation, human experts still play a critical role in:

Approving, guiding, or overriding agent decisions
Providing feedback or clarification during execution
Performing strategic interventions (e.g., product direction, security policies)

This section defines where and how humans and agents collaborate, with safety, observability, and traceability.

“Autonomy doesn’t mean isolation — agents collaborate with humans as part of the system.”

🤝 Human Roles in the Factory¶

Role	Responsibilities
Product Owner	Approve features, scope, and MVPs
System Architect	Review blueprints, bounded contexts
QA Lead	Confirm test results, validate test coverage
Security Officer	Approve sensitive configuration, access policies
CTO / Release Manager	Sign off on release readiness or rollback decisions

📍 Human Touchpoints¶

🔹 Prompt Initiation¶

Human users submit vision prompts via CLI, web UI, or Studio
Example: “Build a platform for veterinary appointment scheduling”

🔹 Vision & Product Review¶

Agents produce vision.md, product-plan.yaml
Humans can:
- Approve or refine scope
- Add constraints (e.g., “Must support multi-tenant”)

🔹 Blueprint Review¶

Before DSL generation, humans may review:
- system-blueprint.yaml
- bounded-contexts.json

🔹 Pull Request Review (via Studio)¶

Generated PRs can be reviewed by humans
They can:
- Approve
- Comment
- Trigger agent rework (e.g., retry AgentId)

🔹 Exception Handling¶

If ExecutionBlocked, the system may:
- Notify a human via email or Studio
- Prompt user for manual decision or input

🔄 Event-Based Escalation¶

Agents emit escalation events when human input is required:

Event Type	Trigger
`HumanReviewRequested`	Agent output marked incomplete or ambiguous
`ApprovalRequired`	Deployment, pricing, security policies
`HumanOverrideAccepted`	User input overrides agent recommendation
`InterventionRequired`	Planner cannot proceed autonomously

Example:

{
  "event": "HumanReviewRequested",
  "traceId": "trace-abc",
  "agentId": "domain-modeler",
  "artifact": "ddd.yaml",
  "reason": "Missing mandatory business rule"
}

🧠 Human-Generated Feedback¶

Humans can submit structured feedback:

On artifacts (e.g., code, DSLs, test coverage)
On agents (e.g., mark skill inaccurate)
On planning flows (e.g., “Don’t parallelize handler + test in this domain”)

✅ Stored in the execution feedback registry and injected into future planner contexts.

📊 Studio Integration¶

Feature	Description
Artifact Review Panel	Comment and approve code or DSLs
Execution Feedback Form	Suggest corrections or improvements
Escalation Queue Viewer	See where agents are blocked or requesting input
Human-Only DSL Editor	Inline edit, override, or regenerate DSL segments

🔐 Access & Security¶

Human approvals are permission-gated by role
Escalations can be directed to specific personas (e.g., release-manager)
All human inputs are versioned, audited, and traceable

✅ Summary¶

Humans play a strategic, decision-making, and review role in the agentic system
Collaboration happens through events, Studio actions, and structured feedback
Escalation flows are built-in, safe, and observable
Human guidance improves quality, correctness, and alignment with business goals

🔐 Security and Isolation Boundaries in Agentic Systems¶

In a platform where autonomous agents can:

Generate and commit code
Configure infrastructure
Trigger deployments

…it is critical to ensure strict security boundaries, access controls, and isolation mechanisms.

This protects:

Multi-tenant SaaS data
System integrity
Compliance and auditability

“Autonomy must never compromise control. Agents operate in constrained, observable environments.”

🧱 Layers of Isolation¶

Layer	Enforcement
Trace Isolation	All agent activity is scoped by `traceId` and cannot cross contexts
Agent Role Scoping	Each agent can only execute specific skills, access limited inputs
DSL Context Scoping	Agents may only consume DSLs tagged with their permitted `context` or `tenant`
Memory Scope	Memory segments are tenant-aware and context-isolated
Artifact Output Fencing	Agents may only write to `outputs/traceId/agentId/` paths

🛑 Forbidden Behaviors by Design¶

Action	Prevented By
Agent writing to unrelated context DSL	DSL registry rules + trace metadata
Cross-agent memory access	Memory router + vector isolation per traceId
Arbitrary network access	Agents are containerized with outbound policy restrictions
Agent triggering other agent directly	Must emit event → orchestrator routes it
Rewriting existing artifacts	File immutability enforcement in output registry

🔐 Access Control Model¶

Agent Component	Enforced Scope
Agent Execution	Must match `agentId` in execution plan or FSM
Skill Loading	Skills only loaded from registered capability registry
Secrets Access	Only allowed via injected bindings (never raw)
PR Creation	Requires specific orchestration-level approval
DSL Write Permissions	Allowed only by DSL Generator or Coordinator agents with correct context and trace ownership

🔐 Tenant-Level Security¶

Each agent execution is associated with a tenantId
DSLs, memory, artifacts, and metrics are partitioned per tenant
Even shared templates or blueprints are read-only from the agent’s view
Secrets are resolved via scoped Key Vaults or encrypted mounts

🧠 Security-Aware Agents¶

Some agents are specifically designed to enforce or validate security:

Agent	Role
Security Architect Agent	Emits IAM DSL, validates roles, scopes
Secret Injection Agent	Injects vault-bound secrets into pipeline.yaml
Deployment Gatekeeper	Blocks production deploys without human approval and test coverage validation

📊 Audit & Compliance Features¶

Artifact	Tracked By
Agent actions	`execution-log.json` with `traceId`, `agentId`, `skillId`
File writes	SHA hash stored and diffed
Memory access	Logged with `agentId`, `segmentId`, and time
DSL changes	Versioned and diffable
Events	Logged and schema-validated before publish
Approvals	Enforced via Studio or signed event payloads

✅ All data is exportable for compliance reports (SOC 2, ISO 27001, etc.).

🧩 Security Best Practices¶

All DSLs are read-only once published
Agent environments are stateless, minimal, and sandboxed
All output directories are write-once-per-trace
External integrations (e.g., GitHub, Azure) use scoped agent service identities
Prompt input and memory injection are filtered and sanitized

✅ Summary¶

ConnectSoft enforces multi-layered agent isolation and scoped permissions
Agent actions are trace-anchored, DSL-guarded, and output-fenced
Security is enforced via both infrastructure and orchestration logic
Auditing, compliance, and tenant isolation are built into the platform by design

🔁 Fallback, Retry, and Escalation in Agentic Systems¶

No autonomous system is perfect. To ensure reliability, the ConnectSoft Agentic System implements a resilient execution model with:

Deterministic retries
Skill- or agent-level fallback strategies
Event-driven escalation to humans or alternate agents

“Resilience in autonomy means agents must fail safely, recover predictably, and escalate when needed.”

🔄 Retry Model¶

✅ When Retries Occur¶

Scenario	Trigger
Skill execution fails (timeout, crash)	`AgentTaskFailed` event
Artifact validation fails	`ArtifactValidationFailed`
Missing required DSL or memory	`ExecutionBlocked`
Agent returns partial/incomplete output	`PartialOutputDetected`

🔁 Retry Mechanism¶

Retries are idempotent and trace-aware
Retries are tracked via retryCount and retryReason
Orchestrator may set:
- Max retries (default: 2)
- Retry backoff (exponential or fixed)
- Retry only for specific skillId or errorCode

🧩 Fallbacks¶

When retry fails or agent is unavailable, the orchestrator or planner can invoke a fallback strategy.

Type	Example
Agent fallback	Use `alternative-agent` to run same skill
Skill fallback	Run `SimplifiedPromptHandler` instead of full `GenerateHandler`
Planner fallback	Adjust execution plan to skip non-critical step
Output fallback	Use cached artifact version if newer output fails validation

✅ All fallbacks are logged and marked with wasFallback: true.

📘 Sample Fallback Declaration¶

execution:
  steps:
    - skill: GenerateHandler
      agent: backend-developer
      fallback:
        agent: fast-codegen-agent
        skill: GenerateHandlerLite
        reason: "Main agent failed twice"

📣 Escalation Events¶

When no fallback is available or human input is required, agents or coordinators emit escalation events:

Event	Trigger Scenario
`HumanReviewRequested`	Agent produces incomplete/misaligned result
`ApprovalRequired`	Before production deploy or pricing DSL
`AgentClusterBlocked`	Multiple retries failed in a subcluster
`EscalationToPlanner`	Planner required to replan the path

📎 Escalation events include:

traceId, agentId, skillId
retryHistory[]
Human-readable error message or guidance

🧠 Role of Coordinator in Fallbacks¶

Coordinator agents handle:

Tracking failed agents in their cluster
Orchestrating retries or invoking fallbacks
Deciding when to escalate to human
Applying domain-specific recovery rules

Example: QA Coordinator retries test generation once, then invokes Bug Investigator agent.

📊 Observability Signals¶

Metric	Description
`agent_retry_total`	Total retries per agent and skill
`fallback_invoked_total`	Number of fallback activations
`escalation_event_total`	Count of escalations emitted
`retry_latency_seconds`	Time added by retry delay
`agent_failure_rate`	% of failed vs successful executions per skill

✅ Summary¶

Retries handle transient issues (e.g., skill failure, partial output)
Fallbacks redirect the task to alternate agents, skills, or simplified flows
Escalations notify humans or planners when automation cannot continue safely
Coordinators and orchestrators govern the full retry-escalation loop with traceable events

🕸️ Agent Mesh Topology¶

The agent mesh in ConnectSoft represents the interconnected network of all agents, their roles, and their collaborative execution flows.

It defines:

How agents are grouped into clusters
How clusters hand off to one another
How skills, DSLs, and memory propagate
How execution is distributed and composable

“An agent mesh is not a pipeline. It’s a graph of intelligent nodes working together with shared purpose.”

🧠 Key Characteristics¶

Property	Description
Modular	Each agent is independently deployable and replaceable
Trace-Isolated	All communication scoped to a `traceId`
Event-Driven	No direct calls between agents — always event-based
Planner-Aware	Agents can be orchestrated via declarative plans
Composable	Clusters and agents can be reused across domains or modules

🧩 Mesh Structure¶

graph TD
    V1[Vision Architect] --> PM[Product Manager]
    PM --> EA[Enterprise Architect]
    EA --> DM[Domain Modeler]
    EA --> AP[Adapter Generator]
    DM --> BD[Backend Developer]
    BD --> TG[Test Generator]
    TG --> QA[QA Coordinator]
    BD --> DO[DevOps Agent]
    QA --> RM[Release Coordinator]

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

Each node = an agent Each edge = event-driven handoff (e.g., DslValidated, CodeGenerated)

👥 Agent Clusters¶

Cluster	Agents
Vision Cluster	Vision Architect, Product Manager
Architecture Cluster	Enterprise Architect, Domain Modeler, API Designer
Engineering Cluster	Backend Developer, Adapter Generator, Frontend Dev
Quality Cluster	Test Generator, QA Engineer, Bug Investigator
Release Cluster	DevOps Agent, Deployment Coordinator, Security Agent

🔄 Inter-Cluster Communication¶

Pattern	Description
Sequential	Vision → Architecture → Engineering
Parallel	Backend + Test + Docs from same DSL
Conditional	Only run QA cluster if `test.yaml` emitted
Recursive	Planner re-invokes previous cluster on validation failure

📘 Execution Path Example (BookingService)¶

traceId: trace-booking-001
path:
  - vision-architect
  - product-manager
  - enterprise-architect
  - domain-modeler
  - backend-developer
  - test-generator
  - qa-engineer
  - devops-agent

Each step emits:

AgentTaskStarted
ArtifactGenerated
AgentTaskCompleted

→ All linked in the mesh graph with traceId.

🛠 Mesh Assembly by Orchestrator¶

Orchestrator uses execution plans or planner-generated graphs to dynamically construct the mesh per project.
Agents are activated based on:
dslType
fsmState
agentGroup
Event contract (e.g., DslValidated, FeatureScopeReady)

✅ The mesh is dynamic, not hardcoded.

🧠 Benefits of Mesh Design¶

Benefit	Result
Reusability	Agent clusters can be shared across domains
Scalability	Clusters can run in parallel for large service graphs
Resilience	Failures can be contained to subgraphs
Traceability	Mesh edges define full software genealogy
Composability	New services or flows can reuse existing mesh segments

📊 Studio Features for Mesh Topology¶

View	Description
Mesh Graph Viewer	Node-edge graph of current trace mesh
Cluster Timeline	Execution duration by agent group
Mesh Health Dashboard	Failures, retries, and success rate by cluster
Execution Path Replay	Reconstruct mesh from `traceId` and outputs

✅ Summary¶

The agent mesh is a graph of modular, event-coordinated agents executing a software project
Clusters operate independently but communicate via events and trace metadata
Orchestrators and planners build and manage the mesh dynamically
Studio provides real-time and historical views of mesh health, flow, and topology

🚀 Deployment & Runtime Execution of Agents¶

To support scalable, modular, and autonomous execution, all agents in the ConnectSoft platform are designed to run in distributed, containerized, and orchestrated environments.

This section outlines:

Where agents run
How they’re deployed
What their runtime lifecycle looks like
How execution is triggered and scaled

“In ConnectSoft, agents are microservices — but with intelligence, traceability, and planning.”

🧱 Deployment Model¶

Component	Hosting Pattern
Agent Containers	Docker images deployed to Kubernetes
Orchestrator	Long-running service pod or hosted SaaS
Planner Agent	Stateless SK-based microservice
Memory Store	Redis, CosmosDB, or vector DB (e.g., Qdrant)
DSL Registry	Versioned Git or Object Storage (Azure Blob, S3)
Artifact Store	Git repositories, CI/CD pipelines, or shared volumes

📦 Agent Runtime Topology¶

flowchart TD
    Trigger[Event: DslValidated] --> Orchestrator
    Orchestrator --> AgentPod[backend-developer agent pod]
    AgentPod --> DSLRegistry
    AgentPod --> MemoryStore
    AgentPod --> ArtifactStore
    AgentPod --> Bus[Event Bus]

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

Each agent runs as a stateless container, scaled horizontally by execution load.

⚙️ Runtime Characteristics¶

Capability	Description
Stateless Execution	Agent state is trace-bound and externalized
Trace-Aware Containers	Each invocation scoped by `traceId`
Input Ingestion	DSLs, memory, plan, and prompts are injected at runtime
Output Emission	Artifacts and events are written to stores/buses
Skill Loading	Skills loaded from internal registry or file system
Cold Start Safe	All dependencies resolved on invocation

🧠 Execution Context Example¶

Each agent instance receives an execution context:

{
  "traceId": "trace-2025-05-17-001",
  "agentId": "backend-developer",
  "dslRef": "dsl/Booking/ddd.yaml",
  "skillId": "GenerateHandler",
  "memoryScope": "booking",
  "executionMode": "autonomous",
  "artifactOutputPath": "out/Booking/"
}

→ Processed by internal kernel for skill execution.

🧪 Deployment Strategies¶

Type	Use Case
Kubernetes Pod	Most agents (default)
Serverless Function	Lightweight agents or on-demand planners
Job Runner / Batch	Long-running execution chains (e.g., code migration agents)
Orchestrator-Side Execution	Coordinators or internal router agents (low latency)

📊 Runtime Observability¶

Signal	Description
`agent_invocation_total`	Number of times agent started
`agent_runtime_duration_sec`	Execution time per skill
`agent_memory_fetch_latency`	Delay in memory injection
`agent_output_size_bytes`	Size of artifacts produced
`agent_container_restarts`	Stability and failure patterns

🔐 Security & Isolation at Runtime¶

Agents run in containers with:
- Read-only volumes for DSL input
- Write-only output directories
- Memory access via scoped identity
No inter-agent direct calls
No access to external internet unless explicitly allowed
Secrets injected only from scoped Key Vault bindings

📦 CICD for Agent Deployment¶

Every agent has a build pipeline
Versioned per agentId:version
Canary deployment supported (e.g., backend-developer@v1.2.0-beta)
Health checks and test harness included in build stage

✅ Summary¶

Agents are deployed as containerized, trace-scoped services
All inputs/outputs are injected and emitted using shared contracts and runtime metadata
The system is cloud-native, secure, and resilient, with horizontal scaling and full observability
Agent execution is orchestrated via events and trace context, not direct RPC

🧬 Versioning and Agent Evolution¶

To support continuous improvement, extensibility, and stability, the ConnectSoft Agentic System is designed to handle:

Versioned agent definitions
Evolution of skills, prompts, and DSL support
Coexistence of legacy and updated agents
Controlled rollout of new behaviors across projects and tenants

“Agents evolve — but their output must remain deterministic, traceable, and safe.”

🔖 Versioning Strategy¶

Every agent is versioned as:¶

<agentId>@<semver>
e.g., backend-developer@1.2.3

What is versioned?¶

Component	Tracked Version
Agent image/container	`agentId@version`
Skills catalog	skillId mappings + definitions
Prompt templates	Per skill per version
Execution plan logic	FSM config version
DSL support schemas	Compatible `dslType` and schema versions
Planner logic (if applicable)	Plan graph templates or reasoning prompts

✅ All versions are registered in the Agent Registry.

📦 Agent Registry Example¶

agentId: backend-developer
version: 1.2.0
skills:
  - skillId: GenerateHandler
    promptTemplate: prompt.v1.2.0.md
    compatibleDsl: ["DDD@>=1.0.0"]
lastModified: 2025-05-17
status: active
isDefault: true

🔁 Coexistence Rules¶

Multiple versions of the same agent can run simultaneously across different traces:

Project	Agent Version Used
BookingService	backend-developer@1.1.0
PaymentsService	backend-developer@1.2.0-beta
LegacyModule	backend-developer@0.9.8

Orchestrator uses either:

Pinned version in execution-plan.yaml
Or default version from the agent registry

⚙️ Upgrade Workflow¶

Step	Description
Developer publishes new agent version	`v1.3.0` pushed to registry and tagged
Studio or orchestrator shows delta	Changed skills, inputs, outputs
Planner can opt-in to newer agent	Via config, user override, or experimental tag
Legacy traces continue on old version	Ensures deterministic execution
Canary deploy or rollback supported	Based on success/failure metrics

🛡 Safe Evolution Practices¶

Principle	Enforcement
Backward compatibility	Skills declare input/output schema versions
Immutable artifacts	Older artifacts remain untouched after upgrade
Controlled rollout	New agent versions deployed to specific clusters only
Regression validation	QA agents validate new skill behavior before rollout
Agent linting	Checks for missing trace metadata, validation gaps

📊 Observability and Metrics¶

Signal	Description
`agent_version_usage_total`	Which versions are active in how many traces
`agent_upgrade_success_rate`	Success ratio for upgraded agents
`agent_version_rollback_total`	Count of forced reverts
`skill_output_diff_detected`	Compares output across versions
`trace_compatibility_warnings`	Mismatch between agent version and DSL version

🧠 Studio UI Support¶

Feature	Description
Version Selector	Choose agent version per trace/project
Skill Diff Viewer	Show prompt/code/schema changes between versions
Compatibility Checker	Warn if agent is incompatible with current DSLs
Agent Rollback UI	Restore previous working agent in single click

✅ Summary¶

Every agent is semver-versioned and registered
Planner/orchestrator selects agent versions per trace or project
Legacy and updated agents can coexist without conflicts
The system supports safe rollout, version-aware execution, and full observability

🧩 Extending the Agent Mesh¶

ConnectSoft’s agentic system is built to be modular and extensible. You can safely introduce new:

Agents
Skills
Clusters
DSL types
Execution flows

…without breaking existing projects or disrupting production flows.

“The mesh is not fixed — it evolves with the platform, one reusable agent at a time.”

🔧 Extension Entry Points¶

What You’re Adding	Extension Pattern
New agent	Define `agentId`, register skills, image, planner support
New skill to existing agent	Update `skills[]` in agent manifest, add prompt or handler
New cluster	Create execution plan block or subgraph, define trigger/transition
New DSL type	Add schema, DSL template, associated agent support
New planner module	Extend planner’s reasoning library or flow composition logic

📘 Example: Add a `Localization Agent`¶

1. Define Agent Manifest¶

agentId: localization-agent
version: 1.0.0
skills:
  - skillId: EmitLanguageResource
    promptTemplate: emit-locale-table.md
    compatibleDsl: ["product-plan.yaml"]

2. Add to Cluster¶

cluster: product-preparation
agents:
  - product-manager
  - localization-agent
  - pricing-agent

3. Execution Plan Rule¶

on:
  event: EditionsDefined
  do:
    agent: localization-agent
    skill: EmitLanguageResource

📁 File & Folder Structure¶

agents/
  localization-agent/
    manifest.yaml
    prompts/
      emit-locale-table.md
    skills/
      EmitLanguageResource.cs
dsl/
  i18n/
    translation.yaml

✅ Follows the same modular convention as all existing agents.

🧠 Best Practices¶

Principle	Description
One role = one agent	Don’t overload agents with multiple responsibilities
Prompt + skill must be versioned	Maintain compatibility and traceability
Reuse DSLs when possible	Avoid DSL sprawl by aligning with existing specs
Trace every action	Emit `AgentTaskStarted`, `CodeGenerated`, etc.
Join a cluster	Don’t create isolated agents; build into the mesh

🛠 Dev Workflow¶

Create agent repo or folder using template
Define agent manifest with agentId, skills, version
Register prompt templates or handlers
Deploy container or function
Update agent registry
Add orchestration rules or planner support
Test with a disposable trace
Activate for a project or DSL

📊 Studio Integration¶

Feature	Description
Agent Registry Manager	Add/update agent metadata
DSL Compatibility Map	Link agent → skill → DSL
Mesh Preview UI	Show how new agent fits in
Agent Execution Tester	Trigger skill with sandboxed input
Approval Flow for Activation	Optional review before mesh-wide activation

🧬 Evolution Support¶

You can version agents and add new versions non-destructively
New agents can run in parallel with old ones on different projects
Planner can experiment with new agents for specific service types or DSL patterns
Coordinators can use fallback-to-new-agent logic gradually

✅ Summary¶

The agent mesh is modular, dynamic, and extendable by design
You can safely add new agents, skills, clusters, and flows
Mesh growth is governed by:
- trace safety
- skill and DSL contracts
- observability
- studio-based governance

🐞 Agent Debugging and Observability Dashboard¶

To ensure agentic autonomy is safe, reliable, and transparent, ConnectSoft includes a rich debugging and observability layer. It empowers developers, architects, and product owners to:

Inspect agent behavior in real time
Understand execution flow and failures
Replay or rerun specific traces or steps
Analyze output consistency across skills and versions

“You should never have to guess what the agents did, why they did it, or what they produced.”

🔍 Key Debugging Tools¶

Tool	Purpose
Agent Log Viewer	View console logs and metadata per agent execution
Trace Explorer	Visualize full agent timeline for a given `traceId`
Execution Graph	DAG or sequence of skill/agent invocations
Artifact Inspector	See generated code/test/output + diff history
Retry & Fallback Log	Show attempts, errors, fallbacks, and decisions
Prompt Replay Console	Re-invoke a skill with DSL + prompt + memory manually

🧬 What Can Be Debugged?¶

Component	What You See
DSLs	Input schema, validation status, change history
Agents	Agent ID, version, skills used, memory injected
Skills	Prompt, output, latency, errors
Artifacts	Generated files + their metadata
Events	Full log of event emissions per trace
Memory	Memory segments injected and their source

📊 Metrics Captured¶

Metric	Purpose
`agent_execution_total`	How often an agent was invoked
`skill_output_latency_seconds`	Duration of skill execution
`agent_error_total`	Number of failures (by skill)
`dsl_validation_status`	Schema validation success/failure
`trace_execution_duration_seconds`	Total time for trace execution
`agent_retry_count`	Retry attempts per trace/skill

→ Metrics are exportable to Prometheus, Grafana, or Azure Monitor.

🧪 Prompt Debugging¶

You can inspect:

Prompt input (full string)
Memory injected
DSL snippet used
Output from SK / LLM
Logs from system and skill runtime
Output diffs across retries or versions

✅ All prompt calls are traceable, replayable, and auditable.

🧠 Studio Dashboard Features¶

Feature	Description
Agent Execution Timeline	Step-by-step view of all agent invocations
Skill Output Viewer	Inspect output of each skill (JSON, .cs, .feature)
Error Inspector	Shows error + traceback, memory state, retry info
Artifact Diff Viewer	Compare current vs past agent output
Manual Skill Replayer	Rerun a skill with new prompt, DSL, or memory
Test Replay Tool	Re-execute failed test cases from trace context

🔐 Safety Measures¶

Replay is sandboxed unless explicitly committed
Re-executed skills don’t affect production unless confirmed
Prompt output is logged only if audit mode is enabled
Execution hashes are validated before replacing artifacts

✅ Summary¶

Debugging is first-class and developer-friendly
Every agent action is observable, auditable, and replayable
Studio provides a full execution dashboard with prompt logs, trace explorer, and artifact inspection
Prompt replays, retry insights, and fallback tracking ensure safe autonomy with full control

🔗 Examples: Agent Chains per Cluster¶

To bring the abstract system to life, this section presents realistic, end-to-end execution flows (agent chains) within specific clusters such as:

Vision & Product Planning
Architecture & Modeling
Engineering & Code Generation
Testing & Quality
DevOps & Release Management

Each chain is traceable via a single traceId, and each agent handoff is governed by events and DSLs.

“Clusters are autonomous teams. Agent chains are how they deliver value.”

🔹 1. Vision & Product Planning Cluster¶

Agents:¶

vision-architect
product-manager
edition-strategist

Flow:¶

sequenceDiagram
    participant V as Vision Architect
    participant P as Product Manager
    participant E as Edition Strategist

    V->>P: VisionDocumentCreated
    P->>E: ProductPlanCreated

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

Outputs:¶

vision.md
product-plan.yaml
editions.yaml

🔹 2. Architecture & Modeling Cluster¶

Agents:¶

enterprise-architect
domain-modeler
api-designer
event-architect

Flow:¶

sequenceDiagram
    participant EA as Enterprise Architect
    participant DM as Domain Modeler
    participant AP as API Designer
    participant EV as Event Architect

    EA->>DM: BoundedContextDefined
    DM->>AP: DslGenerated (ddd.yaml)
    AP->>EV: ApiSpecReady

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

Outputs:¶

system-blueprint.yaml
ddd.yaml, event.yaml, openapi.yaml

🔹 3. Engineering & Codegen Cluster¶

Agents:¶

backend-developer
frontend-developer
adapter-generator

Flow:¶

sequenceDiagram
    participant BD as Backend Developer
    participant FD as Frontend Developer
    participant AG as Adapter Generator

    BD->>FD: HandlerReady
    BD->>AG: DslValidated (adapter.yaml)

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

Outputs:¶

BookAppointmentHandler.cs
BookingRepositoryAdapter.cs
CreateBooking.vue or .razor

🔹 4. Testing & QA Cluster¶

Agents:¶

test-generator
qa-engineer
bug-investigator

Flow:¶

sequenceDiagram
    participant TG as Test Generator
    participant QA as QA Engineer
    participant BUG as Bug Investigator

    TG->>QA: TestCaseGenerated
    QA->>BUG: TestFailed

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

Outputs:¶

BookAppointment.feature
TestCoverageReport.json
failure-log.yaml, annotated diffs

🔹 5. DevOps & Release Cluster¶

Agents:¶

devops-agent
deployment-coordinator
release-manager

Flow:¶

sequenceDiagram
    participant DO as DevOps Agent
    participant DC as Deployment Coordinator
    participant RM as Release Manager

    DO->>DC: PipelineGenerated
    DC->>RM: EnvironmentReady

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

Outputs:¶

pipeline.yaml
deployment-manifest.yaml
release.json

📦 Full Trace Example: BookingService¶

traceId: trace-booking-2025-001
execution:
  - vision-architect
  - product-manager
  - enterprise-architect
  - domain-modeler
  - backend-developer
  - test-generator
  - qa-engineer
  - devops-agent

✅ This flow produces a working service, tested, with CI/CD in place — all fully traceable.

🧠 Observability Insights¶

Cluster	Metric Example
Vision	`vision_to_product_latency_seconds`
Architecture	`blueprint_to_dsl_latency`
Engineering	`codegen_success_total`, `handler_output_checksum`
QA	`test_pass_rate`, `test_to_fix_time`
DevOps	`pipeline_success_rate`, `release_cycle_duration`

✅ Summary¶

Agent chains define how clusters deliver functional outcomes
Each chain is governed by events, traceId, DSLs, and FSMs
Outputs are modular, versioned, and linked to their input context
You can inspect any chain in Studio, replay it, or extend it