Engineer Shares Architecture for Building Scalable Multi-Agent AI Pipelines

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AI engineer Mithilesh Kumar has published a technical guide on building multi-agent systems and Retrieval-Augmented Generation (RAG) pipelines using LangGraph and FastAPI. The architecture moves beyond traditional linear LLM applications by modeling agent interactions as state machines, enabling cyclic execution loops, state management, and human-in-the-loop oversight. The proposed stack combines LangGraph for orchestration, FastAPI for async backend handling, and vector databases with hybrid search for low-latency context retrieval. Foundation models from OpenAI and Google Gemini serve as the underlying AI layer. Key engineering recommendations include deterministic routing to prevent agent loops, Pydantic-enforced structured outputs, and observability tooling such as LangSmith for tracking token usage and execution steps.

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