AI Automation Platform

Overview

An AI automation platform that turns complex business workflows into reliable, agent-driven pipelines. The system combines LangGraph-based orchestration, retrieval-augmented generation over a private knowledge base, and tool-using agents that can read, reason about, and act on enterprise data.

Under the hood, the platform runs Python services on AWS, with model inference routed through Bedrock and OpenAI, embeddings stored in a pgvector-backed PostgreSQL instance, and stateful graph execution handled by LangGraph. Each agent is a node in a directed graph with explicit tools, guardrails, structured output schemas, and persisted memory, so workflows are observable, replayable, and recover cleanly from failures.

The stack is designed for production: prompt versioning, evaluation harnesses, token and cost tracking, retries with idempotency, and audit logs for every agent decision. The result is automation that behaves predictably enough to put in front of real customers, while still leveraging the latest in LLMs, RAG, and agentic patterns.

My Contribution

• Designed the LangGraph-based agent orchestration layer with branching, retries, and persisted graph state.
• Built the RAG pipeline: ingestion, chunking, embeddings, pgvector retrieval, and reranking before generation.
• Implemented tool-using agents with structured output schemas, guardrails, and prompt versioning.
• Architected AWS infrastructure for inference, vector storage, async workflows, and event-driven processing.
• Added evaluation harnesses, token/cost tracking, and observability across agents and RAG steps.