You own agent quality. Tessary gives you the signal.
You have an agent in production at real volume, and your name is on whether it works. Today that means about a day of engineering time per change and finding out about regressions from customers. Tessary synthesizes an eval pipeline from your codebase, runs it on real traffic, and makes regressions a team default instead of a manual review you personally own.
- For
- CTOs and eng leads with an agent already serving real production volume.
- Owns
- Quality. You get paged when the agent breaks, and asked why later.
- Today
- ~1 engineering day per agent change. The goal: under 30 minutes.
Quality lands on you, with no system watching the agent.
At an SMB with a live agent, the person accountable for quality is usually the one fielding the support escalation. The work is reactive, manual, and expensive, and it scales with how fast the agent changes.
- ~1 day
of engineering time per agent change: uploading traces to a dashboard, reviewing them by hand, and ad-hoc spot checks
- Your name
is on agent quality. When a change breaks a flow, the question comes to you, usually after a customer noticed first
- No signal
on which part of the product to improve, because nothing is watching the agent across the changes that actually move it
From a manual review you own to a default the team runs
The path is deliberately short: point it at the production agent, get coverage, make it the default check, and watch the cost per change fall.
Point it at the production agent
Run the evals plugin in the repo for the agent that is already live. It reads the code and, with your traces connected, the real traffic. This is built for an agent with volume, not a prototype.
Get coverage without a project
Instead of staffing an eval initiative, you get a synthesized pipeline: call sites, failure modes, graders, and a failure taxonomy specific to your product, in the first session.
Make it the team’s default check
Graders run on every change and on a schedule against production traces. The team sees regressions as failed verdicts, so quality stops depending on one person remembering to look.
Watch the time per change drop
The status quo is about a day of engineering time per agent change. The target is under 30 minutes, with the first real regression caught inside the first week.
Built for the constraints you actually have
Fast adoption, evidence from real traffic, provider independence, and transparent graders, because anything slower or more opaque does not survive contact with a shipping team.
Time-to-value in minutes
A long SDK integration kills adoption. The plugin produces a first working pipeline in minutes, in your own repo.
Quality you can delegate
Regressions surface as verdicts on real traces, so agent quality is a team default instead of a heroic manual review you personally own.
Evidence from real traffic
Eval quality comes from your production traffic, not synthetic data, so the signal reflects what your customers actually do.
Provider independence
Your agent can run across multiple model providers, and the eval pipeline works across them too.
Transparent and portable
Every grader is readable, runs on your own keys, and stays with you. No black box, no lock-in, which matters for regulated buyers.
A taxonomy that compounds
The failure taxonomy and curation history become institutional knowledge about what "broken" means for your product.
Questions CTOs ask about agent evals
Related pages
Evals for agentic products
The overview: synthesized eval pipelines that evolve with your agent.
Regression detection
Catch breakage from any change, judged against a baseline.
LLM evals that evolve
Why auto-evolving graders beat static golden datasets.
The agent evals SDK
Wire graders into your agentic workflow and run them on every change.
Make agent quality a system, not a fire drill.
Connect the repo for your production agent, get a working eval pipeline in minutes, and give your team a default check that catches regressions before customers do.