AI Agent Development

AI Agent Evaluation & Reliability

Reliability has to be demonstrated on the build intended for release, through repeated task trajectories, explicit tool and policy checks, calibrated graders, and critical slices that stay visible.

A confident demo proves almost nothing about the next run. We tie a regression suite to the build you want to release and trace what the agent does across repeated tasks, so tool behavior, recovery, escalation, and run-to-run variation stay visible where the release decision gets made.

Build-linked regression evidence, reviewed grader disagreements, and a critical-slice brief give your release owner the grounds to accept the agent, accept it with conditions, or reject it.

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Illustration of AI Agent Evaluation & Reliability: a team testing an AI agent's tools and decision boundaries

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Steps, gates, and who decides

How we work

We start from the build you actually have, not the demo build. Every stage after that hangs on one question: can the release owner see what this agent does, repeatedly, and act on it?

  1. Set the evaluation charter

    We map representative tasks, known failures, tool contracts, policies, and release criteria into one evaluation specification.

    AI assist
    The agent's own logs give the model raw material for candidate tasks and failure categories, which the release owner then reviews.
    Human gate
    Do the tasks and thresholds reflect the agent's real operating conditions? Your release owner approves which tasks and thresholds define the charter.

  2. Instrument and run

    We capture trajectories and side effects, then repeat representative, adverse, recovery, and escalation cases against the same build.

    AI assist
    Repeated runs happen under tooling that captures each trajectory for calibrated review.
    Human gate
    Can each important result be reproduced from its trace? Your evaluation lead confirms a failing trace before it counts as a defect.

  3. Calibrate the evidence

    We compare deterministic checks, rubric graders, and human-reviewed traces, and resolve any disagreement as a calibration issue, out in the open.

    AI assist
    When a deterministic check and a rubric grader disagree, the model surfaces the pair so a person can settle it.
    Human gate
    Are grader decisions stable enough for the release question? A named specialist resolves which grader call is trustworthy.

  4. Retest the release gate

    We rerun failed paths after changes and record passed thresholds, open exceptions, residual risk, and the next review trigger.

    AI assist
    Retest results arrive as a draft slice report that a reviewer edits before it reaches the release owner.
    Human gate
    Does your release owner accept, conditionally accept, or reject the build? Your release owner accepts, conditions, or rejects the build.

Named artifacts you keep

What you get

The evidence stays pinned to one build, its inputs, and the release question you brought. Nothing here describes a different agent or a different version.

  • Playbook

    Agent task and failure charter

    Defines representative tasks, failure categories, tool contracts, policies, thresholds, and review owners.

  • Test evidence

    Build-linked trajectory regression pack

    Connects test cases and trajectory capture to the agent version being reviewed.

  • Dataset

    Grader disagreement and trace sample file

    Records where deterministic checks, rubric graders, and specialist review agree or diverge.

  • Decision record

    Recovery and escalation slice findings brief

    Breaks out task, policy, tool, recovery, and escalation results with open exceptions and retest status.

Scope and honest limits

When to bring us in

An agent that handled one impressive task may fail the eleventh, call a tool outside its contract, or stall exactly when it should hand off. Choose this when you need to know that before release, not after.

A good fit when

  • Your first agent run looks convincing, but repeated representative tasks still vary in success, tool behavior, recovery, or escalation.
  • Tool calls complete in the demo, yet nobody reviews their side effects against the explicit contract and policy on the same build.
  • A release date is approaching, but the owner still lacks thresholds and a separate view of critical task, tool, recovery, and escalation exceptions.
  • Important agent paths are known, but no failure taxonomy or trajectory instrumentation shows where a repeated run diverges from the intended path.
  • Deterministic assertions and rubric graders both score the build, yet their disagreements have not been resolved against specialist-reviewed traces.
  • The blended result looks strong, while stress, recovery, policy, tool, and task slices still hide the path most likely to block release.
  • A release gate exists, but its owner cannot see which exceptions remain open, what changed, or which failed trajectories need another run.

Better handled as other work when

  • You need trajectory findings accepted for audit, certification, legal, or regulatory use. The pack records repeated runs, while approval stays with your authority.
  • You need proof that the agent is perfectly accurate, autonomous, safe, or free of residual risk. Repeated runs describe only the tested tasks and build.
  • You need the production agent operated or its failed paths remediated. This engagement evaluates and retests the agreed build, while implementation is separate.

If one of these is closer to your situation, start here instead: See the evaluation service

We operate the systems we test

It's hard to test a system well if you've never had to keep one running. We operate production AI ourselves, so our evaluation, security testing, and LLMOps work starts from what actually breaks. The people on it are senior engineers, and Zeo has been doing client work since 2011.

Content we've produced on this topic

Articleby Burak PehlivanWhat is WebMCP? Agentic Web and Websites Interacting with AI AgentsArticleby Yiğit KonurMCP Server Observability: Monitoring, Testing & Performance MetricsArticleby Yiğit KonurMCP Server Safety: Human-in-the-Loop Controls & Risk Assessment

Tools we use

Tools behind this work

  • Langfuse

    traces the agent's tool calls and recovery across every repeated run

  • Braintrust

    ties the regression suite to the exact build the release decision is about

  • Confident AI / DeepEval

    runs calibrated graders against trajectories, checked for agreement with humans

  • Arize Phoenix

    isolates the specific task slices where the agent's reliability breaks down

Next step

Test the build, not the demo

Show us the agent you actually intend to release, the tasks it must survive, and the person with final authority over release.
Talk to Zeo

Before you decide

Questions we hear a lot

The agent's scope and architecture, representative tasks, tool contracts, trajectories, policies, known failures, environment access, and the release criteria you're proposing. Sensitive material also gets an agreed purpose, access limit, and retention period before testing starts.