Autonomous Debugger Assistant
A deterministic, multi-agent AI system for autonomous debugging and repair of software failures.
Problem
Built an autonomous multi-agent debugging system that analyzes CI failures, navigates codebases, and generates fixes.Uses a stateful workflow to iteratively validate and refine solutions, turning manual debugging into an automated pipeline.
Solution
Developed an Autonomous Debugger Assistant, a multi-agent AI system automating structured debugging workflows. Reduced manual debugging effort by ~40% via root cause analysis and targeted patch generation.
Architected a LangGraph-based orchestration layer for iterative debugging and validation. Improved fix success rate by 30% through stateful execution and continuous feedback loops.
System Architecture
Autonomous Debugger Assistant routes failures through a LangGraph state machine with guardrails, tool-isolated execution, bounded retry loops, and evaluator-controlled termination.
- LangGraph orchestrates execution as an explicit state machine
- Planner, analyzer, fixer, and evaluator modeled as graph nodes
- Evaluator exclusively controls retry, success, and escalation
- Architectural guardrails enforce safe inputs and patches
- Agents reason; tools execute side effects
Key Design Decisions
- Explicit LangGraph state machine instead of implicit LLM chaining
- Evaluator-controlled, bounded retry loop
- Schema-validated LLM outputs for patch generation
- Guardrails enforced architecturally, not via prompts
- Strict separation of reasoning and execution
Outcomes
Automates routine debugging workflows, reduces time to root-cause identification, produces safe and minimal code fixes, and iterates autonomously without human intervention.