Agentic Software Development Research

Systematic research into AI-driven software development workflows with feedback loops.

Research Topics

1. Agentic Loops & Orchestration

• Planning → Execution → Validation loops
• Nested agent hierarchies (orchestrator → workers)
• State machines for agent workflows
• Error recovery and retry patterns
• Human-in-the-loop integration points

2. Feedback & Reflection

• Self-reflection mechanisms (capability-evolver pattern)
• Runtime learning from errors
• Performance feedback integration
• Memory/knowledge persistence across sessions
• Continuous improvement cycles

3. Code Review Automation

• Automated PR review agents
• Static analysis integration
• Security scanning in CI
• Style guide enforcement
• Architecture review automation

4. Testing & Quality

• Automated test generation
• Property-based testing with AI
• Visual regression testing
• Performance testing automation
• Test coverage analysis

5. Deployment & Operations

• CI/CD pipeline automation
• Canary deployment with AI monitoring
• Rollback automation
• Infrastructure as Code validation
• Post-deployment validation

6. Linting & Code Quality

• Automated code formatting
• Type safety enforcement
• Dead code detection
• Dependency analysis
• Technical debt quantification

7. SDLC Orchestration

• Requirements → Code → Deploy pipeline
• Multi-agent coordination
• Context management across phases
• Audit trails and observability
• Cost optimization for AI usage

Research Methodology

For each topic:

1. Literature Review — Papers, blogs, GitHub repos
2. Tool Analysis — Existing implementations
3. Pattern Extraction — Reusable components
4. Gap Analysis — What's missing
5. Implementation Notes — How to build it

Output

Research findings organized as:

• /topics/ — Deep dives per topic
• /sources/ — Annotated bibliography
• /patterns/ — Reusable design patterns
• /impl/ — Implementation guides