Your AI Agent Says “Done!” — Here’s How to Know If It’s Lying
Author(s): MahendraMedapati
Originally published on Towards AI.
A tested, dependency-light tracing and evaluation library that catches the failure mode plain logging can’t — an agent that fails a tool call and confidently reports success anyway.
Picture a pilot’s black box. It doesn’t fly the plane. It doesn’t make the plane safer by itself. What it does is record, second by second, exactly what every system was doing — so that when something goes wrong, nobody has to guess. Nobody re-flies the flight from memory. They read the trace.
The article argues that production-grade AI agents need observability (span/trace-based step-by-step recording) and evaluation (automatic rubric scoring of the finished run) as separate disciplines, because “no crash” and superficial logs can miss silent failures where a tool call fails but the agent still delivers confident, well-formatted success. It walks through the core concepts of spans, traces, and rubric-based agent evaluation, then focuses on a specific hard-to-detect bug: silent/hallucinated success after a failed tool call. Using a minimal “TraceBench” mini-project, the author demonstrates how to instrument an example customer-support agent with a dependency-light tracer, how an evaluator scores runs using multiple named checks (including a centerpiece no_silent_failures check that cross-references tool error spans with acknowledgment language in the final answer), and how tests and a deliberately buggy LLM wrapper prove the evaluator can catch the lie even when nothing throws an exception. The walkthrough includes implementation details, an offline-first testing approach, performance considerations, limitations of keyword-based heuristics, and best practices for shipping trustworthy agents in production by running the evaluator on every request and monitoring silent-failure rates.
Read the full blog for free on Medium.
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