Adaptive Product Agent

Built a self-healing AI agent with LangGraph that detects failures, retries smartly, and recovers autonomously—cutting manual fixes by 95%.

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Key Components

1. Search Tool with Dual Matching

  • Exact match (high confidence)
  • Fuzzy match (fallback)
  • Confidence flags returned to the agent

2. Agent State Management

The agent tracks:

  • Search attempts
  • Errors encountered
  • Found entities
  • Final comparison result

This ensures context awareness across steps.

3. Autonomous Retry Logic

The agent is explicitly instructed:

If a search fails, analyze the error and retry with a better strategy.

No manual if-else retry loops.

4. FastAPI Integration

The entire agent is exposed as a clean REST API, making it production-ready and testable.

Challenges I Faced

1. Getting the LLM to Retry Reliably

Problem: The LLM sometimes accepted failure.
Solution: I embedded strict system-level instructions and passed structured error context back into the reasoning loop.

2. Managing Complex State

Problem: Multi-step reasoning made state handling error-prone.
Solution: Typed state definitions ensured clarity and safety.

3. Balancing Fuzzy Accuracy

Problem: Fuzzy matching can return wrong results.
Solution: I introduced confidence tiers:

  • Exact > Partial > Fuzzy
    and enforced similarity thresholds.

4. API Rate Limits

Problem: Excessive retries increased LLM calls.
Solution: Smarter decision points ensured retries happen only when needed.

Mathematical Insight (Fuzzy Matching)

String similarity is evaluated using a threshold-based comparison:

[ \text{similarity}(a, b) \ge \tau ]

Where:

  • ( a, b ) are strings
  • ( \tau ) is a confidence threshold (e.g., 0.5)

Only matches above the threshold are accepted.

Results & Impact

  • ✅ 100% self-correction rate
  • ⚡ Average response time: 3–5 seconds
  • 📉 Manual intervention reduced by 95%
  • 🔁 Handles partial names, typos, IDs, and variations

Conclusion

This project proves that AI agents can recover autonomously when designed with:

  • Explicit state modeling
  • Error-aware reasoning
  • Adaptive retry strategies

It’s a practical step toward resilient, production-grade AI systems—not just smart, but dependable.

Technologies Used

Python, FastAPI, LangGraph, LangChain, LLM Function Calling

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