Evals that evolve with your agent
Prompts, models, and orchestration change every week. Static eval suites go stale and miss the regressions that ship anyway. Tessary reads your codebase and production traces, synthesizes a working eval pipeline, and keeps it current as your agent changes, so you catch regressions from any change before your users report them.
- Setup
- Run the evals plugin in your repo. First graders in minutes, no eval file to start from.
- Catches
- Regressions from prompt edits, model bumps, code refactors, and infra changes.
- Runs on
- Your provider keys. Every grader is transparent and yours to keep.
- Built for
- CTOs and engineers with an agent already serving real production volume.
Static evals cannot keep pace with a moving agent.
Agent-based products change faster than the eval approaches built for slow-moving software. Golden datasets, manual annotation, and dashboard uploads go stale faster than teams can maintain them, so changes ship blind and regressions surface only after someone complains.
- ~1 day
of engineering time per agent change today: uploading traces to a dashboard, reviewing them by hand, and running ad-hoc spot checks
- Any change
a prompt edit, a model version bump, a code refactor, or an infra update can break a flow nobody meant to touch
- After the fact
most regressions surface through user complaints and support tickets, not before the change ships
From your codebase to a working eval pipeline
No blank eval file, no hand-built failure list. The pipeline is synthesized from your code, calibrated on your traces, and re-synthesized as the product evolves.
Point it at your repo
Run the evals plugin inside your own codebase. It reads your code and, when you connect them, your production traces. Nothing leaves your environment by default.
Get a synthesized pipeline
Tessary finds every place your product calls a model, classifies what each call is doing, and writes graders with rubrics for the ways each one can fail. You get a failure taxonomy specific to your product, not a generic checklist.
Curate and connect your traces
Accept, reject, or edit graders in the platform, then connect your trace source. Each run judges real production traffic and records a verdict per grader.
Catch regressions as the product moves
Re-synthesis runs as your agent changes. When a prompt edit, model bump, or refactor breaks a flow, the regression shows up against the baseline instead of in a support ticket.
Eval infrastructure that stays current
The graders are an output you can read and keep. The value that compounds is the synthesis, the taxonomy, and the curation history that grows with your product.
Synthesis from your code
Connect your repo and get call sites, failure modes, and graders. You do not start from a blank eval file or a generic template.
Regression detection across any change
Compare against a baseline after every change, including the ones that look unrelated to the agent layer.
A failure taxonomy that grows
A hierarchical map of how your product fails, specific to you, that compounds with every curation decision you make.
Transparent, portable graders
Read every grader. Run them on your own provider keys. Keep them if you ever stop using Tessary.
Runs on your production traces
Connect Braintrust or Langfuse and judge real production traffic, so evals reflect real usage instead of synthetic data.
Works across providers
Your agent does not have to run on one model vendor, and neither does the eval pipeline that watches it.
Built for the person who owns agent quality.
- Production agents
Tessary is for the CTO or engineer who owns agent quality and gets blamed when it breaks. It fits teams with an agent already serving real volume, where a regression has already cost something, not pre-product teams still shaping a first prototype.
Questions about agent evals
Stop shipping agent changes blind.
Connect your code, get a working eval pipeline, and keep it current as your agent evolves. Catch the next regression before your users do.