📡 Observability-Driven Design¶

🔍 Why Observability Is Foundational in a Software Factory¶

In ConnectSoft, observability is not a feature — it’s a design constraint. Every agent, microservice, orchestrator, pipeline, and artifact must emit signals that allow the platform to understand what happened, why, and with what effect.

“If the platform can’t see it, it can’t trust it. If you can’t trace it, it didn’t happen.”

Observability allows ConnectSoft to:

Diagnose and resolve agent failures
Track blueprint execution across modules
Enforce policy via runtime signals
Optimize cost and performance
Deliver real-time feedback into AI-generated software lifecycles

🧠 What Makes Observability First-Class¶

Capability	Why It Matters
Traceability	Every action is linked to a `traceId`, `agentId`, and `skillId`
Accountability	Users, agents, and orchestrators are all auditable via telemetry
Reusability	Observable modules can be tested, simulated, and regenerated safely
Feedback loops	Agent prompts and outputs are monitored for accuracy, latency, and result quality
Multi-tenant visibility	Every signal is scoped by `tenantId`, `environment`, and `moduleId`

🔁 Observability Enables the Factory Lifecycle¶

flowchart TD
  BlueprintSubmission --> AgentExecution
  AgentExecution --> ServiceGeneration
  ServiceGeneration --> Deployment
  Deployment --> TelemetryEmission
  TelemetryEmission --> ObservabilityLoop
  ObservabilityLoop --> FeedbackToAgents

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

✅ Every stage emits observability signals that are collected, traced, and used for validation and evolution.

🔧 Observability vs Monitoring¶

Monitoring	Observability
Predefined metrics	Ad hoc questions and unknowns supported
Dashboard-first	Trace-first, lifecycle-driven
Reactive alerts	Proactive trace + audit + insight
Focused on services	Focused on factory activity, agent flow, and blueprint health

🔐 In a Secure Factory, Observability Also Enables:¶

Detection of misused secrets or unsafe agent scopes
Auditing for privileged actions across Studio, CLI, and pipelines
Policy violations and feedback for security, compliance, and cost control
Regression identification from test → release → production
AI assistant feedback loops with context tracing and hallucination detection

📊 Studio’s Observability Dependency¶

Core Studio features powered by observability:

Trace explorer (agent flow per blueprint)
Blueprint health dashboard
Module performance metrics
Cost and error heatmaps
Event log for BlueprintParsed, AgentExecuted, ModuleDeployed, PolicyViolated

✅ Summary¶

In ConnectSoft, observability is a design requirement — every system component, agent, and trace must emit telemetry
This powers debuggability, traceability, security, policy enforcement, AI validation, and multi-tenant insights
Observability isn’t bolted on — it’s part of the software factory’s DNA

🧠 Traceable Agent Execution¶

Every action in ConnectSoft — whether it’s code generation, infrastructure provisioning, or blueprint parsing — is performed by an agent executing a skill. To maintain trust, safety, and reproducibility, each execution is fully traceable using structured observability identifiers:

“Every skill, every agent, every line of output — linked to a trace.”

🧬 Core Identifiers for Agent Traceability¶

Identifier	Description
`traceId`	Globally unique ID for the execution of a single blueprint across agents and modules
`agentId`	The identity of the agent persona executing a skill (e.g., `backend-developer`, `security-architect`)
`skillId`	The name of the operation being performed (e.g., `GenerateHandler`, `EmitOpenApi`)
`tenantId`	Tenant or customer context; defines scope and data boundaries
`moduleId`	Logical component under construction (e.g., `BookingService`)
`executionId`	Optional ephemeral ID representing a single agent run or retry within a trace

📘 Example Execution Metadata¶

{
  "traceId": "trace-93df810a",
  "agentId": "frontend-developer",
  "skillId": "GenerateComponent",
  "moduleId": "CustomerPortal",
  "tenantId": "vetclinic-001",
  "status": "Success",
  "durationMs": 1842,
  "outputChecksum": "sha256:faa194..."
}

→ Stored in execution-metadata.json, logged, and referenced in telemetry events.

📊 Why This Structure Matters¶

Use Case	Supported By Identifiers
Blueprint replay	`traceId` links agent sequence and inputs
Multi-agent coordination	`executionId` tracks skill chains and retries
Tenant isolation	`tenantId` enforces scoping and metrics partitioning
Failure debugging	`agentId + skillId` quickly locates failed runs
Audit & compliance	`traceId` and `userId` pair enable traceable change logs

🔄 End-to-End Trace Flow¶

sequenceDiagram
  participant Studio
  participant Orchestrator
  participant Agent
  participant Service

  Studio->>Orchestrator: Submit blueprint (traceId)
  Orchestrator->>Agent: Execute skill (agentId + skillId)
  Agent->>Service: Emit artifact (moduleId + tenantId)
  Service-->>Orchestrator: Acknowledge (executionId)

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

✅ Every step is logged and observable.

🔍 In Telemetry Streams¶

All logs, spans, and metrics emitted by agents and services include:

traceId
agentId
skillId
moduleId
tenantId
status, duration, error (if applicable)

→ This ensures observability is not only consistent — it's queryable, filterable, and correlatable.

📊 Studio Agent Trace View¶

Interactive trace explorer: shows all agent executions in a trace
Drill-down into skill-level logs and metrics
Breadcrumb from traceId → module → agent → skill
Failure annotation and retry history per skill

✅ Summary¶

ConnectSoft enforces full traceability of every agent action using traceId, agentId, skillId, tenantId, and moduleId
These identifiers are embedded in all telemetry, enabling deep insight, correlation, and validation of autonomous agent behavior
Traceability is the backbone of observability in the AI Software Factory

📄 Structured Logging Strategy¶

In ConnectSoft, logs are not just text — they are structured, identity-enriched telemetry objects. Every log emitted by an agent, service, orchestrator, or tool is designed for searchability, redaction, traceability, and machine-driven correlation.

“Logs aren’t written for humans — they’re written for agents and analyzers first.”

This section describes how ConnectSoft implements a structured, secure, and contextual logging approach to power observability at scale.

🧩 What Is Structured Logging?¶

A structured log is an object, not a string — typically a JSON-encoded payload with fixed fields, optional metadata, and semantic meaning.

Example:

{
  "timestamp": "2025-05-11T12:33:24Z",
  "level": "Information",
  "traceId": "trace-abc123",
  "agentId": "api-designer",
  "skillId": "GenerateOpenApi",
  "tenantId": "vetclinic-001",
  "moduleId": "BookingService",
  "message": "Generated 4 OpenAPI operations",
  "durationMs": 217,
  "status": "Success"
}

✅ Log structure supports filtering, alerting, redaction, correlation, and replay.

🧠 Logging Fields Required in ConnectSoft¶

Field	Purpose
`timestamp`	For time-based queries, trace timelines
`level`	Supports filtering by severity (Error, Warning, Info, Debug)
`traceId`	Ties log to full execution lifecycle
`agentId` / `skillId`	Shows actor + capability
`tenantId` / `moduleId`	Enables isolation and aggregation
`message`	Human-readable summary
`status`	Indicates success/failure of the action
`exception`	If present, includes stack trace or error message
`tags` (optional)	Custom dimensions for advanced analysis

🔒 Secure Logging: Redaction and Sensitivity¶

Sensitive fields (accessToken, email, password) are automatically masked
Logs never include raw secrets or PII unless explicitly marked safe
Blueprint fields can declare sensitivity: pii → triggers log redaction logic
Agent prompt output is summarized, not stored in full

✅ Violations emit RedactionFailure events during test or runtime.

🧪 Logging Anti-Patterns Prevented¶

Anti-Pattern	Blocked By
Plaintext secrets	Redaction engine + blueprint linter
Tenantless log lines	Runtime middleware rejects missing `tenantId`
Unstructured logs (e.g., `Console.WriteLine`)	Not supported in templates — flagged in CI
Logs without `traceId`	CI linter + orchestrator validator

🧠 Logging Levels Guidance¶

Level	Usage
`Debug`	Internal diagnostic traces (e.g., "Retrying skill...")
`Information`	Key events: agent actions, deployments, test results
`Warning`	Recoverable errors, degraded modes
`Error`	Failed actions, assertion failures, execution exceptions
`Critical`	System-wide failures, security violations, data loss risk

📊 Studio Log Explorer Features¶

Filter logs by:
- agentId, skillId, tenantId, traceId, status, level
Redaction indicator on sensitive fields
Time slider to navigate execution timelines
Log volume heatmaps per module/agent
Correlation to metrics and traces for integrated triage

✅ Summary¶

ConnectSoft logs are structured, enriched, and security-aware by default
Logging acts as machine-parseable observability telemetry, not just human-readable output
All logs include traceId, agentId, tenantId, and masking support to ensure auditability, safety, and clarity

📈 Metrics for Agents, Services, and Modules¶

In ConnectSoft, metrics are first-class telemetry signals emitted from agents, services, orchestration layers, and the platform runtime. These metrics power dashboards, alerts, cost analytics, SLA enforcement, and behavioral tuning across the AI Software Factory.

“If we can't measure it per module, per tenant, per skill — we can’t optimize it.”

This cycle covers the types of metrics ConnectSoft emits, how they’re structured, and how they enable visibility at scale.

🧩 Metric Categories¶

Category	Example Metrics
Agent execution	`agent_execution_duration_seconds`, `agent_failures_total`, `agent_success_rate`
Skill-level metrics	`skill_latency_seconds`, `skill_output_size_bytes`, `skill_retry_count`
Blueprint-level	`blueprint_traces_started_total`, `blueprint_success_rate`, `blueprint_regeneration_count`
Microservices	`http_requests_total`, `db_query_duration_seconds`, `cache_hit_ratio`, `queue_length`
Tenant/module scope	`tenant_active_services_total`, `module_errors_per_minute`, `tenant_resource_cost_usd`

✅ Every metric is tagged with traceId, tenantId, moduleId, and environment.

📘 Example: Agent Metrics Output (Prometheus format)¶

# HELP agent_execution_duration_seconds Duration of agent skill execution
# TYPE agent_execution_duration_seconds histogram
agent_execution_duration_seconds{agentId="frontend-developer", skillId="GenerateComponent", tenantId="vetclinic-001", moduleId="CustomerPortal", status="Success"} 1.184

→ Collected by Prometheus, visualized in Grafana or Studio dashboards.

🧠 Metric Dimensions (Standard Tags)¶

Label	Purpose
`agentId`	Who performed the action
`skillId`	What capability was used
`tenantId`	Which tenant's trace it belongs to
`moduleId`	Which service/module the metric is scoped to
`traceId`	Lifecycle trace context
`status`	success/failure/error type
`environment`	dev/staging/production/preview

🧪 Metric-Based Test Assertions¶

Generated tests include assertions like:

agent_success_rate > 95%
skill_latency_seconds < 2.0
module_errors_per_minute < threshold
http_5xx_errors = 0 on new routes
cost_metrics match SLA tiers

✅ These metrics are used for release gates, regressions, and anomaly detection.

🤖 Metric-Emitting Agents¶

Agent	Metrics
`AgentCoordinator`	Execution counts, duration, retry rate per skill
`Observability Engineer Agent`	Metric template injection, Prometheus scrapers
`Test Generator Agent`	Metrics used for test coverage assertions
`DevOps Engineer Agent`	Emit cost and infra metrics tied to blueprint output

📊 Studio Metric Explorer¶

Dashboards by:
- Agent, Skill, Module, Tenant, Environment
Sparkline and histogram visualizations
Live views of queue backlogs, error rates, execution trends
Alert conditions (e.g., failure rate > X%, trace stuck > Y min)
Cross-linked to logs and traces for context

🔐 Security & Cost Metrics¶

secrets_access_total
unauthorized_requests_total
service_identity_mtls_failures_total
agent_execution_cost_usd
resource_consumption_per_tenant

✅ Summary¶

ConnectSoft emits rich, dimensioned, and actionable metrics for every agent, skill, trace, module, and environment
These metrics power Studio dashboards, CI/CD release gating, anomaly detection, cost optimization, and compliance enforcement
Metrics are tagged, scoped, and standardized across the platform for full traceability and automation

🔀 Distributed Tracing with OpenTelemetry¶

ConnectSoft relies on OpenTelemetry-based distributed tracing to capture the full lifecycle of blueprint execution, agent workflows, microservice calls, infrastructure operations, and external interactions — all in a traceable, tenant-aware, and versioned format.

“In a factory of autonomous agents, spans are the glue that tells the truth.”

This section outlines how traces and spans are constructed, linked, and analyzed to deliver true end-to-end observability.

📐 What Is a Trace?¶

A trace is a complete picture of an operation — such as a blueprint execution or module deployment — made up of spans representing steps within that operation.

📦 Span Metadata Structure¶

{
  "traceId": "trace-123",
  "spanId": "span-456",
  "parentSpanId": "span-000",
  "name": "GenerateHandler",
  "startTime": "2025-05-11T12:32:44Z",
  "durationMs": 842,
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "moduleId": "BookingService",
  "tenantId": "vetclinic-001",
  "status": "Success",
  "attributes": {
    "outputSizeBytes": 2048,
    "retries": 0
  }
}

✅ Every span includes standard tags and optional custom dimensions.

🧩 Span Types in ConnectSoft¶

Span Type	Examples
Agent skill execution	`GenerateComponent`, `EmitDTO`, `RefactorHandler`
Service API call	`POST /api/booking`, `GET /availability`
Blueprint phase	`ParseBlueprint`, `EnforceSecurityPolicy`, `PlanExecutionTree`
Deployment	`EmitReleaseYaml`, `CreateNamespace`, `InjectSecrets`
External I/O	Git fetch, Azure Key Vault access, queue interaction

🔗 Span Relationships¶

graph TD
  Blueprint[ParseBlueprint] --> SkillA[GenerateOpenAPI]
  SkillA --> SkillB[GenerateController]
  SkillB --> Deploy[EmitReleaseArtifacts]

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

✅ Spans are linked hierarchically to show execution order and performance impact.

🔍 Where Traces Are Emitted¶

Agents via SDKs or built-in span emitters
Services via OpenTelemetry SDK (e.g., AddOpenTelemetryTracing() in .NET)
Pipelines via orchestrators and CI plugins
Studio events and command triggers

🧪 Trace-Based Test & Failure Analysis¶

Detect incomplete traces → TraceTimeoutDetected
Span duration regression → SkillLatencyIncreased
Missing span → TelemetryGapAlert
Failed agent run → ErrorSpan with exception + status
Retry spans emit retryCount, retryDelay, retryOutcome

📊 Studio Trace Explorer¶

Timeline view: shows agent-to-skill-to-service execution as Gantt-style flow
Dependency graph: module-to-module trace correlation
Span diff: compare blueprint v1.2 vs v1.3 on skill performance
Cost overlay: per-span execution cost attribution
Security context: see tenantId, role, authClaims per span

🧠 Use Cases Unlocked by Distributed Tracing¶

Use Case	How Spans Enable It
Prompt regression	Compare `skillId=GenerateHandler` performance over time
Multi-agent bottlenecks	Trace execution delays across agents in orchestration
Incident forensics	Root cause analysis tied to `traceId` and `errorSpanId`
SLA violation detection	Detect if blueprint execution exceeds time budget
Blueprint replay	Regenerate full artifact chain based on trace log

✅ Summary¶

ConnectSoft uses OpenTelemetry-powered distributed tracing to monitor and analyze every phase of blueprint and agent execution
Traces are made of linked spans, each enriched with identity, skill, and outcome metadata
Tracing enables observability, diagnostics, performance optimization, and runtime verification at scale

🧭 Execution Events and Factory State Transitions¶

In ConnectSoft, the entire AI Software Factory operates as a state machine, orchestrated through a series of explicit execution events. These events represent transitions between phases in the factory lifecycle — from blueprint parsing, to skill execution, to release deployment — and are observable, traceable, and auditable in real time.

“Every meaningful state change in the factory emits a signal.”

This section details how execution events serve as the source of truth for observability and orchestration across agents, services, and environments.

📬 What Is an Execution Event?¶

An execution event is a structured telemetry object emitted when a significant state change occurs in the platform. These events:

Drive trace timelines and dashboards
Power Studio’s lifecycle visualizations
Trigger automation (e.g., test, validate, alert)
Anchor decisions in orchestration logic
Form the audit log for compliance

🧾 Standard Factory Execution Events¶

Event Type	Description
`BlueprintParsed`	Blueprint successfully validated and decomposed into modules/skills
`AgentExecutionRequested`	Agent skill triggered by orchestrator
`AgentExecuted`	Agent finished executing a skill (success, failure, duration)
`SkillValidated`	Output passed all structural or policy checks
`ModuleGenerated`	Code or artifact emitted by agent
`DeploymentTriggered`	Release initiated for an environment
`ReleasePromoted`	Version approved and moved to target stage
`SecurityPolicyViolated`	Attempted unsafe action or failed policy
`TraceCompleted`	Full factory flow completed for blueprint trace
`ObservationFeedbackIssued`	AI model feedback loop triggered (optional)

📘 Example Event: `AgentExecuted`¶

{
  "eventType": "AgentExecuted",
  "timestamp": "2025-05-11T13:20:54Z",
  "traceId": "trace-987",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "status": "Success",
  "durationMs": 1180,
  "tenantId": "vetclinic-001",
  "moduleId": "BookingService"
}

✅ Automatically linked to logs, spans, metrics, and audit trails.

🧩 Events vs Logs vs Spans¶

Signal	Purpose
Logs	Line-level detail, useful for troubleshooting
Spans	Time-bounded operation steps with performance metrics
Events	High-level state transitions used to coordinate agents and UIs

🔄 Event-Driven Factory Flow (Example)¶

flowchart TD
  BlueprintParsed --> AgentExecutionRequested
  AgentExecutionRequested --> AgentExecuted
  AgentExecuted --> ModuleGenerated
  ModuleGenerated --> DeploymentTriggered
  DeploymentTriggered --> ReleasePromoted

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

✅ Studio listens to this stream and updates live state visualizations accordingly.

🔧 Event Emitters¶

Agents emit: AgentExecuted, SkillValidated, ObservationFeedbackIssued
Orchestrators emit: BlueprintParsed, TraceCompleted, AgentExecutionRequested
CI/CD Pipelines emit: DeploymentTriggered, ReleasePromoted, ReleaseFailed
Policy/Validator Services emit: SecurityPolicyViolated, ComplianceCheckPassed, EventRedacted

📊 Studio Execution Timeline¶

View per-trace event sequence
Correlate events to logs, spans, and agent execution cards
Filter by traceId, tenantId, eventType
Exportable event streams (JSON, CSV) for audit and replay
Trigger-based UI state (e.g., “Waiting for Approval”, “Released to Staging”)

✅ Summary¶

Execution events define the state machine of the ConnectSoft AI Software Factory
These events are used for orchestration, monitoring, compliance, automation, and UI rendering
Combined with spans and logs, they enable real-time observability of the entire factory lifecycle

📜 Blueprint-Aware Observability Contracts¶

In ConnectSoft, observability isn’t only implemented in runtime layers — it is declared explicitly in blueprints. Authors define what should be observable, what metadata should be emitted, and how modules, agents, and APIs should behave in terms of traceability, redaction, metrics, and event flows.

“If observability isn’t in the blueprint — it doesn’t exist at generation time.”

This section explains how blueprints express observability contracts, and how agents and templates enforce them during generation and execution.

📘 Example: Observability Contract Block in Blueprint¶

observability:
  tracing:
    enabled: true
    traceIdStrategy: auto
    spanTags:
      - agentId
      - skillId
      - tenantId
      - userId
  logging:
    level: Information
    redactionPolicy: pii
  metrics:
    enabled: true
    emitCustom: true
    tags:
      - moduleId
      - environment
  events:
    emitExecutionEvents: true
    include:
      - AgentExecuted
      - ModuleGenerated
      - PolicyViolated

→ Drives trace instrumentation, structured logging, telemetry tagging, and event stream emission.

🧠 Benefits of Blueprint-Level Observability Contracts¶

Benefit	Result
✅ Declarative trace expectations	Agents auto-inject trace logic with required tags
✅ Metric compliance	Ensures all modules expose required usage, error, and latency metrics
✅ Redaction enforcement	Logging policies follow declared PII/sensitivity levels
✅ Studio readiness	Dashboards are scaffolded based on declared contract needs
✅ Policy alignment	Trace schema and events can be validated against organizational rules

🧩 Contract Elements Supported¶

Element	Description
`tracing.enabled`	Enables OpenTelemetry trace wiring with specified tags
`logging.level`	Sets default minimum log severity for module
`redactionPolicy`	Chooses masking behavior (`pii`, `secret`, `all`)
`metrics.enabled`	Injects Prometheus/OpenTelemetry exporters with default counters
`emitExecutionEvents`	Ensures state changes generate high-level events

🤖 Agent Responsibilities¶

Agent	Observability Skill
`Observability Engineer Agent`	`ApplyTracingInjection`, `EmitSpanInstrumentation`, `DefineMetricEmitters`
`Backend Developer Agent`	`HonorLoggingPolicy`, `AttachRedactionAttributes`, `EmitMetricScaffolding`
`Infrastructure Engineer Agent`	`GenerateTracingConfigMap`, `ExposePrometheusPorts`, `BindEventsToQueue`
`Test Generator Agent`	`EmitTraceIntegrityTests`, `AssertMetricPresence`, `RedactionBehaviorValidation`

🛠️ Contract Validation & Enforcement¶

During Codegen:
Linter ensures redaction rules exist for PII fields
Missing traceId injection is flagged
metrics.enabled: false on public services → warning
During CI/CD:
observability-contract-checker runs
Missing logs, metrics, or spans fail observability-completeness test
Blueprint rejected if contract is violated during test simulation

📊 Studio Integration¶

View declared observability contract alongside blueprint source
Visual “contract vs reality” diff per module (green = covered, red = missing)
Alert stream for:
TraceDropped
MetricNotEmitted
EventSuppressed
Audit log showing when observability contracts were changed and by whom

✅ Summary¶

Observability in ConnectSoft begins at the blueprint level — with contracts that declare trace, metric, logging, and event expectations
These contracts are validated, enforced, and tested throughout the factory pipeline
This approach guarantees consistent, secure, and complete observability across modules and environments

🏷️ Span Enrichment and Custom Dimensions¶

In ConnectSoft, spans are not just performance markers — they are context-rich records of platform activity. Each span is enriched with tags that describe the actor, context, scope, input, and expected outcome. This makes spans queryable, correlatable, and machine-usable for diagnostics, insights, and feedback loops.

“If logs tell you what happened, spans tell you why — and who was responsible.”

This cycle outlines how spans are automatically enriched and how teams can extend span metadata with custom dimensions.

🧠 Why Span Enrichment Matters¶

Reason	Benefit
✅ Root cause analysis	Tags show the actor (`agentId`), the intent (`skillId`), and the context (`tenantId`)
✅ Multitenancy traceability	Every span is scoped by tenant/module/environment
✅ Prompt feedback	Span tags help correlate prompt failures, hallucinations, or retries
✅ Dependency mapping	Cross-span dimensions enable service/module topology
✅ SLO tracking	Spans emit timing, outcome, and error reason in a standard way

📘 Example: Enriched Span JSON¶

{
  "traceId": "trace-001",
  "spanId": "span-002",
  "name": "GenerateOpenApi",
  "startTime": "2025-05-11T13:22:08Z",
  "durationMs": 382,
  "attributes": {
    "agentId": "api-designer",
    "skillId": "GenerateOpenApi",
    "moduleId": "BookingService",
    "tenantId": "vetclinic-001",
    "inputSource": "blueprint",
    "outputFormat": "yaml",
    "status": "Success",
    "outputSizeBytes": 2048
  }
}

✅ Spans like this power dashboards, feedback loops, and debugging.

📦 Standard Span Tags in ConnectSoft¶

Tag	Meaning
`agentId`	Who triggered the span
`skillId`	What capability was used
`moduleId`	Module/component acted upon
`tenantId`	Tenant context for multi-tenant safety
`environment`	dev/staging/prod
`durationMs`	Total time taken
`status`	Success/Failure/Error type
`retries`	Number of execution attempts
`outputSizeBytes`	For generation-based spans
`inputChecksum`	Hash of prompt/input for validation
`userId` (optional)	When user-triggered
`costUsd` (optional)	Cost of agent execution (if measured)

🛠️ Agent/Template Responsibilities¶

Component	Span Injection
`Observability Engineer Agent`	`InjectSpanTags`, `EmitOpenTelemetryScaffolding`, `ValidateTagCoverage`
`All Agents`	Auto-enrich every span with `agentId`, `skillId`, `traceId`, `tenantId`
`Code Templates`	Add enrichment middleware in `.NET`, Node, Python, etc.
`Microservices`	Emit spans using OpenTelemetry SDKs with standard tag injectors

🧪 Validation and Enforcement¶

Span validation tests:
Missing traceId → InvalidSpanDropped
Unknown skillId or agentId → flagged in CI
outputSizeBytes > threshold → triggers LargeSpanPayloadDetected
Blueprint contract may require mustTag: [agentId, skillId, status]

📊 Studio Usage of Enriched Spans¶

Filter spans by:
Agent
Skill
Tenant
Module
Time window
Result status
Generate:
Latency heatmaps
Cost-per-skill dashboards
Retry frequency histograms
Module topology diagrams

✅ Summary¶

Spans in ConnectSoft are context-enriched telemetry units, not raw timers
Standardized and custom tags power trace filtering, cost analysis, debugging, test feedback, and more
All agents and services emit spans with consistent identifiers and metadata, enabling deep platform introspection

🧪 Testing via Observability¶

In ConnectSoft, observability is not just for operations — it is also a primary validation mechanism for test automation. Instead of relying solely on hardcoded assertions or brittle mocks, agents and pipelines use logs, metrics, and spans as test oracles to validate correctness, security, performance, and compliance.

“If it’s observable, it’s testable — and in ConnectSoft, everything is observable.”

This cycle explores how observability signals are used to generate, assert, and verify test outcomes across the AI Software Factory.

🧩 Observability-Backed Assertions¶

Signal	Used to Assert
Span metadata	Duration, retries, skill usage, status (e.g., `durationMs < 2000`)
Logs	PII redaction, correct logging level, error presence, traceId consistency
Metrics	Thresholds for error rate, response time, success ratio
Execution events	Lifecycle correctness (e.g., `AgentExecuted → SkillValidated → DeploymentTriggered`)
Traces	Full-path validation: all required spans present and connected
Cost metrics	Validate cost-per-agent-execution within expected bounds

📘 Test Case Example: Security Redaction via Logs¶

test:
  name: "Email field is redacted"
  input: GET /api/customers/123
  expectLogs:
    - level: Information
      doesNotContain: "customerEmail"
    - level: Information
      contains: "customerEmail": "***REDACTED***"

→ The test passes by evaluating log content and masking logic — no additional mocking required.

🔍 Span-Based Test Example¶

assertSpan:
  skillId: GenerateHandler
  status: Success
  durationMs: "<1500"
  tags:
    tenantId: vetclinic-001
    outputSizeBytes: "<5000"

✅ Enables performance regression detection over time.

🔧 How Tests Are Emitted¶

Agent	Testing Skill
`Test Generator Agent`	`EmitSpanAssertions`, `LogRedactionAssertions`, `MetricsThresholdTests`
`QA Agent`	`AssertBehaviorFromTelemetry`, `GenerateAuditTraceTest`, `TraceCompletionValidation`
`Security Architect Agent`	`VerifyUnauthorizedEvents`, `ScanLogsForUnmaskedSecrets`

🧪 Types of Observability-Powered Tests¶

Test Type	Example
Redaction & masking	Logs must not contain raw PII
Auth validation	Unauthorized API calls emit specific logs and events
Retry correctness	Span shows exponential backoff and retry reason
SLO enforcement	Blueprint execution completes within `X ms`, `Y%` success
Cost boundary	`agent_execution_cost_usd < 0.01`
Trace integrity	No orphaned spans, incomplete paths, or unexpected sequence breaks

📊 Studio Test Integration¶

Observability-backed test coverage explorer
View logs/spans/metrics per test case
Highlight gaps (e.g., “No span coverage for skill: RefactorHandler”)
Export failed test → reproducible trace bundle
Overlay test results on service or agent dashboards

✅ CI/CD Pipeline Integration¶

CI runs observability assertions alongside functional and security tests
Failing tests block promotion
Failed traces saved for debugging
--observability-only test mode for telemetry validation without full execution

✅ Summary¶

Observability in ConnectSoft is deeply integrated with automated testing
Logs, metrics, spans, and events act as assertion points for validating:
Redaction
Performance
Correctness
Cost
Flow structure
This allows ConnectSoft to validate dynamic, AI-generated systems safely and continuously

🤖 Prompt Validation and AI Feedback via Observability¶

In ConnectSoft, AI agents generate code, APIs, tests, and infrastructure using natural language prompts and structured instructions. Observability isn’t just used to trace what they do — it is used to evaluate the quality of their output, detect hallucinations, and enable self-improving behavior via feedback loops.

“Observability is how agents get better — not just how we debug them.”

This cycle covers how ConnectSoft leverages telemetry signals to validate prompt outcomes, reinforce agent performance, and guide future generation behavior.

🧠 Why Prompt Observability Matters¶

Problem	Observability-Based Feedback
Hallucinated fields or properties	Detect schema drift via telemetry comparison (blueprint vs output)
Slow response from LLM or plugin	Span latency, retry count, token usage tracked
Invalid code or untestable output	Execution errors linked back to `traceId + agentId + skillId`
Unstable generation (non-idempotent)	Output fingerprint compared across retries or regenerations
Low quality AI output	Post-task scoring via `outputQualityScore` tag or feedback signals

📘 Span with Prompt Metadata (Example)¶

{
  "traceId": "trace-8723",
  "agentId": "api-designer",
  "skillId": "GenerateOpenApi",
  "durationMs": 1382,
  "promptTokens": 512,
  "completionTokens": 1142,
  "outputChecksum": "sha256:f9a1e1...",
  "status": "Success",
  "feedbackScore": 4.5,
  "flags": ["retry:once", "outputMasked", "validation:passed"]
}

→ Used to analyze prompt behavior over time and across agents.

🔎 Prompt Quality Signals¶

Signal	Purpose
`outputChecksum`	Detect duplicate or divergent results from same input
`outputValidationStatus`	Was the generated output schema-valid or test-passable?
`retryCount`	Indicates instability or flakiness of AI response
`feedbackScore`	Human or simulated ranking of output (1–5)
`promptTokens` / `completionTokens`	Cost and verbosity measurement
`aiModelVersion`	Tracks model used for reproducibility and performance regression
`agentPromptHash`	Identifies template used to instruct agent (e.g., “RESTful API with scopes”)

🤖 Agent Behaviors Informed by Observability¶

Behavior	Signal Used
Prompt refinement	High retry + low `feedbackScore` triggers prompt tuning
Trace rejection	Invalid skill output → rollback trace and replan
Auto-retry logic	`outputValidationStatus = fail` triggers retry with fallback
Skill deactivation	Repeated failure → agent enters cooling-off period or marks skill for manual review
Output fingerprinting	Ensures deterministic generation across environments and retries

📦 Feedback Events (Telemetry)¶

Event Type	Description
`PromptValidated`	Output passed structural + semantic checks
`PromptFailed`	Output rejected during test or blueprint comparison
`OutputScored`	Human or test-based rating submitted
`TraceRegenerated`	Blueprint retriggered due to invalid agent output
`AgentPromptTuned`	Agent’s prompt updated based on historical trace stats

🧪 Studio and CI Feedback Loop Integration¶

Feedback modal on generated content (1–5 score + tags)
CI pipeline feedback scoring system from tests + validators
Studio visualization: skillId → feedback histogram
Skill risk index: flakiness × retry rate × failure rate × feedback score
Auto-promotion blocks if feedbackScore < 3.5 or validationFailureRatio > 15%

📊 Prompt Observability Dashboards¶

Latency per model / prompt template
Retry heatmaps per skill
Token cost tracking per agent and module
Output success/failure histograms
Prompt-to-result consistency monitor (across tenants and projects)

✅ Summary¶

Observability in ConnectSoft enables dynamic validation of agent-generated outputs, particularly for LLM-based prompts
Traces, spans, and events capture prompt quality, retry behavior, model cost, and validation outcomes
This allows ConnectSoft to support safe, adaptive, self-correcting agent orchestration — at scale

💥 Resilience via Observability¶

In ConnectSoft, resilience is not just about retries or circuit breakers — it’s about detecting, tracing, and responding to failure patterns using observability signals. When an agent fails, a deployment stalls, or a microservice degrades, observability provides the data and context to automatically recover, retry, or alert — without manual triage.

“You don’t build resilient systems. You build systems that know when they’re not resilient — and act.”

This cycle explains how ConnectSoft uses logs, spans, metrics, and events to detect degradation and trigger autonomous recovery workflows.

🧠 What Resilience Looks Like in the Factory¶

Scenario	Resilience Mechanism
Agent skill fails	Span failure triggers retry with backoff
Deployment fails	`DeploymentFailed` event → rollback or remediation plan
API errors spike	Metric alert triggers scale-up or routing to alternate version
Secret vault timeout	Log + span + healthcheck pattern = automatic retry or failover
Output invalid	Trace rollback initiated with root-cause span correlation

📘 Example Span (with Resilience Signals)¶

{
  "traceId": "trace-541",
  "spanId": "span-883",
  "skillId": "GenerateOpenApi",
  "agentId": "api-designer",
  "status": "Error",
  "errorType": "ValidationFailed",
  "retryCount": 1,
  "retryDelayMs": 3000,
  "fallbackUsed": true,
  "durationMs": 2183
}

→ Connects to failure root cause and recovery path.

🔁 Resilience Patterns Tracked¶

Pattern	Tracked By
`RetryAttempted`	Span tag `retryCount` > 0
`FallbackTriggered`	Event: `FallbackFlowUsed`
`CrashLoopDetected`	Log frequency + span count + probe failures
`DegradedOutput`	Metric deviation + prompt feedback + validation errors
`ExcessiveLatency`	`span.durationMs` exceeds SLO or prior baseline
`SilentFailure`	Trace missing required spans/events triggers `TraceIncomplete` alert

🤖 Agents That React to Observability Failures¶

Agent	Skill
`Orchestrator Agent`	`AbortTrace`, `RestartAgentWithFallback`, `TriggerAlertEvent`
`Observability Engineer Agent`	`EmitRetrySpan`, `DetectCrashLoop`, `AdjustAgentCooldownPolicy`
`Test Generator Agent`	`EmitResilienceTest`, `AssertFallbackBehavior`, `MeasureFailureRecoveryTime`

🧪 CI/CD and Runtime Recovery Triggers¶

error_rate > threshold → rollout blocked or reverted
BlueprintTraceFailed → test rerun and root cause drill-down
SkillValidationFailed → agent auto-retry or alternative skill planner triggered
KubernetesReadinessProbeFailing → deployment rollback + DeploymentRecoveryInitiated event emitted

📊 Studio Resilience Tools¶

Failure heatmaps by skill, module, or agent
Retry success rate dashboard
Degraded performance detector (latency percentile spikes, output quality dips)
Trace repair suggestions (e.g., “try alternate skill”, “inject override”, “rerun sub-trace”)
Resilience score per blueprint or module (based on volatility, stability, retry rate)

🧠 Observability-Driven Decisions Enabled¶

Decision	Signal
Retry vs Abort	Span failure reason + retry history + skill fallback config
Rollback vs Patch	Deployment trace health + coverage report + version drift
Alert vs Self-heal	Confidence in failure pattern + success of automated fix attempts
Replan blueprint	`TraceUnrecoverable` + planner diff evaluation

✅ Summary¶

ConnectSoft uses observability not just to detect failures, but to drive automated recovery and self-healing behavior
Spans, logs, and events carry failure metadata like retry counts, fallback usage, latency deviations, and crash loops
Resilience is measured, tested, and enforced — making the platform robust in the face of errors, outages, and AI unpredictability

📉 Anomaly Detection and Health Signals¶

In ConnectSoft, observability data isn’t just passively collected — it’s actively analyzed to detect anomalies, regressions, and emerging risks. By applying rules, baselines, and statistical models to spans, metrics, and logs, the platform emits early warning signals and triggers remediation or alerting before impact occurs.

“We don’t wait for failure — we surface deviation.”

This section explains how ConnectSoft uses real-time health monitoring and anomaly detection to keep the factory safe, scalable, and predictable.

🧠 What Is an Anomaly?¶

An anomaly is any behavior that deviates significantly from baseline expectations, including:

Latency spikes
Failure rate increases
Missing or malformed telemetry
Unusual log content or volume
Deviation from observed historical patterns

📘 Health Signal Example (Metric-based)¶

{
  "metric": "agent_execution_duration_seconds",
  "agentId": "api-designer",
  "skillId": "GenerateOpenApi",
  "tenantId": "vetclinic-001",
  "value": 3.57,
  "baselineAvg": 1.28,
  "anomalyScore": 92,
  "status": "Warning",
  "signal": "LatencyDeviation"
}

→ Detected by Observability Analyzer → forwarded to Studio and optionally triggers AnomalyDetected event.

🧩 Signal Sources Used¶

Source	Signal Type
Spans	Execution time, retry count, failure density, missing span detection
Metrics	Rate, histogram buckets, percentiles (P95, P99), SLO breaches
Logs	Volume spikes, unknown patterns, unauthorized access, redaction violations
Events	Unexpected transitions (`AgentExecuted` after failed plan), skipped phases
Feedback	Sudden drop in AI prompt quality or user feedback score

🔍 Detection Patterns¶

Pattern	Trigger
`LatencySpike`	span.duration > baseline × multiplier
`RetrySurge`	retry count exceeds threshold for given skill
`NewErrorType`	new error class detected in logs or span failure reason
`TelemetryGap`	expected span/event/log not observed
`CostSpike`	sudden jump in execution or hosting cost
`TraceStalled`	execution stuck without new activity beyond timeout threshold

🤖 Agents That Respond to Anomalies¶

Agent	Skill
`Observability Engineer Agent`	`DetectLatencyRegression`, `EmitAnomalySignal`, `UpdateHealthScore`
`Orchestrator Agent`	`PauseBlueprintExecution`, `TriggerFailoverPlan`, `NotifyStudio`
`Security Architect Agent`	`DetectUnauthorizedAccessPattern`, `EmitRedactionAnomalyAlert`

🧪 Studio & CI Feedback¶

Health Score per module based on:
Latency
Failure rate
Telemetry coverage
Span completeness
Anomaly alerts in Studio activity stream
Incident summaries linked to traces and spans
CI regression blockers: e.g., “latency increased > 50% from baseline”, “failure rate > 10% in last 10 runs”

📊 Studio Health & Anomaly Dashboards¶

Sparkline of anomalies by skill or module
Execution health score history
Anomaly classification (Performance, Security, Data Integrity, Resilience)
Filter anomalies by:
Agent
Module
Tenant
Time window
Export anomaly report (anomaly-report.json)

✅ Summary¶

ConnectSoft actively detects and analyzes anomalies using logs, spans, metrics, and event flows
Health signals are emitted, scored, and acted on — supporting preemptive recovery and regression detection
This approach turns observability into a safety mechanism and performance optimizer at platform scale

⚡ Observability in Serverless and Agentless Flows¶

Not all work in ConnectSoft is performed by long-lived services or stateful agents. Many tasks are executed in transient, event-driven, or on-demand runtimes — such as Azure Functions, AWS Lambda, or short-lived blueprint workers. These "agentless" or ephemeral contexts still require full observability — without persistent infrastructure.

“Just because it’s serverless doesn’t mean it’s sightless.”

This section explains how ConnectSoft ensures tracing, logging, metrics, and execution tracking work seamlessly in short-lived or edge-triggered components.

🧠 Challenges in Serverless Observability¶

Challenge	Resolution
No long-lived process	Emit full observability at startup and teardown
Cold starts obscure latency	Track cold start time as a span tag
Context is missing or partial	Inject `traceId`, `tenantId`, and `agentId` via input binding or wrapper
Logs are ephemeral	Route to central collector via OpenTelemetry or logging gateway
Metrics aggregation difficult	Use remote push/exporters, not local scraping

📘 Example: Azure Function Span (Cold Start + Agent)¶

{
  "traceId": "trace-289",
  "spanId": "span-789",
  "name": "ProcessWebhook",
  "agentId": "webhook-listener",
  "skillId": "ProcessWebhookPayload",
  "status": "Success",
  "durationMs": 1183,
  "coldStart": true,
  "tenantId": "tenant-xyz",
  "inputSource": "queue",
  "executionEnvironment": "azure-functions",
  "tags": {
    "eventId": "evt-00123",
    "functionApp": "cs-notification-worker"
  }
}

📦 What’s Observable in Serverless / Ephemeral Contexts¶

Signal	Method
Trace	OpenTelemetry SDK (`TraceId`, `SpanId`, `ParentId`) — injected via bindings or middleware
Logs	Structured logs with enriched context; forwarded to central store
Metrics	Exported via OpenTelemetry push (e.g., OTLP, Prometheus Pushgateway)
Execution Events	`AgentExecuted`, `FunctionTriggered`, `WebhookReceived`, `TraceCompleted`
Cost	Approximate billing metrics per execution (duration, memory, I/O)

🤖 Agent + Runtime Support¶

Component	Observability Tools
`FunctionTemplateAgent`	Emits wrapped telemetry scaffolding for .NET, Node, Python, etc.
`Orchestrator Agent`	Injects `traceId`, `tenantId`, `agentId` into serverless payloads
`Observability Engineer Agent`	Adds probes, telemetry headers, context injection logic
`Studio Agent`	Maps ephemeral span traces back to persistent project/module

🔐 Security & Multi-Tenant Considerations¶

Each ephemeral function is tagged with:
tenantId
traceId
executionId
Secrets must be accessed via secure bindings or injected via managed identity
Logs are scanned for PII or unredacted sensitive output in post-processing

📊 Studio & Dashboards¶

Function execution viewer
Cold start indicator and impact analysis
Serverless cost-per-execution and trace overlay
Trace-to-function correlation graphs
Failure hot paths (e.g., "Webhook → Function → Missing Span → Retry")

✅ Summary¶

ConnectSoft extends full observability into serverless, ephemeral, and agentless execution environments
Tracing, logging, and metrics are contextualized, centralized, and real-time
This ensures that even short-lived workloads are auditable, debuggable, and performance-visible

📊 Studio Dashboards and Trace Explorers¶

Observability in ConnectSoft doesn’t just live in logs and backends — it powers the Studio, the central UI for managing projects, agents, blueprints, and execution flows. Dashboards and trace explorers give teams live, visual feedback on how the factory is performing, what agents are doing, and how modules behave over time.

“If observability is the bloodstream, Studio is the nervous system.”

This section describes how observability data is rendered, queried, and navigated in Studio to drive decision-making, debugging, auditing, and optimization.

🧭 Core Observability-Driven Views in Studio¶

View	Purpose
📈 Metrics Dashboards	Visualize counters, histograms, rates per module, tenant, or agent
🧵 Trace Explorer	See end-to-end agent executions as timelines or hierarchies
🧪 Test Coverage Map	Visual link between span coverage and test output
🔄 Blueprint Flow Viewer	Displays event-based execution lifecycle with telemetry annotations
💰 Cost Explorer	See cost metrics linked to `traceId`, `agentId`, and `skillId`
🚨 Anomaly Timeline	Highlights spikes, gaps, regressions, or outlier behavior

📘 Example: Agent Execution Timeline¶

[
  {
    "agentId": "frontend-developer",
    "skill": "GenerateComponent",
    "start": "13:04:11",
    "durationMs": 1342,
    "status": "Success",
    "spanId": "span-001"
  },
  {
    "agentId": "qa-engineer",
    "skill": "EmitTestAssertions",
    "start": "13:04:13",
    "durationMs": 734,
    "status": "Success",
    "spanId": "span-002"
  }
]

→ Visualized as a Gantt-style chart within the Trace Explorer tab.

Filter	Use Case
`traceId`	Debug or inspect a specific factory run
`tenantId`	Multi-tenant isolation and analysis
`agentId` / `skillId`	Triage slow agents or flakey skills
`moduleId`	Review microservice-level telemetry
`status`	Locate errors, anomalies, retries
`timeRange`	Analyze execution patterns over time

🔧 Dashboards Powered by:¶

OpenTelemetry spans and metrics
Execution events (AgentExecuted, BlueprintParsed, ModuleDeployed)
Blueprint-declared observability contracts
CI/CD outputs (validation reports, SBOMs, cost estimates)

🧪 Additional Studio Observability Tools¶

Span detail modal: View all tags, logs, duration, cost, retry info
Heatmaps: Errors per skill/module/agent over time
Sankey view: Agent-to-skill-to-module flow with success/failure arrows
Metric panel: Live Prometheus/OTLP widget with Grafana-style expressions
Trace diff: Compare trace A vs B to detect regression, delta, or drift

📦 Exportable Artifacts¶

trace-bundle-{traceId}.zip: All logs, spans, metrics, artifacts, outputs
observability-report.json: Machine-readable summary
ci-insight-summary.md: Human-readable audit for promotion/release docs
cost-breakdown.csv: Per-agent, per-trace, per-module

✅ Summary¶

ConnectSoft Studio transforms raw observability signals into real-time, actionable dashboards
Visual tools give teams insight into agent behavior, trace health, cost patterns, and system anomalies
These features make the invisible visible — supporting debugging, auditability, and optimization

🚨 Alerting, SLOs, and Automated Incident Signals¶

ConnectSoft uses observability not only for visibility but also for real-time alerting and reliability enforcement. By defining Service Level Objectives (SLOs) and coupling them with alerts based on logs, spans, and metrics, the platform can detect degradation, enforce reliability budgets, and trigger automated incident workflows.

“If it violates the SLO — the factory doesn’t ignore it. It acts.”

This section explains how alerting and error budgeting are integrated into agent workflows, orchestrator logic, and Studio dashboards.

🧠 What Are SLOs in ConnectSoft?¶

Term	Definition
SLO (Service Level Objective)	A target threshold for availability, latency, success, etc.
SLA (Service Level Agreement)	External-facing promise (often contract-based)
Error Budget	The maximum allowable number of failures or slow responses over time
SLI (Service Level Indicator)	The actual measurement used to evaluate SLOs (e.g., `99.9% agent success rate`)

📘 Example: Blueprint-Level SLO Declaration¶

slo:
  agentSuccessRate: ">=99%"
  skillLatencyP95: "<1500ms"
  testCoverage: ">=90%"
  observabilityCompleteness: "100%"
  errorBudgetWindow: "30d"

→ Used in validation, alerts, and Studio dashboards.

🧩 Alert Types¶

Alert Type	Trigger
`LatencySLOBreached`	`skill_latency_p95 > declared threshold`
`AvailabilityDrop`	`agent_success_rate < 99%`
`TraceDrop`	Missing required spans or events
`ErrorBudgetExceeded`	Too many errors in budget window
`AnomalyAlert`	Automated signal based on outlier detection
`CIRegressionDetected`	Observability test failure or drift from baseline

🔔 Alert Channels¶

Studio notifications (UI + push)
Slack / Teams / Email via webhook
AlertRaised event emitted to orchestration or ticketing systems
Optional integration with PagerDuty, Azure Monitor, etc.

⚙️ Automation Based on Alerts¶

Trigger	Response
`AgentSLOViolation`	Pause promotion or agent execution; trigger skill review
`ModuleErrorSurge`	Rollback last deployment; initiate hotfix flow
`BlueprintTraceFailure`	Alert blueprint owner; flag trace as blocked
`LatencySpike`	Throttle traffic or initiate scale-up policy
`FeedbackDrop`	Replan skill or send to prompt-tuning agent

📊 Studio Reliability & Alerting Views¶

SLO dashboards per module, tenant, or environment
Real-time alerts with severity and suggested action
Burn rate graphs: track consumption of error budget
Uptime trendlines per skill, agent, or orchestrator
Alert correlation with spans, events, and traces

🧪 Alert Testing and Simulation¶

CI pipelines simulate violations to test alert flow
“Pre-release SLO check” step ensures no budget exceeded
Skill-level observability coverage reports identify missing indicators
Blueprint planner blocks releases with active unresolved alerts

✅ Summary¶

ConnectSoft transforms observability into proactive defense using SLOs, error budgets, and smart alerts
Alerts trigger studio notifications, automated recovery, and agent orchestration changes
SLO definitions are declarative, tested in CI/CD, and visualized in real time

🧩 Observability for Coordination and Orchestration Layers¶

ConnectSoft orchestrates complex, multi-agent workflows across the software factory. To make these processes traceable, debuggable, and resilient, the platform embeds deep observability into the orchestration layer — allowing Studio, agents, and DevOps pipelines to understand who coordinated what, in what order, with what outcome.

“Orchestration without observability is chaos.”

This cycle explores how traces, spans, and execution events reveal the structure and health of orchestration processes — including blueprint execution, skill sequencing, and multi-module coordination.

🧭 Orchestration Observability Goals¶

Goal	Outcome
✅ Trace skill chaining	Understand how one agent leads to another (e.g., `GenerateDTO → EmitHandler → EmitTest`)
✅ Monitor coordinator logic	Detect bottlenecks or failed orchestration branches
✅ Attribute errors to orchestration stage	Quickly identify planning, routing, or scheduling failures
✅ Visualize execution flow	Enable Studio to show what happened, when, and why
✅ Support replay and audit	Provide evidence for trace regeneration, drift analysis, or compliance review

📘 Span Structure in Coordinated Flows¶

{
  "traceId": "trace-556",
  "spanId": "span-001",
  "name": "PlanAgentWorkflow",
  "agentId": "orchestrator",
  "skillId": "PlanExecutionTree",
  "moduleId": "BookingService",
  "durationMs": 142,
  "children": [
    "span-002", "span-003", "span-004"
  ]
}

→ The parent span connects downstream agent executions into a coherent tree.

🧠 Events for Orchestration Observability¶

Event	Purpose
`AgentExecutionRequested`	Agent flow started by orchestrator
`AgentRoutedToSkill`	Specific skill chosen from planner
`AgentSkipped`	Agent excluded from execution (e.g., based on blueprint diff)
`CoordinationFailed`	Orchestration breakdown or planning error
`TraceCompleted`	Blueprint fully processed and execution tree closed

🧪 Failure Attribution Patterns¶

Symptom	Diagnostic Span/Event
Unexecuted agent	No `AgentExecuted` span, `AgentSkipped` event
Invalid agent input	`SkillValidationFailed` with cause = `OrchestratorContextMismatch`
Bottlenecked blueprint	Long `PlanExecutionTree` span, retry storm across agents
Partial trace	Missing child spans in `PlanAgentWorkflow` tree
Retry loop	Repeat spans with same `skillId`, `traceId`, increasing `retryCount`

📊 Studio Coordination Visuals¶

Flow graph: Agent-to-agent execution tree
Planner map: Planned vs actual skill paths
Trace diff: Compare v1 vs v2 of same blueprint coordination plan
Orchestration timeline: Duration of each phase with status annotations
Failure map: Red-highlighted failure points in coordination tree

🔧 Metrics Tracked in Orchestration Layer¶

Metric	Meaning
`orchestration_duration_seconds`	Time to plan and trigger agents
`trace_completion_latency_seconds`	Total time from blueprint submission to completion
`agent_routing_failures_total`	Planner chose a route that failed to execute
`skill_branching_factor`	Number of downstream skills triggered by a given skill
`retry_tree_depth`	Max depth of re-execution for a trace path

✅ Summary¶

ConnectSoft embeds observability into orchestration logic, not just runtime systems
Coordinated agent flows are fully traced, logged, and event-enriched, allowing for replay, audit, optimization, and failure analysis
Studio uses this data to show end-to-end flow topology, skill execution graphs, and coordination outcomes

💰 Cost-Aware Observability¶

In ConnectSoft, observability doesn’t just measure performance or correctness — it also provides real-time visibility into cost drivers. Every agent, blueprint execution, and deployment is traceable not only by trace ID and tenant, but also by its resource consumption and monetary impact.

“If it’s observable, it should be accountable — including in dollars.”

This section explains how observability powers cost transparency, optimization, forecasting, and budget enforcement across the entire platform.

🧾 Cost Dimensions Captured in Telemetry¶

Dimension	Signal Source
Execution time	`span.durationMs` from agent/service
Token usage (LLMs)	`promptTokens`, `completionTokens` span tags
Memory & CPU	Prometheus metrics (`container_memory_usage_bytes`, `cpu_seconds_total`)
Cloud services	Cost tags emitted by infrastructure agents
Storage & bandwidth	Logs from blob usage, DB IO, network spans
Blueprint resource budget	Declared in blueprint → validated via CI pipeline or during planning

📘 Example Span with Cost Metadata¶

{
  "traceId": "trace-804",
  "agentId": "qa-engineer",
  "skillId": "EmitSpecFlowTests",
  "durationMs": 2483,
  "tokenCostUsd": 0.0064,
  "infraCostUsd": 0.021,
  "totalCostUsd": 0.0274,
  "tenantId": "tenant-003",
  "tags": {
    "environment": "staging",
    "moduleId": "AppointmentService"
  }
}

✅ These values are emitted as metrics and logs, used in Studio visualizations and cost alerts.

🧠 Cost-Aware Blueprint Declaration¶

costConstraints:
  maxExecutionCostUsd: 1.00
  maxLLMTokens: 20000
  alertIfCostPercentAboveBaseline: 25%

→ Enforced in:

CI test gates
Agent skill planners
Post-trace cost validator

📊 Metrics & Tags for Cost Monitoring¶

Metric	Purpose
`agent_execution_cost_usd`	Per-agent + per-skill cost measurement
`trace_total_cost_usd`	Aggregate cost of a full blueprint run
`cost_per_token_usd`	Tracking LLM usage across models and vendors
`tenant_cost_month_to_date`	Financial usage per project/tenant
`cost_anomaly_score`	Flags if module cost increased unusually over time

🔔 Cost Alerts & Automation¶

Trigger	Action
`cost_per_trace > $1.00`	Blueprint flagged for optimization review
`LLM usage spike > threshold`	Alert + prompt tuning agent notified
`infraCost drift > 2x baseline`	Deployment blocked or scaling analyzed
`tenantCost > quota`	Alerts + optional API throttling enforced

🧪 Studio Dashboards & Optimization Tools¶

Cost per skill, agent, module, tenant
Time-series of cost trends
Budget usage meter per environment (e.g., dev vs staging vs prod)
“Most expensive traces” leaderboard
Cost forecast simulator for new blueprints

🤖 Agent Responsibilities¶

Agent	Skill
`DevOps Engineer Agent`	`EmitInfraCostMetrics`, `ValidateCostTags`, `CheckQuotaUsage`
`Observability Engineer Agent`	`AggregateCostFromTraces`, `EmitCostAnomalyAlerts`
`Orchestrator Agent`	`BlockOverBudgetExecution`, `TriggerOptimizationPlan`

✅ Summary¶

ConnectSoft embeds cost awareness directly into observability data
Traces, spans, metrics, and events include execution cost, LLM usage, infra spend, and tenant-level billing context
This powers real-time budget enforcement, forecasting, and trace-level cost attribution

🔐 Compliance, Privacy, and Redacted Observability¶

ConnectSoft is a multi-tenant, AI-driven platform that handles sensitive data across regulated domains. Observability in such environments must balance insight with compliance. That means telemetry must be privacy-aware, tenant-scoped, and redacted by default — while still enabling traceability, debugging, and auditing.

“If it leaks in a log, it wasn’t observability — it was a liability.”

This section explains how ConnectSoft enforces redaction, scoping, and compliance-ready observability using field sensitivity tagging, runtime masking, and secure trace policies.

🧠 Why Privacy-Aware Observability Matters¶

Concern	Mitigation
PII in logs or spans	Redaction engine masks or removes sensitive values
Cross-tenant visibility	All observability is tagged by `tenantId` and isolated by design
Secrets in telemetry	Vault values never appear in spans, logs, or metrics
Regulatory needs	SOC 2, GDPR, HIPAA readiness baked into observability outputs
Replays or exports	Sensitive fields redacted or encrypted on export

📘 Blueprint: Declaring Field Sensitivity¶

fields:
  - name: customerEmail
    sensitivity: pii
    redactInLogs: partial
  - name: accessToken
    sensitivity: secret
    redactInLogs: full
  - name: internalNote
    sensitivity: internal-only
    redactInTraces: true

→ Drives log filters, span tag scrubbers, and telemetry masking logic.

🧩 Redaction Modes¶

Mode	Behavior
`partial`	e.g., `user@**.com`, `*-1234`
`full`	Entire value masked or removed
`hash`	Replaced with a one-way hash (`sha256(...)`)
`nullify`	Set to `null` before logging or span export
`contextual`	Only redact in public environments or when risk detected

🔐 Scoped Observability by Design¶

Isolation Type	Enforced By
Tenant scope	All telemetry includes `tenantId`; studio, dashboards, alerts are tenant-filtered
Environment scope	Dev/staging/prod metrics/logs are separated
User context	`userId` used to limit visibility and correlate actions
Blueprint ID	Traceable only by authorized roles with audit access

🧪 Privacy & Compliance Testing¶

EmitRedactedLogsTest, AssertMaskedSpans, BlockUnscopedTelemetry
SpanContainsPII → triggers TelemetryViolationDetected
Studio CI linter: SensitiveFieldMissingRedactionRule
audit-log-validator: ensures redacted copies for export bundles

📊 Studio Compliance Views¶

Redaction status per module and field
Telemetry sensitivity map (PII, secret, internal-only overlays)
Export bundle builder with --redacted, --secure-view, --tenant-scope options
Compliance score indicator per blueprint or release
Anomaly detection: secrets or PII patterns seen in logs or spans

🤖 Agent Contributions¶

Agent	Skill
`Security Architect Agent`	`EnforceTelemetryRedaction`, `ValidateTracePrivacy`, `EmitComplianceAuditEvents`
`Observability Engineer Agent`	`ApplySpanScrubbers`, `InjectLogRedactors`, `AttachPrivacyTagsToMetricLabels`
`Test Generator Agent`	`EmitTelemetryPrivacyTests`, `SimulateSensitiveTraceFailure`

✅ Summary¶

Observability in ConnectSoft is redacted, scoped, and compliant by default
Sensitive data is tagged at the blueprint level and scrubbed across logs, spans, events, and metrics
This ensures full traceability without compromising privacy, security, or auditability

⚠️ Observability Anti-Patterns¶

Even with powerful tools, observability can become dangerous or useless if misused. ConnectSoft identifies and actively prevents a wide range of observability anti-patterns — ensuring telemetry remains accurate, secure, performant, and actionable across thousands of services and agents.

“Bad observability is worse than no observability — it creates false confidence.”

This section outlines common anti-patterns ConnectSoft guards against, and the systems in place to detect and block them.

🧩 Common Observability Anti-Patterns¶

Anti-Pattern	Risk	How ConnectSoft Prevents It
Missing `traceId` in logs or spans	Breaks traceability	CI linter, runtime middleware reject missing context
Unstructured logs (e.g., `Console.WriteLine`)	Impossible to parse, search, or redact	Templates enforce structured logging with metadata
PII in logs	Compliance breach	Redaction rules auto-applied; blueprint sensitivity enforced
Over-logging	Costly, noisy, performance hit	Log rate limiters, `LogNoiseScore` analyzer
Metrics with high cardinality	Resource burn, unreadable dashboards	Metric tag policy validation + Conftest rule
Trace loops (recursive spans)	Trace explosion, broken UIs	Orchestrator blocks circular agent executions
Spans with no service/agent attribution	Unusable trace context	All templates require `agentId`, `skillId`, `moduleId` tags
Shadow observability (duplicated events from non-standard tools)	Inconsistent or misleading data	Studio alerts on mismatched `traceId` + `spanId` structures

📘 Blueprint Linter Checks for Anti-Patterns¶

MissingTelemetryScope
MetricTagCardinalityTooHigh
LogMessageUnstructured
SpanMissingRequiredAttributes
UnmaskedSensitiveFieldInLog
InvalidTraceHierarchyDetected

🧪 Observability Sanity Tests¶

Test	What It Validates
`assertLogStructureConforms()`	All logs use required fields
`assertSpanCompleteness()`	Required spans exist and are linked
`assertTraceCompleteness()`	Trace includes all major phases
`assertNoSecretsInLogs()`	Secrets not present in raw logs
`assertMetricCardinalityBounded()`	Limits unique tag combinations in time window

📊 Studio Views for Anti-Pattern Monitoring¶

Telemetry Coverage Score per module
Span Health Radar — detects orphaned, duplicated, or broken spans
High-Cardinality Metric Watchlist
Log Volume Outlier Map
Compliance Mode: disables raw logs and enables full masking enforcement

🤖 Agents That Mitigate Anti-Patterns¶

Agent	Preventive Skills
`Observability Engineer Agent`	`ValidateTelemetrySchema`, `ScoreLogNoise`, `RejectHighCardinalityMetric`
`Security Architect Agent`	`DetectUnmaskedPII`, `EnforceRedactedTelemetryPolicy`
`DevOps Engineer Agent`	`InjectStandardLogger`, `ReplaceNonCompliantSpanEmitters`
`QA Agent`	`EmitObservabilityCompletenessTests`, `SimulateTraceAnomalies`

✅ Summary¶

ConnectSoft actively detects, blocks, and remediates observability anti-patterns
Blueprint linters, CI checks, runtime middleware, and Studio tools work together to ensure telemetry is clean, complete, and compliant
Observability is only valuable when consistent, secure, and scoped — ConnectSoft makes that the default

✅ Summary and Observability-First Checklist¶

After 20 sections, we’ve established that observability in ConnectSoft is not optional — it’s a design foundation. From blueprint to agent, from skill execution to deployment, observability empowers traceability, compliance, performance, resilience, cost-awareness, and autonomy.

“If it’s worth doing, it’s worth tracing.”

This final section summarizes key principles and provides a checklist for building observable-by-default systems in the ConnectSoft AI Software Factory.

🧠 Core Observability Principles¶

Principle	Description
Trace everything	Every agent skill and orchestration step must emit traceable spans
Structure logs	Logs are structured JSON with `traceId`, `agentId`, and sensitivity-aware content
Emit metrics with context	Every metric is tagged with `moduleId`, `tenantId`, `environment`, and `skillId`
Emit execution events	All major lifecycle transitions are recorded as structured events
Respect privacy and redaction	Sensitive fields must be declared in the blueprint and masked across all outputs
Cover costs	Traces, spans, and metrics must include cost and resource usage indicators
Visualize everything	If you can’t explain it in Studio, it’s not fully observable
Fail on anti-patterns	Incomplete spans, unstructured logs, and high-cardinality metrics are all testable violations

📋 Observability-First Design Checklist¶

🔁 Traces¶

All agent skills emit spans with traceId, agentId, skillId, tenantId
Parent-child span relationships are properly linked
Trace completeness tested in CI

📄 Logs¶

Logs are structured (JSON) and context-enriched
No secrets or PII in logs (validated via blueprint tags)
Logging level controlled per environment (e.g., Debug in dev, Info in prod)

📈 Metrics¶

Prometheus/OpenTelemetry exporters enabled by default
Tags do not exceed configured cardinality thresholds
Metrics cover latency, success rate, retries, cost

📬 Execution Events¶

Emitted for major transitions (BlueprintParsed, AgentExecuted, TraceCompleted)
Events include full trace metadata
Events feed Studio, audit log, and anomaly detectors

🔒 Security & Compliance¶

Sensitive fields declared in blueprint
Redaction policies enforced across logs and spans
Exported traces can be redacted or scoped per tenant

📊 Studio & Dashboards¶

Dashboards exist per module, tenant, agent
Trace explorer shows full execution lifecycle
Alerts and anomaly detection are tied to observability signals

💥 Failure & Resilience¶

Span errors trigger retries, fallbacks, or alerts
Health degradation visible in real-time via observability
CI/CD gates prevent unobservable or high-risk deployments

💰 Cost¶

Token usage, execution time, and resource cost included in spans
Cost constraints declared and enforced per blueprint

🎯 Final Takeaway¶

Observability in ConnectSoft is:

Declarative — defined in blueprints
Automated — emitted by templates, agents, and infrastructure
Auditable — structured, secure, and traceable
Actionable — drives decisions, alerts, self-healing, and AI feedback
Unified — powering Studio, pipelines, dashboards, and compliance exports

It’s not just “how we see the system.” It is the system.

📡 Observability-Driven Design¶

🔍 Why Observability Is Foundational in a Software Factory¶

🧠 What Makes Observability First-Class¶

🔁 Observability Enables the Factory Lifecycle¶

🔧 Observability vs Monitoring¶

🔐 In a Secure Factory, Observability Also Enables:¶

📊 Studio’s Observability Dependency¶

✅ Summary¶

🧠 Traceable Agent Execution¶

🧬 Core Identifiers for Agent Traceability¶

📘 Example Execution Metadata¶

📊 Why This Structure Matters¶

🔄 End-to-End Trace Flow¶

🔍 In Telemetry Streams¶

📊 Studio Agent Trace View¶

✅ Summary¶

📄 Structured Logging Strategy¶

🧩 What Is Structured Logging?¶

🧠 Logging Fields Required in ConnectSoft¶

🔒 Secure Logging: Redaction and Sensitivity¶

🧪 Logging Anti-Patterns Prevented¶

🧠 Logging Levels Guidance¶

📊 Studio Log Explorer Features¶

✅ Summary¶

📈 Metrics for Agents, Services, and Modules¶

🧩 Metric Categories¶

📘 Example: Agent Metrics Output (Prometheus format)¶

🧠 Metric Dimensions (Standard Tags)¶

🧪 Metric-Based Test Assertions¶

🤖 Metric-Emitting Agents¶

📊 Studio Metric Explorer¶

🔐 Security & Cost Metrics¶

✅ Summary¶

🔀 Distributed Tracing with OpenTelemetry¶

📐 What Is a Trace?¶

📦 Span Metadata Structure¶

🧩 Span Types in ConnectSoft¶

🔗 Span Relationships¶

🔍 Where Traces Are Emitted¶

🧪 Trace-Based Test & Failure Analysis¶

📊 Studio Trace Explorer¶

🧠 Use Cases Unlocked by Distributed Tracing¶

✅ Summary¶

🧭 Execution Events and Factory State Transitions¶

📬 What Is an Execution Event?¶

🧾 Standard Factory Execution Events¶

📘 Example Event: AgentExecuted¶

🧩 Events vs Logs vs Spans¶

🔄 Event-Driven Factory Flow (Example)¶

🔧 Event Emitters¶

📊 Studio Execution Timeline¶

✅ Summary¶

📜 Blueprint-Aware Observability Contracts¶

📘 Example: Observability Contract Block in Blueprint¶

🧠 Benefits of Blueprint-Level Observability Contracts¶

🧩 Contract Elements Supported¶

🤖 Agent Responsibilities¶

🛠️ Contract Validation & Enforcement¶

📊 Studio Integration¶

✅ Summary¶

🏷️ Span Enrichment and Custom Dimensions¶

🧠 Why Span Enrichment Matters¶

📘 Example: Enriched Span JSON¶

📦 Standard Span Tags in ConnectSoft¶

🛠️ Agent/Template Responsibilities¶

🧪 Validation and Enforcement¶

📊 Studio Usage of Enriched Spans¶

✅ Summary¶

🧪 Testing via Observability¶

🧩 Observability-Backed Assertions¶

📘 Test Case Example: Security Redaction via Logs¶

🔍 Span-Based Test Example¶

🔧 How Tests Are Emitted¶

🧪 Types of Observability-Powered Tests¶

📊 Studio Test Integration¶

✅ CI/CD Pipeline Integration¶

✅ Summary¶

🤖 Prompt Validation and AI Feedback via Observability¶

🧠 Why Prompt Observability Matters¶

📘 Span with Prompt Metadata (Example)¶

📘 Example Event: `AgentExecuted`¶