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title: "Agentic Development Research - Master Index" description: "Comprehensive research corpus for designing observable, reliable agentic SDLC orchestration" date: 2026-02-06 version: 1.0.0 topics: 6 total_pages: ~50 status: ready_for_processing

Agentic Development Research - Master Index

Quick Navigation

Research Goals

Design an observable, reliable orchestration system for AI-driven software development that:

1. Minimizes human input to requirements and high-level decisions
2. Maximizes automation across planning → implementation → deployment
3. Maintains observability through comprehensive telemetry
4. Ensures reliability via safety controls and feedback loops
5. Enables continuous improvement through reflection and learning

Key Patterns Identified

Orchestration Patterns

• Agent as Orchestrator: Central coordinator delegates to workers
• Nested Hierarchies: Multi-level agent structures
• Shared Context Bus: Common state and event stream
• Message Passing: Async communication between agents

Feedback Patterns

• Post-Execution Reflection: Analyze outcomes and extract lessons
• In-Loop Reflection: Pause and reassess during execution
• Meta-Reflection: Reflect on the reflection process itself
• GEP Protocol: Structured evolution with genes and capsules

Quality Patterns

• Hybrid Review: Rule-based checks + AI review
• Risk-Based Depth: Adjust review thoroughness by risk
• Adaptive Testing: Select and prioritize tests dynamically
• Canary Validation: Gradual rollout with automated monitoring

Reliability Patterns

• Circuit Breakers: Prevent infinite loops and runaway costs
• Checkpointing: Save state for recovery and audit
• Graceful Degradation: Fallback strategies when agents fail
• Human Gates: Required approval for high-stakes decisions

Architecture Overview

┌─────────────────────────────────────────────────────────┐
│                    SDLC Orchestrator                     │
│  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐       │
│  │ Planner │ │Implement│ │ Reviewer│ │ Tester  │       │
│  └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘       │
│       │           │           │           │             │
│       └───────────┴─────┬─────┴───────────┘             │
│                         │                               │
│                         ▼                               │
│              ┌─────────────────┐                        │
│              │   Shared Context │                       │
│              │   Bus / Events   │                       │
│              └─────────────────┘                        │
└─────────────────────────────────────────────────────────┘
                         │
         ┌───────────────┼───────────────┐
         ▼               ▼               ▼
    ┌─────────┐    ┌─────────┐    ┌─────────┐
    │Telemetry│    │  Audit  │    │ Feedback│
    │ Metrics │    │  Trail  │    │  Loop   │
    └─────────┘    └─────────┘    └─────────┘

Implementation Roadmap

Phase 1: Foundation (Weeks 1-2)

• Set up shared context infrastructure
• Define agent interfaces and protocols
• Implement basic orchestrator
• Create telemetry collection system

Phase 2: Core Agents (Weeks 3-4)

• Planning agent with requirements analysis
• Implementation agent with code generation
• Review agent with quality gates
• Integration testing between agents

Phase 3: Quality & Deployment (Weeks 5-6)

• Testing agent with smart selection
• CI/CD integration
• Deployment automation
• Monitoring and feedback loops

Phase 4: Reliability (Weeks 7-8)

• Safety controls and circuit breakers
• Checkpoint and recovery system
• Human-in-the-loop integration
• Comprehensive testing

Metrics to Track

Efficiency Metrics

• Lead time (requirement → production)
• Human hours per feature
• Agent utilization rate
• Cost per deployment

Quality Metrics

• Defect escape rate
• Test coverage
• Code review turnaround
• Production incident rate

System Metrics

• Agent error rate
• Feedback loop effectiveness
• Learning rate (improvement over time)
• Human satisfaction scores

Open Questions for Processing

When processing this research to design the orchestration system, consider:

1. Granularity: How many agents? Specialized vs. general-purpose?
2. Communication: Synchronous or async? Shared memory or message passing?
3. Safety: Where to place human gates? What auto-rollback triggers?
4. Learning: How to extract and apply patterns from successful runs?
5. Cost: How to balance AI usage vs. human time savings?
6. Observability: What telemetry is essential vs. nice-to-have?

Usage Instructions

For System Design

1. Read topics 01-03 for core patterns
2. Read topics 04-05 for quality and deployment
3. Read topic 06 for integration guidance
4. Use patterns section as a checklist

For Implementation

1. Start with telemetry infrastructure (observability first)
2. Implement one agent at a time
3. Add safety controls before production use
4. Iterate based on metrics

For Research Extension

1. Each topic has a "Research Gaps" section
2. Add findings to the sources list
3. Update patterns based on new learnings
4. Version the corpus

File Structure

agentic-dev-research/
├── README.md              # This file
├── OPENCODE-PLAN.md       # OpenCode execution strategy
├── topics/
│   ├── 01-agentic-loops.md
│   ├── 02-feedback-reflection.md
│   ├── 03-code-review.md
│   ├── 04-testing-qa.md
│   ├── 05-cicd-deployment.md
│   └── 06-sdlc-orchestration.md
├── sources/               # Bibliography (to be populated)
├── patterns/              # Extracted reusable patterns
└── impl/                  # Implementation guides

Next Actions

1. Process this corpus with an AI agent to extract:

   • System architecture recommendations
   • Implementation priorities
   • Risk assessment
   • Cost estimates

2. Validate findings against:

   • Existing tools (Claude Code, GitHub Copilot, etc.)
   • Academic research
   • Industry case studies

3. Prototype minimal viable orchestrator:

   • Single end-to-end workflow
   • Basic observability
   • Safety controls

4. Measure against baseline:

   • Developer productivity
   • Code quality
   • System reliability

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This research corpus is ready for agentic processing. Each topic contains structured findings with code examples, patterns, and gaps that can be synthesized into a comprehensive orchestration system design.