TurboDuck’s yellow duck inspector in a black leather coat and sunglasses examines an illustrated agent workflow.
AI AGENTS / TOOLS / BEHAVIOR / QUALITY

Trust is earned.
Test your agents.

AI agent evaluation with a tester’s instinct.

We inspect the decisions, tool calls, and real-world outcomes behind a convincing answer. Find the gaps before your users do.

THE INSPECTOR’S PROTOCOL
01 Observe02 Evaluate03 Verify
AGENT INSPECTOR / TURBODUCKILLUSTRATED BRAND SCENE
Built for agents that do things↳ Customer support↳ Business operations↳ Tool-driven workflows
01 / How we help

From a convincing demo.
To evidence you can trust.

Start with your most important workflow. Leave with a clearer picture of risk, actionable findings, and tests your team can keep using.

01 — UNDERSTAND

Agent Readiness Review

Find the gaps between a convincing demo and dependable behavior.

  • Workflow and risk assessment
  • Exploratory and edge-case testing
  • Prioritized findings and evaluation plan
Find your starting point
02 — MEASURE

Evaluation Sprint

Turn “good enough” into explicit criteria and repeatable checks.

  • Representative scenarios and rubrics
  • Automated checks and baseline results
  • Regression suite and findings review
Build your evaluation suite
03 — KEEP IMPROVING

Continuous Evaluation

Keep quality visible as prompts, models, tools, and customer needs change.

  • Regression runs and release reviews
  • New coverage from real incidents
  • Dataset and grader maintenance
Make quality a habit

Every engagement is scoped to your workflows, integrations, and risk. Your team retains its evaluation datasets, rubrics, and test code.

02 / Inside the evaluation

Inspect the action.
Verify the outcome.

An agent can sound right and still do the wrong thing. Explore three illustrative findings from a customer-support workflow.

Illustrative scenarios and authored traces. Not a client result or a measured benchmark.Read the full sample report ↗
03 / Our approach

Human judgment.
Systematic evaluation.

Good evaluation starts with a better question: what must this agent do, what must it never do, and how will we know?

We combine software QA discipline with checks designed for systems whose behavior can vary from one run to the next.

Curious by nature. Rigorous by design.We follow the evidence all the way to the outcome.
01

Define what success looks like

Map requirements, permissions, business outcomes, and the failures that matter most.

02

Test beyond the happy path

Exercise realistic tasks, ambiguous inputs, adversarial requests, and unavailable tools.

03

Check the action and the outcome

Combine code-based checks, calibrated AI grading, and human review. Repeat trials to understand variability.

04

Turn findings into lasting coverage

Document evidence, prioritize fixes, and preserve regression tests for the next release.

The thinking behind TurboDuck

Built on a background
in software QA and testing.

TurboDuck brings that experience to AI agents: questioning assumptions, investigating edge cases, and connecting a failure to its impact. Our focus is practical evaluation that helps engineering and product teams make informed release decisions.

A few good questions

Before we dive in.

What kinds of agents do you evaluate?

Our initial focus is customer-support and operational agents that use tools or take actions across business systems. We scope each engagement around a specific workflow and its expected outcomes.

Do we need an evaluation platform already?

No. We can help define your initial scenarios and checks, or work with the evaluation infrastructure your team already uses. Tool selection follows the workflow and your constraints.

How do you handle variable AI behavior?

We use repeated trials, explicit scoring criteria, and human calibration where judgment is involved. Reports distinguish observed failures from broader claims and explain the limits of the evidence.

What happens to our data and test assets?

Before access is granted, we agree on the testing environment, data boundaries, retention, and approved tools. Your team retains the evaluation datasets, rubrics, and test code produced for your engagement.

Does an evaluation guarantee the agent is safe?

An evaluation provides evidence within a defined scope. It cannot prove that every future interaction will succeed. Findings should inform release decisions alongside monitoring, operational controls, and human oversight.

Your next release deserves better evidence

Let’s put your agent
through its paces.

Start with one workflow, your biggest concern, and what a successful outcome should look like.

info@turboduck.ai Tell us about your agent, workflow, or evaluation goals.