Comprehensive Test-to-Production Harness for Agentic Systems

1. Architecture Overview

The most elegant solution for agentic systems combines GitOps principles with observable pipelines, utilizing staged environments with progressive validation. This approach enables both velocity and safety.

graph LR
    A[Develop] --> B[Validate]
    B --> C[Stage]
    C --> D[Confirm]
    D --> E[Deploy]
    
    A --> A1[Unit Tests\nLinting]
    B --> B1[Integration\nTests]
    C --> C1[Performance\nTests]
    D --> D1[Canary\nTests]
    E --> E1[Monitoring\n& Alerts]

2. Logging Architecture for Agentic Visibility

The logging architecture is critical for agentic systems. A properly designed logging system should provide:

2.1 Structured Event Logging

{
  "timestamp": "2025-05-07T14:30:45.123Z",
  "level": "INFO",
  "agent_id": "cursor-agent-42",
  "event_type": "reasoning_step",
  "correlation_id": "task-xyz-789",
  "parent_event_id": "decision-123",
  "context": {
    "environment": "staging",
    "model_version": "3.7",
    "input_hash": "sha256:abc123..."
  },
  "message": "Evaluating test case outcomes",
  "data": {
    "test_passed": 42,
    "test_failed": 1,
    "code_coverage": 0.87
  }
}

2.2 Context-Preserving Log Chain

Each agent action generates a correlation ID that threads through all subsequent events, creating a traceable decision path. This is crucial for forensic analysis and understanding agentic decision-making.

3. Test-Driven Development Workflow

For agentic systems, TDD must be adapted to handle both deterministic and stochastic outcomes:

1. Define expected behavior boundaries (not just point values)
2. Write tests that validate whether outputs fall within acceptable bounds
3. Implement agent functionality
4. Verify behavior matches expectations
5. Refine agent prompt/system message/fine-tuning

4. Pipeline Stages with Validation Gates

4.1 Development Stage

Unit Tests: Validate atomic behaviors using mock inputs
Static Analysis: Code quality, security scanning
Agent Prompt Tests: Validate that agent system messages produce expected behaviors

4.2 Validation Stage

Integration Tests: Verify component interactions
Property-Based Tests: Ensure behavior holds across a range of inputs
Jailbreak Attempts: Validate system message robustness

4.3 Staging Environment

Performance Tests: Measure latency, throughput, and resource utilization
Regression Tests: Ensure new changes don't break existing functionality
A/B Testing: Compare agent versions on key metrics

4.4 Confirmation Gate

Canary Deployment: Roll out to limited subset of traffic
Shadowing: Run new agent alongside existing agent and compare outputs
Human-in-the-loop Validation: Critical review of agent outputs

4.5 Production Deployment

Blue/Green Deployment: Zero-downtime cutover strategy
Monitoring: Real-time telemetry with anomaly detection
Circuit Breakers: Automatic rollback triggers

5. Cursor MCP Agent Integration

Cursor's Multi-agent Collaboration Protocol requires special consideration:

agent_configuration:
  team_structure:
    - role: "architect"
      responsibility: "System design and verification"
    - role: "implementer"
      responsibility: "Code generation from specifications"
    - role: "tester"
      responsibility: "Test generation and validation"
    
  workflow:
    - phase: "specification"
      gate_requirements: "All test cases defined and approved"
    - phase: "implementation"
      gate_requirements: "Code passes linting and basic unit tests"
    - phase: "verification"
      gate_requirements: "All tests pass, coverage >= 90%"

6. Forensic Log Analysis Capabilities

To support effortless pipeline visibility:

6.1 Event Correlation Engine

graph LR
    A[Raw Logs] --> B[Correlation Engine]
    B --> C[Event Store]
    C --> D[Aggregation]
    D --> E[Query API]
    E --> F[Visualization]

6.2 Agent Decision Tracing

Every agent decision is traced with:

Input state
Reasoning steps (including alternatives considered)
Output action
Expected outcomes
Actual outcomes (after execution)

6.3 Debugging Tools

Time-travel debugging for agent decisions
Counterfactual analysis ("what if" scenarios)
Prompt injection detection
Silent failures detection (when agents quietly produce suboptimal results)

7. Implementation Recommendations

Infrastructure as Code: Define all pipeline stages using GitOps principles
Distributed Tracing: Implement OpenTelemetry for comprehensive observability
Feature Flagging: Control agent capabilities via feature flags
Golden Signals Monitoring: Track latency, traffic, errors, and saturation
Anomaly Detection: Machine learning for unusual agent behavior patterns

8. Metrics Framework

Key metrics to track:

Agent Decision Quality: Measured against ground truth where available
Pipeline Velocity: Time from commit to production
Test Coverage: Both code and prompt/behavior coverage
Agent Self-Consistency: Variance in outputs for similar inputs
Rollback Rate: Frequency of deployment reversions

9. Advanced Logging Architecture

flowchart TD
    A[Agent Actions] --> B[Structured Logger]
    B --> C[Log Transport]
    
    C --> D[Real-time Analysis]
    C --> E[Storage]
    
    D --> F[Alerting]
    D --> G[Dashboards]
    
    E --> H[OLAP Store]
    
    H --> I[Forensic Analysis]
    H --> J[ML Training]
    
    K[Human Feedback] --> L[Ground Truth DB]
    L --> I
    L --> J
    
    subgraph "Observability Platform"
    F
    G
    I
    end

This comprehensive approach ensures that agentic systems maintain high quality while preserving development velocity, with logs that enable deep visibility into agent states and decisions throughout the pipeline.

SoMaCoSF/Agentic_CI.md