# Autonomous Development Plan Example

Inspired by [AI-First DevOps](https://technologyworkroom.blogspot.com/2025/06/building-ai-first-devops.html), this example demonstrates how AI can create comprehensive development plans for new features or projects.

## Files
- `input.json` - The prompt requesting an autonomous development plan
- `schema.json` - Structured schema for development planning
- `README.md` - This documentation

## Example:

When you use this example, here is what happens step by step:

**Input:**
```json
{
    "messages": [
        {
            "role": "system",
            "content": 
              "You are an expert AI-first development architect."
              "Create comprehensive development plans that embody the principles "
              "of autonomous development, vibe coding, and AI-first DevOps. "
              "Focus on natural language to implementation workflows, "
              "quality gates, and exponential productivity gains."
        },
        {
            "role": "user",
            "content": "Create a comprehensive development plan for building "
                        "an AI-first DevOps pipeline that automatically generates PR descriptions, "
                        "performs security analysis, and creates changelogs. "
                        "The system should integrate with GitHub Actions, "
                        "use LLM-as-judge for quality validation, and support "
                        "multiple AI models. Include architecture decisions, "
                        "implementation tasks, testing strategy, and risk assessment. "
                        "Focus on the AI-first principles of autonomous operation and "
                        "exponential productivity gains."
        }
    ],
    "context": {
        "session_id": "ai-first-devops-plan-001",
        "metadata": {
            "task_type": "autonomous_development_planning",
            "domain": "ai_first_devops",
            "complexity": "high",
            "inspiration": "Building AI-First DevOps blog article"
        }
    }
}
```

**Schema:** The system enforces a JSON schema that defines the development plan structure:
- `project_overview`: Object with title, description, ai_first_principles, and expected_productivity_gain
- `architecture`: Object with system_design, components array, and data_flow
- `implementation_plan`: Object with phases array, each containing phase, duration_weeks, description, deliverables, and ai_tasks
- `quality_gates`: Object with automated_validation array and ai_quality_metrics array
- `risk_assessment`: Object with risks array and overall_risk_score
- `success_metrics`: Object with productivity_metrics, quality_metrics, and ai_efficiency_metrics arrays

**Command:**
```bash
llm-ci-runner \
  --input-file examples/04-ai-first/autonomous-development-plan/input.json \
  --output-file development-plan.json \
  --schema-file examples/04-ai-first/autonomous-development-plan/schema.json \
  --log-level INFO
```

**Output:**
```json
{
  "success": true,
  "response": {
    "project_overview": {
      "title": "AI-First DevOps Pipeline",
      "description": "A fully autonomous DevOps pipeline leveraging AI to generate PR descriptions, perform security analysis, and create changelogs, integrated with GitHub Actions and multiple AI models.",
      "ai_first_principles": [
        "autonomous operation",
        "exponential productivity gains",
        "natural language to implementation workflows"
      ],
      "expected_productivity_gain": "exponential"
    },
    "architecture": {
      "system_design": "The system is designed as an AI-powered orchestration layer that integrates with GitHub Actions. It employs multiple AI models including LLMs for natural language tasks and other specialized AI models for security analysis and code quality assessment. Data flows from GitHub triggers, through AI modules for analysis and generation, culminating in automated PR descriptions, security reports, and changelogs.",
      "components": [
        {
          "name": "PR Description Generator",
          "purpose": "Automatically generate comprehensive PR descriptions from code diffs and commit messages.",
          "ai_integration": "Uses LLMs fine-tuned for summarization and natural language generation.",
          "autonomy_level": "fully_autonomous"
        },
        {
          "name": "Security Analysis Module",
          "purpose": "Perform static and dynamic security analysis on code changes.",
          "ai_integration": "Employs specialized security AI models for vulnerability detection.",
          "autonomy_level": "fully_autonomous"
        },
        {
          "name": "Changelog Generator",
          "purpose": "Create human-readable changelogs reflecting recent changes.",
          "ai_integration": "Utilizes LLMs for natural language summarization of commits and features.",
          "autonomy_level": "fully_autonomous"
        }
      ],
      "data_flow": "GitHub triggers a workflow, which invokes the AI modules for analysis and generation. Results are compiled and used to create PR descriptions, security reports, and changelogs, which are then automatically added to the PR. Feedback can be looped for continuous improvement."
    },
    "implementation_plan": {
      "phases": [
        {
          "phase": "foundation",
          "duration_weeks": 4,
          "description": "Establish core AI integration with GitHub Actions, set up version control, and define data schemas.",
          "deliverables": [
            "Initial AI integration tests",
            "Basic GitHub Action workflows",
            "Schema definitions for code, PRs, and comments"
          ],
          "ai_tasks": [
            "Set up API connections to AI models",
            "Design data schemas for code and comments"
          ]
        },
        {
          "phase": "core_features",
          "duration_weeks": 6,
          "description": "Implement PR description generator, security analysis module, and changelog generator.",
          "deliverables": [
            "Automated PR description system",
            "Security analysis reports",
            "Automated changelog creation"
          ],
          "ai_tasks": [
            "Fine-tune LLMs for summarization",
            "Integrate security AI models",
            "Develop text aggregation and formatting"
          ]
        },
        {
          "phase": "integration",
          "duration_weeks": 4,
          "description": "Integrate components into a cohesive pipeline, test end-to-end flow.",
          "deliverables": [
            "Integrated DevOps pipeline with AI modules",
            "Test reports and logs"
          ],
          "ai_tasks": [
            "Optimize data flow",
            "Implement error handling and fallback"
          ]
        },
        {
          "phase": "optimization",
          "duration_weeks": 3,
          "description": "Enhance AI models for accuracy and speed, implement feedback loops.",
          "deliverables": [
            "Improved models and workflows",
            "Dashboard for monitoring AI performance"
          ],
          "ai_tasks": [
            "Active learning and model retraining",
            "Performance tuning"
          ]
        },
        {
          "phase": "deployment",
          "duration_weeks": 2,
          "description": "Deploy to production, monitor, and set up maintenance routines.",
          "deliverables": [
            "Production-ready AI DevOps pipeline",
            "Monitoring dashboards and alerts"
          ],
          "ai_tasks": [
            "Set up continuous learning pipelines"
          ]
        }
      ],
      "critical_path": [
        "AI model fine-tuning",
        "GitHub Actions integration",
        "Security AI deployment"
      ]
    },
    "quality_gates": {
      "automated_validation": [
        {
          "gate_name": "Schema Compliance",
          "validation_type": "schema_compliance",
          "threshold": "",
          "automation_level": "fully_automated"
        },
        {
          "gate_name": "LLM as Judge",
          "validation_type": "llm_as_judge",
          "threshold": "",
          "automation_level": "fully_automated"
        },
        {
          "gate_name": "Security Scan",
          "validation_type": "security_scan",
          "threshold": "",
          "automation_level": "fully_automated"
        },
        {
          "gate_name": "Performance Test",
          "validation_type": "performance_test",
          "threshold": "",
          "automation_level": "semi_automated"
        }
      ],
      "ai_quality_metrics": [
        "Accuracy of PR descriptions",
        "Vulnerability detection rate",
        "Changelog completeness"
      ]
    },
    "risk_assessment": {
      "risks": [
        {
          "risk": "AI models produce inaccurate or biased outputs",
          "probability": "medium",
          "impact": "high",
          "mitigation": "Implement multi-model validation, human review gates during rollout.",
          "ai_mitigation": "Apply fairness and bias detection, continual retraining."
        },
        {
          "risk": "Security vulnerabilities from AI-generated code analysis errors",
          "probability": "medium",
          "impact": "high",
          "mitigation": "Regular updates of security AI models, manual verification for critical components.",
          "ai_mitigation": "Use ensemble models, cross-validation techniques."
        },
        {
          "risk": "Integration failures with existing CI/CD pipelines",
          "probability": "low",
          "impact": "medium",
          "mitigation": "Phased rollout, extensive testing and fallback procedures.",
          "ai_mitigation": "Feature flagging, redundancies."
        },
        {
          "risk": "Data privacy issues when processing code and comments",
          "probability": "low",
          "impact": "medium",
          "mitigation": "Ensure data encryption in transit and at rest, comply with data governance policies.",
          "ai_mitigation": "Limit data access, anonymize sensitive info."
        }
      ],
      "overall_risk_score": 7.5
    },
    "success_metrics": {
      "productivity_metrics": [
        "PR merge cycle time reduction",
        "Automation coverage percentage",
        "Number of automated tasks"
      ],
      "quality_metrics": [
        "Accuracy of PR descriptions (user feedback)",
        "Vulnerability detection rate",
        "Changelog accuracy and completeness"
      ],
      "ai_efficiency_metrics": [
        "Model inference time",
        "Error rate of AI outputs",
        "Retraining frequency"
      ]
    }
  },
  "metadata": {
    "runner": "llm-ci-runner",
    "timestamp": "auto-generated"
  }
}
```

## What This Demonstrates
- **AI-First Development**: AI creates comprehensive development plans
- **Vibe Coding**: Natural language to structured development workflow
- **Autonomous Planning**: AI determines architecture, tasks, and implementation strategy
- **Quality Gates**: Built-in validation and testing requirements
- **Risk Assessment**: AI identifies potential issues and mitigation strategies

## AI-First DevOps Concepts
This example embodies the principles from the AI-First DevOps article:
- **Natural Language to Code**: Describe what you want, AI plans the implementation
- **Autonomous Development**: AI handles the planning, you focus on high-level direction
- **Quality Assurance**: Built-in testing and validation requirements
- **Risk Management**: Proactive identification of potential issues
- **Exponential Productivity**: AI amplifies human development capabilities
