AVX-CS-014 · Generative AI · AI Agents
Multi-Agent RAG Development System
A LangGraph multi-agent pipeline that automates code generation, testing, debugging, and regeneration through a decision-based workflow.
role-specialized pipeline
Distinct agents own generation, testing, debugging, and regeneration within one coordinated workflow.
error-driven regeneration
Execution errors route back through solution and regeneration agents until the code runs correctly.
decision-based control
A LangGraph decision graph governs what each agent does next based on real execution results.
Executive Summary
What we built, and why it matters.
Avrixo built a LangGraph-powered multi-agent system, using LangChain and FastAPI, that automates the software development cycle. Specialized agents handle code generation, testing, debugging, and regeneration, coordinated through a decision-based workflow.
The result is a self-improving pipeline: when execution surfaces errors, the system routes back through solution and regeneration agents until the code works, then finalizes a clean output.
The Core Problem
The bottleneck beneath the business goal.
Turning a problem statement into working, tested code is iterative and slow. A single-shot model output rarely runs correctly, and manual debugging loops eat time.
Automating that loop needs more than one model call: it needs agents with distinct roles and a controller that decides what happens next based on real execution results.
The Technical Solution
Engineered for accuracy, scale, and trust.
Avrixo designed a graph of agents in LangGraph: a code generator produces a solution, a compiler agent runs static and dynamic analysis plus tests, and a decision node routes failures to solution and regeneration agents that rework the code automatically.
When execution passes, a finalization agent assembles the output and logs; the whole pipeline is exposed through FastAPI, with LangChain orchestrating the LLM steps and prompt engineering shaping each agent's behavior.
Architecture Notes
System decisions that made the product viable.
LangGraph decision graph coordinating specialized agents.
Compiler agent with static/dynamic analysis and test execution.
Error-driven routing to solution and regeneration agents.
FastAPI-exposed pipeline with finalized output and logs.
Tech Stack Matrix
Infrastructure behind the outcome.
Agent Orchestration
LangGraph · LangChain · Decision Routing
AI
LLM Prompt Engineering · Generative AI
Application & Ops
FastAPI · MLOps
Start a Conversation
Need a system built like this?
Fixed scope, clear milestones, and a team that ships production systems end to end.