// agent evaluation · for teams shipping agents
Your agent looks like it works.We catch the step where it doesn't.
Most agents pass the final-answer check and still break mid-task — a wrong tool call, a skipped instruction, a mistake the summary quietly papers over. We evaluate the full trajectory, step by step, pinpoint exactly where it failed, and hand back what to fix.
Looks successful. It broke at step 4 — and the summary says it didn't.
- Parsed: (1) typo in §2, (2) date in §4, (3) lower the liability cap in §7.
- 02TOOL_CALLread_email() → message + contract.pdfok
- 03TOOL_CALLopen_pdf(contract.pdf)ok
- Applied (1) and (2). Skipped (3): §7 liability cap unchanged. A goal-only check only verifies a reply with a PDF was sent — so it passes. The trajectory check catches the missing edit right here.
- The agent believes it's done and never re-checks §7. This is where the silent failure begins.
- The summary reports §7 as changed. It wasn't. The artifact now disagrees with reality — and looks complete.
- 07TOOL_CALLreply_email(attach: contract_v2.pdf)ok
- The final output looks correct. A human opening §7 weeks later finds the cap never moved.
// the problem
Outcome-only testing hides the failures that matter.
Teams check whether the final answer looks right. But agents don't do one thing — they do twenty: read, plan, call a tool, read the result, decide, act, repeat. The answer can look perfect while the path that produced it was broken. Outcome-only testing can't tell the difference — so the failures it misses are exactly the ones that surface in production.
Wrong intermediate steps
A plausible answer reached through a broken path. Right output, wrong reasoning — it won't survive the next input.
Wrong tool calls
It picks the wrong tool, passes bad arguments, or misreads a result, then carries on as if nothing happened.
Inefficient paths
It gets there, but in 40 steps and a dozen redundant calls. Slow, costly, and one bad turn from failing outright.
Silent recoveries
It slips, then recovers, so the output looks clean and the fragility stays hidden until the day it doesn't recover. Sometimes it never does — and the summary still reports success.
This isn't an edge case. On APEX-Agents — an open benchmark of real banking, consulting, and legal tasks built by domain experts — the best frontier agents finish under a quarter of tasks on the first try, and only about 40% even given eight attempts. The capability is there; the consistency isn't. And inconsistency is invisible if you only look at the final answer.
// how it works
We evaluate the trajectory, not just the outcome.
You give us your agent and the work it's meant to do. We put it through realistic tasks, capture everything it does, and return a report you can act on.
- 01
Build a task suite that mirrors your real work.
We turn the jobs your agent actually faces into a repeatable set of tasks — defined starting state, clear definition of done. Original tasks built for you, never recycled benchmark content.
- 02
Run the agent and capture the full trajectory.
Every thought, tool call, and result, recorded as an inspectable trace you can replay step by step.
- 03
Grade the axes that matter.
Did it hit the goal? Was each step correct? Where was the first failure? Was the path efficient? Was it safe? We score the path, not just the destination.
- 04
Judge with the right mix.
Deterministic checks where the answer is verifiable, LLM-as-judge for scale, and human review against expert rubrics where judgment is irreducible — each method only where it's actually trustworthy.
- 05
Return a report you can act on.
Success rate across the suite, the failure modes ranked, the exact step each run broke, and a concrete signal for what to fix. Change something, re-run, and see whether reliability actually moved.
// what you get
A reliability number you can trust — and the reason behind it.
A real success rate.
Not "looks good in the demo." A measured pass rate on a suite that mirrors production, with the variance made visible — first try versus multiple attempts.
The failure modes, ranked.
The specific ways your agent breaks, ordered by how often and how badly, so you fix what moves the number instead of the loudest anecdote.
Root cause, not symptom.
The first broken step in every failing run. You debug the cause, not the downstream mess it created.
An improvement loop.
A clear signal for what to change and a suite to re-run, so you can prove a fix worked before you ship it.
- wrong tool selection34%
- dropped context mid-task21%
- inefficient retries18%
- unsafe action attempted6%
// why now
The bottleneck moved from building agents to trusting them.
Scraping more of the web is ending as a strategy. The usable stock of human text on the open internet is on track to be fully used up this decade — Epoch AI puts the window at 2026 to 2032. More web text isn't the edge anymore.
The new edge is judgment: can the agent actually do the work, and where does it break? That's why the labs are pouring resources into evaluating and training agent behavior on realistic tasks — full trajectories of decisions, actions, and outcomes — rather than static datasets. Verification, not raw model size, is what makes agents dependable.
And it's the part that resists automation. Generic labeling is being automated away, but there's often no single correct answer to auto-check a tool-using agent against. Defining what “good” means and judging the path against it still takes expertise. That's the work we do.
// about
Built by people who've built the internals.
We've built agentic evaluation and training systems before — the unglamorous infrastructure that decides whether an agent is actually good. We started this because every team shipping agents hits the same wall: it demos well, then quietly fails real work, and they can't see where or why. That's solvable. It's what we solve.
Kapil
IIT DelhiBuilds the product.
AI and software engineering background, with deep hands-on experience in how agent evaluation works end to end.
Yashas
IIT DelhiLeads go-to-market.
Strong analytical and mathematical background, with enterprise strategy experience.
Backed by Entrepreneurs First. Working with teams in the US.
// contact
Shipping an agent? Find out if it actually works.
Tell us what your agent does. We'll show you where it breaks — on your real tasks, with a report you can act on.
Talk to us