Tool-use evaluation: Testing AI agent capabilities

Agent work rarely fits in a neat box. One prompt looks like a pure function call; the next turns into a wandering conversation with tools, retries, and backtracking. Generic accuracy scores fall apart in that mess. What helps is splitting evaluation by how the agent actually works and tying those checks to the outcomes that matter. This post walks through a practical way to do that, with sources and tactics used by teams shipping real systems.

Expect hands-on advice: how to score single-turn versus multi-turn tasks, which metrics to trust, and how to wire offline checks to live gates. The result is an evaluation setup that catches drift fast, avoids fake wins, and keeps agents pointed at user value.

The evolving nature of agent tasks

Single-turn tasks behave like functions; multi-turn flows behave like workflows. Treat them differently or the signal gets noisy. Split your evals by task mode and run the right checks for each.

For single-turn tasks:

• Assert inputs, outputs, and tool calls: strict schemas, fast verdicts

• Lock in repeatable fixtures and compare exact matches where possible

For multi-turn flows:

• Score path choice, recovery steps, and when to stop

• Track transitions between tools and the quality of fallbacks

• Use stepwise judges for plans and tools to isolate where things go off the rails

Here is what typically goes wrong: infinite loops; bad tool selection; tool spam that looks like progress but is not. Short feedback loops catch these quickly. Keeping a human in the loop is not optional for nuanced work, a point Chip Huyen stresses in her interview on applied AI engineering source. Community playbooks on agent evaluation tools help with practical wiring too source.

Coverage should match the task’s surface area. Vary fixtures and goals, then mix offline checks with live monitors so blind spots show up early. Hybrid flows that connect evaluation to outcomes are a durable pattern, and it echoes what Statsig has seen in its work on experimentation with AI systems source.

Defining meaningful metrics and judgments

Good evaluations start with metrics that map to real user goals. Resist vanity scores. Start with correctness, then expand into reasoning and tool use, and only then optimize speed or cost.

Prioritize these:

• Completion accuracy: does the final answer meet the objective fully and exactly; run it offline and confirm online with hybrid evaluations source

• Argument soundness: is the rationale coherent, grounded, and sourced; bring in a human reviewer where nuance matters, as called out in both Chip Huyen’s advice and human-in-the-loop discussions from practitioners sources: Huyen, HITL

• Tool correctness and restraint: the right tool, correct arguments, minimal calls; log tool paths and validate them with repeatable tests tied to experimentation trends source

Then, keep the rubric compact and consistent:

• Task success: exact, partial, or fail

• Reasoning quality: consistent, supported, relevant

• Tool usage: correct tool, correct args, correct count; no misuse

Two guardrails keep teams grounded. First, track user value, not just match rates. Practitioners report that engagement and task outcomes beat clever prompts every time source. Second, start small and cap risk. Lenny Rachitsky’s product guidance applies cleanly to agents: align with real work, gate exposure, and expand when the evals say yes source. Hybrid offline and online checks give the confidence to do exactly that, and platforms like Statsig make tying those judgments into feature gates straightforward source.

Building representative tests for real-world demands

Datasets should look like production: messy inputs, varied goals, and the tools users actually touch. Anchor each test to a single skill to avoid crossed wires. Evaluate mapping, routing, and argument construction in isolation, then compose.

Focus areas:

• Map: intent detection, slot filling, grounding against allowed sources

• Route: tool selection, fallbacks, stopping criteria that avoid dead ends

• Arguments: schema compliance, type safety, boundary values and error handling

Keep evals close to production traces; refresh often. Pull fresh examples from tickets and logs, then add a quick human pass where stakes are high, echoing guidance from Chip Huyen’s field experience source. Pair a regression suite with staged rollouts and canaries; this hybrid setup aligns tests with real impact and is a pattern highlighted in Statsig’s guides for AI builders source.

Tooling matters. Choose systems that support repeatable suites, human review, and workflow-level scoring. Community roundups help separate noise from useful options source.

Enhancing observability and iterative improvements

Live dashboards surface anomalies fast. Span-level traces tie alerts to specific prompts, tools, and outputs so root causes are clear. Run offline evals in parallel with production and use the results to guide controlled fixes, a practice also called out in Statsig’s experimentation work on AI source.

Iteration never stops. Metrics go stale; agents drift. Add human evaluations where automation struggles and keep the loop tight with small, safe changes informed by offline checks and guardrailed by online gates source.

Useful references when calibrating your setup:

• Community playbooks on evaluation tools and workflows source

• Field reports from QA teams using AI in real products source

• Practical questions from builders on what actually works sources: how to evaluate, what works in practice

Close the loop with hybrid rollout. Tie offline judgments to live A/B gates and outcome metrics, then ramp only when both agree. Statsig shares patterns for this on its AI tooling guides and vertical case studies; the short version is simple: connect eval results to production decisions sources: hybrid evals, AI verticals.

Closing thoughts

Strong agent evals look like this: split by task mode, measure what matters, test with production-like data, and wire everything to live outcomes. Favor user value over clever prompts; keep a human in the loop where judgment is nuanced; ship in small, reversible steps.

Want to go deeper:

• Statsig on experimentation and AI trends source

• Hybrid offline and online evaluations, plus tools for AI builders source

• Hands-on guidance from practitioners like Chip Huyen source

Hope you find this useful!