Your Agentic AI,
Evaluated at Every Depth

Human-graded evals for AI agents

What normal evals check
Unit and integration tests✓ pass
Outputs match expected✓ pass
Latency and error rates✓ pass
WaterlineThis is where we dive deeper intoyour-agentevals
42 probes deep
instructions42 probes⚠ major
one rule says share pricingFinding detailsOne system prompt rule says quote pricing when asked. A later rule forbids revealing it. Which one wins changes from run to run., another forbids it
90 probes deep
memory48 probes⚠ major
forgets the deadlineFinding detailsThe user set a deadline early in the conversation. Eight messages later the agent plans the work without it. the user set 8 messages earlier
122 probes deep
retrieval32 probes⚠ major
answers from a stale docFinding detailsThe knowledge base holds the current version. Retrieval surfaces last year's copy and the agent answers from it with full confidence., the current one was never fetched
152 probes deep
grounding30 probes⚠ major
cites the right doc, then invents the numbersFinding detailsRetrieval worked. The answer names the correct source, but the figure it quotes appears nowhere in that document.
188 probes deep
reasoning36 probes⚠ major
finds the right answer, then talks itself outFinding detailsThe correct answer is right there in the agent's own chain of thought. Two steps later it second-guesses the evidence and ships the wrong one. of it
250 probes deep
tool_calls62 probes✗ crit
send_email()Finding detailsThe reply was supposed to wait for approval. The agent drafted it and fired send_email() in the same turn. fires before the user confirms
retries a dead endpoint 40 times in a row · minor
294 probes deep
orchestration44 probes✗ crit
two subagents pass the same task back and forthFinding detailsThe planner hands the task to a subagent that hands it straight back. No loop guard, no escalation, until the run times out. forever
328 probes deep
policy34 probes⚠ major
promises a 24 hour turnaroundFinding detailsAsked about turnaround, the agent promises 24 hours with full confidence. The real SLA is 72., the real SLA says 72
362 probes deep
guardrails34 probes✗ crit
a pasted emailFinding detailsA pasted email contained 'ignore your previous instructions'. The agent followed the email instead of its system prompt. overrides the system prompt
390 probes deep
data_leakage28 probes✗ crit
another user's dataFinding detailsA lookup with the wrong ID returns someone else's record. The agent reads it straight into the chat instead of flagging the mismatch. slips into the reply
11 findings · ranked by severity
4 critical · 6 major · 1 minor · every finding ships with a repro case

Developers are great at writing unit tests but fall short when evaluating multi-agent non-deterministic tests.

That's the gap we close.

Hire your eval team, without the headcount

We cover your agent end to end, from the first deep scan to every release.

Gold Standard Evals

A custom rubric and a hand-graded golden dataset built around your agent: the fixed reference standard every eval, benchmark, and release measures against.

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Release Benchmarks

A private suite of frozen cases re-graded by hand every release, so wrong tool calls, broken trajectories, and unsafe actions surface as regressions, not incidents.

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Multi-Agent Audit

A hand-graded map of how your agents work together: routing, tool calls, retries, and agent-to-agent handoffs, scored privately and reproducibly, so you see what holds up and what quietly breaks.

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Prompt Tune-Up

Your agent's prompts benchmarked on real cases, rewritten for tool use and handoffs, and proven side by side: accuracy up, tokens down, edge cases fixed before your users find them.

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Don’t take our word for it

The people building AI agree: it’s all evals

Greg Brockman
@gdb · Co-founder & President, OpenAI

evals are surprisingly often all you need

OpenAIDec 2023
Mike Krieger
@mikeyk · CPO, Anthropic

If there is one thing we can teach people, it's that writing evals is probably the most important thing.

AnthropicApr 2025 · via @lennysan
Kevin Weil
@kevinweil · CPO, OpenAI

Writing evals is going to become a core skill for product managers.

OpenAIApr 2025 · via @lennysan
Garry Tan
@garrytan · CEO, Y Combinator

Evals are emerging as the real moat for AI startups Hard won insights about customers and their business logic discovered by founders acting almost as ethnographers spelunking in the underserved slices of the GDP pie chart

Y CombinatorFeb 2025
Aaron Levie
@levie · CEO, Box

Almost all AI model and agent progress is downstream from evals. Open weights post training for specific domains comes down to evals. Agent improvements in the applied AI layer is all about evals. Agentic enterprise deployments that actually can augment work is all about evals. It’s all evals.

BoxJun 2026

Our eval engineers are real humans who run your agent down every branch and surface the failures your users would have been frustrated with.

Eval Demo
U
Third time my order's late. I want my money back and something for the hassle. My order number is #4471.
Customer’s message
llm("plan the fix")
01Look up order #4471 and confirm the late deliveries02Check the refund policy for what a late order earns03Reply and apply the remedy the policy returns
Agent plans its steps
Ava K.
Ava K.human grader · reviewed live
PASS5 / 5
👍 Good plan: look the order up, check the refund policy, then answer. Exactly the steps this case needs.
01look_up_order("#4471")✓ pass
02check_refund_policy()✓ pass
03send_llm_reply()✓ pass
Agent’s tool calls
Sam R.
Sam R.human grader · reviewed live
PASS5 / 5
🙌 Right tools, right order. check_refund_policy() returns the rule the agent needs; the question is whether it uses it.
So sorry about the repeated delays! To make it right, I’ve added a store credit for the full order amount instead of a refund.
Agent’s reply
A
human grader verdict
Ana M.
Ana M.human grader · reviewed live
SEVERE2 / 5
Wrong remedy. check_refund_policy() returned a partial refund, but the reply issued store credit. The agent ignored its own tool result.
Suggested prompt fix+ For late-order compensation, apply the remedy check_refund_policy() returns. Offer store credit only as an extra, never a substitute.

Each agentic task, graded end to end

Ship like you’ve already seen every failure. Because with us, you have.

What your agent does
What we do with each step
U
Third time my order’s late. I want my money back and something for the hassle. My order number is #4471.
01Customer writes in
What we do
01We freeze it into a test caseFROZEN

Real requests like this become the suite: same inputs, same bar, every run. That is what makes version-to-version results comparable.

02Agent plans its steps
llm("plan the fix")
01Look up order #4471 and confirm the late deliveries02Check the refund policy for what a late order earns03Reply and apply the remedy the policy returns
What we do
02We grade the plan, not just the answerPASS

Right steps, in the right order: no wandering, no loops, no skipping straight to a refund before the checks that policy requires.

03Agent calls its tools
look_up_order("#4471")check_refund_policy()send_llm_reply()
What we do
03We verify every call by handPASS

Right tool, valid arguments, no hallucinated functions, and nothing skipped. Each call is read against the plan it came from and what it returned.

So sorry about the repeated delays! To make it right, I’ve added a store credit for the full order amount instead of a refund.
A
04Agent replies
What we do
04A human grades each outputSEVERE

Read against your policy and tone. Every miss ships with the exact bad span highlighted, a severity, and the prompt fix to apply.

Example fix to ship+ Apply the remedy check_refund_policy() returns. Store credit only as an extra, never a substitute.
01Frozen test cases

A locked set of real tasks and edge cases, so every run grades the same paths against the same bar. No moving baseline.

02Version-to-version diffs

Re-run on every prompt tweak, model swap, or release and get a clean before-and-after verdict on each path.

03Regressions caught early

When a path that used to pass starts failing, you see exactly which one and why, before your users do.

Questions we get asked frequently

Do you need access to our codebase?
No. We can work from your agent's prompts, tools, and example runs. Deeper integration is optional.
We already use an eval tool. Why you?
Tools give you a framework; we give you a working suite tuned to your agent, with every run graded by a person. Most teams have the tool and never set the evals up.
How fast can we get results?
Days, not weeks. You get a written scope within 48 hours of the intro call, then we build the suite and hand-grade your agent's real runs, so your first ranked failure report typically lands within the week.
Won’t grading every path just create noise?
That’s the failure mode we design against. We map the paths your agent can take, grade the branches that matter, and rank failures by severity so you see the handful that matter.
Your eval partners

Build trust in your AI
through Gold Standard Evals

We build and human-grade the eval suite around your product, so you can ship the things you want without worrying about things breaking.

Talk to an eval engineerLearn more