Debugging AI Agents: How to Record LLM Traces and Build Pytest Regression Tests

When a traditional deterministic Python function fails in production, reproducing the issue is usually straightforward: you isolate the function, supply the identical inputs, step through with a debugger, and fix the underlying logic.

With complex LLM-powered AI agents, debugging becomes significantly more tricky.

An agent orchestrates multiple steps: calling large language models, fetching contextual documents, executing custom tools, and making decisions based on intermediate state. When an agent returns an incorrect final answer, re-running the application rarely produces the exact same sequence of events. The model response may vary due to temperature or provider-side updates, external APIs might return fresh data, and executing tools repeatedly can introduce unwanted state side effects.

What if you could capture an AI agent failure once and turn it into a deterministic, offline regression test?

In this technical breakdown, we explore how application-level bugs can silent-fail even when your underlying LLM responds correctly, and how open-source trace recording tools like Stepfork enable developers to record, replay, and auto-generate pytest test suites.

The Silent Failure Paradox: Right Model, Wrong Application Logic

A common misconception in AI software engineering is assuming that agent errors originate from model hallucination or poor prompt instruction. In practice, substantial failure modes occur in the post-processing layer—the glue code that parses, transforms, or correlates LLM outputs with domain business logic.

Consider an automated IT Incident-Triage Agent designed to process infrastructure alerts, check system health endpoints, inspect deployment histories, and calculate an incident severity level (e.g., P0, P1, P2).

+------------------+     +-----------------------+     +----------------------------+
|  Alert Payload   | --> | AI Agent (LLM Call)   | --> | Post-Processing Correlation| --> Final Result
| (HTTP 500 Spike) |     | Evaluates health & run|     | App Logic / Severity Rules |
+------------------+     +-----------------------+     +----------------------------+

When building production agents with high-performance infrastructure—often routing through unified gateways like n1n.ai for multi-provider resilience and minimal latency—the model performance itself is exceptionally consistent. However, downstream code introduced by human developers can silently corrupt the final output.

The Real-World Experiment

To demonstrate this failure pattern, we configured an IT triage agent powered by Gemini 2.5 Flash (accessed via Google's OpenAI-compatible API endpoint). The test scenario supplied a synthetic incident alert:

• Incident Summary: Production payment checkout API throwing HTTP 500 errors across 72% of customer requests immediately following a system deployment.
• Target Policy: An active API failure affecting core transaction checkout with high error rates constitutes a P0 critical incident requiring immediate human escalation.

The agent executed three instrumented local tools:

1. lookup_service_health: Retrieved operational metric fixtures confirming a 72% error rate.
2. get_recent_deployments: Retrieved records indicating a deployment occurred 15 minutes prior.
3. fetch_incident_runbook: Loaded triage policy docs defining severity tiers.

When evaluated by Gemini 2.5 Flash, the model performed its reasoning accurately and generated the correct raw decision:

\{
  "severity": "P0