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Agent Platform

AI Agent Evaluation & Testing

We test whether an agent actually performs the required task reliably — before release and continuously in production.

TRACEai-agent-evaluation
  1. 01DATASETReal cases with expected outcomes.
  2. 02RUNThe agent executes each case.
  3. 03TRACEEvery step is recorded.
  4. 04GRADEAutomated and human graders score the run.
  5. 05COMPAREResults are compared with the last release.
  6. 06EVAL.PASSOnly a release that holds up ships.
6 STEPSTRACE.COMPLETE

What it is

Agent evaluation is a continuous process. We evaluate behaviour, not only model benchmarks: what the agent did, with which tools, and whether the outcome was right.

There is no single universal "agent score". The metrics depend on the system — task success, tool-call accuracy, grounding, policy compliance, latency, cost and human escalation are the usual starting set.

When you need it

  • SIGNAL 01Nobody can say whether the last prompt or model change made the agent better or worse.
  • SIGNAL 02The agent passes the demo and fails on real cases.
  • SIGNAL 03Model benchmarks look good, but the task still fails.
  • SIGNAL 04You need evidence of quality before a customer or reviewer will sign off.

What we build

The engineering.

  • 01

    Golden datasets

    Real cases, with the expected outcome, that define what "working" means.

  • 02

    Task-success and workflow evaluation

    Did the agent complete the task and the workflow, not just produce plausible text.

  • 03

    Trajectory and tool-use evaluation

    The path the agent took — which tools, in which order, with which arguments.

  • 04

    Regression testing

    Evaluation that runs on every change and blocks a release that gets worse.

  • 05

    Safety and adversarial testing

    Deliberate attempts to make the agent misbehave.

  • 06

    Human and production evaluation

    Human review where automatic grading is not enough, and evaluation on live traffic.

How it works

One run, end to end.

  1. 01

    DATASET

    Real cases with expected outcomes.

  2. 02

    RUN

    The agent executes each case.

  3. 03

    TRACE

    Every step is recorded.

  4. 04

    GRADE

    Automated and human graders score the run.

  5. 05

    COMPARE

    Results are compared with the last release.

  6. 06

    EVAL.PASS

    Only a release that holds up ships.

What it integrates with

Chosen for the workload and your environment — not a preferred provider.

  • Trace stores
  • Graders
  • Datasets
  • CI pipelines
  • Model comparison
  • Retrieval evaluation
All capabilities

In production

Production is part of development.

Evaluation, security, deployment and operations begin before release — on this service as on every other.

EVAL

How we test it

  • Task success, tool-call accuracy and workflow completion.
  • Policy compliance, grounding and failure rate.
  • Latency, cost and human escalation, tracked across releases.

POLICY

How we secure it

  • Safety evaluation and adversarial testing, including prompt injection.
  • Policy compliance checks as part of every run.

RUNTIME

How we deploy it

  • Evaluation wired into the release pipeline as a gate.
  • Model and prompt comparisons before any switch in production.

TRACE

How we operate it

  • Production evaluation on sampled live runs.
  • New failures turned into new test cases.

What you receive

Engineering outputs, not a deck.

We do not hand over a prototype and leave production engineering to you.

  1. 01A golden dataset built from real cases
  2. 02An evaluation suite with task, trajectory and tool-use graders
  3. 03Regression testing in your release pipeline
  4. 04Safety and adversarial test results
  5. 05A report of current behaviour, failures and priorities
  6. 06Production evaluation running continuously