Building Failure Intelligence for AI Agents

Source: Dev.to

Problem Statement

When you run AI agents in production, you quickly realize that dangerous failures aren’t random.

Examples of recurring failures

• Similar hallucination structures
• Repeated tool‑call mistakes
• Prompt injection variants
• Context leakage patterns

Most tools give you logs, but they don’t turn those logs into actionable intelligence.

Proposed Model

• Canonical failure entities: Every failure is recorded as a distinct entity.
• Deterministic fingerprint: A fingerprint is generated for each execution.
• Historical matching: New executions are matched against the database of past failures.
• Policy engine: Maps confidence levels to actions (allow / warn / block).

Key Idea

Do not modify the LLM itself. Instead, convert failure history into enforcement intelligence that can be applied at runtime.

Current Work

The approach is still early, but a prototype is available here:
https://github.com/prateekdevisingh/kakveda

Discussion

How are others handling repeat failure patterns in agent‑based systems?

---

Tags: opensource, llm, agents, devops, aigovernance