Resilient Agentic Workflow: Enterprise-Grade AI Agent System with Fault Tolerance and Quantitative Evaluation

This project introduces Resilient Agentic Workflow, an enterprise-grade AI agent system built with LangGraph and Supabase. It addresses the fragility of standard LLM scripts in complex multi-step tasks by enabling state persistence (breakpoint resumption) and provides quantitative evaluation via DeepEval. Key features include fault tolerance, audit tracking, and objective quality assessment, making it suitable for long-running enterprise tasks.

In production environments, standard LLM scripts often lose all progress when facing API failures, network issues, or process crashes—critical problems for long-running enterprise tasks. Resilient Agentic Workflow is designed to solve this by supporting state checkpointing and quantitative execution quality evaluation, ensuring task continuity even after failures.

State Persistence: Unlike stateless LLM scripts, the system saves state to Supabase after each node execution, enabling breakpoint resumption, fault tolerance, observability, and audit tracking. Quantitative Evaluation: Uses DeepEval framework for LLM-as-a-judge assessment, measuring faithfulness (consistency with context) and relevance (alignment with user needs) to avoid subjective judgments.

Workflow Example: A 4-step competitive analysis task: 1) Competitor research via Grok API; 2) Forced structured JSON output for price extraction;3) Deterministic Python node for database comparison;4) Strategy drafting based on difference analysis. Tech Stack: LangGraph (orchestration), Grok Cloud API (LLM), Supabase (state storage), DeepEval (evaluation), FastAPI+Render (deployment).

Elasticity Demo: Simulates crash after step 2; agent restarts, retrieves state from Supabase, skips completed steps, and continues from breakpoint. Evaluation: Uses DeepEval with commands like deepeval test run tests/test_agent.py to measure faithfulness (anti-hallucination) and relevance, enabling objective quality tracking.

Use Cases: Competitive intelligence analysis, market research report generation, data pipeline processing, automated document generation. Deployment: Clone repo → install dependencies → configure .env → start service with uvicorn app.main:app --reload.

This project demonstrates critical elements for production AI agents: reliability (state persistence), observability (logs/evaluation), quantitative improvement (metrics-driven), and clear architecture (LangGraph state machine). It serves as a reference for teams looking to deploy robust enterprise AI systems.