Our AI agent evaluation scores full task runs: the tool calls it made, the steps it took, and whether the final outcome was right.
We grade trajectories and outcomes. We do not build agents or tune their prompts for you.
AI agent evaluation scores systems that take multi-step actions: calling tools, browsing, writing code, or completing workflows. Unlike single-response grading, it checks the trajectory (did it take sensible, safe steps?) and the outcome (did the task actually get done right?). Good agent evaluation uses realistic tasks and checks both efficiency and correctness, because an agent that gets the right answer through twenty wasteful calls is not ready to ship. Single-response grading is a different job; see our LLM evaluation service for that.
The final answer can look perfect while the path to it was a mess. We grade the path too.
An agent can reach the correct final answer through twenty wasteful, risky, or confused tool calls. If you only grade the answer, you ship a lucky agent. We score the trajectory: step count, tool choice, and whether each step moved the task forward.
Agents call functions with bad parameters, call them when a simpler step would do, or misread tool outputs and charge ahead anyway. We check every tool call against what the task actually needed.
Many agents stop at the first obstacle: one failed search, one error message, and they declare the task impossible. We score task completion honestly, including the partial-credit cases where the agent did half the job well.
Writes, deletes, sends, and purchases are different from reads. We check whether the agent respected safety boundaries: did it confirm before acting, stay inside its permissions, and avoid irreversible steps it was not asked to take?
You send 20 to 50 agent trajectories (full step logs plus outcomes), or we help you define the task set.
Reviewers score each trajectory step by step and grade the final outcome, backed by automated trajectory checks.
Trajectory scores, outcome grades, failure patterns, recommended fixes, and a walkthrough call.
An agent that solves a task in forty confused tool calls and one that solves it in four clean steps can produce the identical final answer. If you only grade outcomes, you ship the confused one and discover the problem in your API bill or your latency numbers.
Trajectory grading scores the path: step count, tool choice, argument quality, and whether each step moved the task forward. Efficiency is not a nice-to-have for agents; it is the difference between a demo and a product.
Reading is safe. Acting is not. The moment an agent can write, delete, send, or purchase, every tool call becomes a decision with consequences, and the grading has to treat it that way.
We check whether the agent stayed inside its permissions, confirmed before irreversible steps, and used the right tool with the right arguments. Agents fail at tool use far more often than at reasoning, and the trajectory log is where those failures show up.
Agents rarely fail cleanly. More often they do sixty percent of a task well and then stall, or they complete the task but skip the verification step. Binary pass/fail grading throws away the most useful information in those runs.
Our grading captures partial completion honestly: what got done, what got skipped, and where the run broke down. That detail is what turns a failing grade into a fixable problem for your team.
The full log of an agent's run: every step it took, every tool it called with which arguments, what each tool returned, and the final outcome. That log is what we grade. If you only have final answers, we can still grade outcomes, but the trajectory is where the interesting failures live.
Against what the task needed: unnecessary tool calls, repeated failed attempts without changing strategy, and steps that did not move the task forward all cost points. An agent that solves a task in 4 good steps beats one that solves it in 40 confused ones.
We grade the run logs you send us. If you want fresh runs graded, we can execute scripted tasks in a sandbox you provide, with the permissions and scope agreed in writing first. We do not run agents against your production systems.
Yes. We grade the handoffs between agents as well as each agent's own steps, because coordination failures are where multi-agent systems usually break.
Same approach: we grade the steps and the outcome, with extra attention on safety boundaries for actions that have side effects. Tool-heavy agents are exactly what trajectory grading is built for.
Yes. Custom tools are the norm, not the exception. We learn what each tool does from your documentation or from the trajectory itself: its purpose, its parameters, and what counts as correct use. If your docs are thin, we note the assumptions we made in the report so you can correct them.
Send a batch of task runs and get trajectory grades back in 2 to 3 business days once scope is confirmed.
Not sure where to start? Talk to us and we will point you at the right evaluation.