analiza aiqbench
This is a practical guide to testing AI agents, covering how to build small trace-based datasets and choose evaluation methods for nondeterministic behavior. It is vendor-authored and centers on LangSmith, so readers should treat its recommendations as one workflow rather than a standard. The useful takeaway for agent builders is to start with a few observed behaviors and expand the eval suite from there.
The Most Common Questions About Agent Evals, Answered
Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down.
As you build and ship an agent, you’ll need a way to check that it behaves the way you expect, both in development and once real users start relying on it. In traditional software, the same inputs produce the same outputs, so a unit test can check for an exact result. Agents are harder to test for a few reasons: The agent chooses its own steps, so a correct output can still come from a “wrong” path. For example, an agent might call a tool it doesn't need or take an action without user permission. Agent evals are the solution. They score your agent’s runs with checks that account for these variations. Similar to the agents they evaluate, these tests are not one-size-fits-all, and the best suite of evals depends on the team, their product, and what they want to optimize for. Over the past few years of working on LangSmith, LangChain’s platform for agent engineering, our team has encountered a lot of different eval questions. This blog walks through some of the most common questions on how to get started with agent evals and the different types of evals you can use in your workflows. Where should we start?…
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