When Building an AI Agent, the Journey Matters as Much as the Destination
A practical framework for evaluating trajectories, tool use, and process, not just the final answer. Originally published on Medium . 1. Introduction I am sure almost everyone reading this has tried to build an AI agent by now. For the POC, after some trial and error, it works, and you can demo it, everyone is impressed. You have tested it manually and it works, mostly. But thinking in terms of…
Building an AI agent involves more than just a single output, as the journey matters just as much as the destination. Traditionally, software engineering relies on integration tests, unit tests, and other evaluation methods, but AI agents pose unique challenges. These agents can hallucinate, fall into infinite loops, take unexpected paths, and even claim work is done prematurely.
When grading an LLM call, the focus is typically on one output, but agents perform actions, utilize tools, and engage in multi-step reasoning. A single hallucination or error can derail the entire process, often with the final answer still appearing correct.
To address this, the author built a small bug-fixing AI agent and developed an evaluation framework. The project, open-source and available on GitHub, aimed to evaluate the agent's process rather than just its output. The author acknowledges that evaluating an AI agent applies to scenarios where the agent is given a specific task, like fixing a bug, and stops when it's done.
The core concepts of agent evaluation include single-turn versus multi-step reasoning, capability versus regression evaluations, and the risks of relying on public benchmarks. The author emphasizes the importance of creating a golden dataset with known root causes and verification steps, as this foundation is crucial for a robust evaluation.
Three configurations were tested against a dataset of 10 planted Python bugs, with the baseline configuration using a full system prompt and toolset. The headline findings showed that the baseline configuration had a higher pass rate compared to the other configurations, but the pass rates varied between runs due to non-determinism in the LLM.
Overall, the evaluation process highlighted the need for a comprehensive framework that considers the entire agent journey, not just the final output.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
