As the CTO, you own agent quality. Tessary tells you what broke it.
You have an agent in production at real volume, and your name is on whether it works. Today you learn a flow broke from a customer, and the why takes days to dig out. Tessary watches grader verdicts as a trend, catches the drop, and walks it back to the change responsible, in time to fix it before it costs you an account.
- 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.
- Edge
- The cause, named. The same read also runs on the PR before a deploy and can gate the merge.
Quality lands on you, and the why is the hard part.
The product side feels the drop first, and your team gets asked why. That is the same split Sentry serves, and Tessary answers for both. It starts with watching the agent across the changes that actually move it.
- 16%
of Sonnet 4 requests were misrouted at the worst hour of a routing bug that ran from early August into September 2025. Anthropic wrote afterward: "The evaluations we ran simply didn't capture the degradation users were reporting."
Anthropic postmortem, 2025
- 89%
of 1,340 survey respondents have observability in place, but only 52.4% run offline evaluations. Watching dashboards is common. Knowing why a score moved is not.
LangChain State of Agent Engineering, 2025
- 5%
of all LLM call spans reported an error in February 2026. The rest of what went wrong returned cleanly, so nothing in your error budget moves while a flow quietly degrades.
Datadog, State of AI Engineering 2026
From a manual review you own to a cause you can point at
Land your traffic, watch the graders as a trend, route the alerts, and when something drops, get the change behind it.
Land your production traffic
Push traffic in via the OTLP receiver (gen_ai OpenTelemetry) with the TypeScript or Python SDK, or connect Langfuse or Braintrust as an upstream source and start from the traces you already collect. This is built for an agent with volume, not a prototype.
Watch quality as a trend, not a dashboard
Graders score real traffic on the dimensions your product depends on: task completion, groundedness, tone. Detection reads the trend across verdicts over time rather than any single score, so an imperfect grader still catches a real change; it misfires at a stable rate, and a jump from 3% to 11% is a genuine shift.
Route alerts where the team already is
Digests, briefs, and threshold alerts go to Slack, Sentry, PagerDuty, Linear, or a webhook, so a regression stops depending on one person remembering to look. Before a deploy, a GitHub Action reads the PR diff, maps the touched surface to the failure families most likely to fire (tool-call errors, refusal spikes, hallucinated citations), and can gate the merge.
Get the cause, not just the page
When a grader trend breaks, Tessary traces the failing quality dimension back through the agent's chain to the change that caused it: a prompt edit, a tool update, a dependency bump, an upstream agent's shift, or a provider model update that never touched your repo. Commit-SHA lineage covers the ones that did.
Built for the constraints you actually have
Cause-finding on production drops, a pre-deploy read on risky changes, provider independence, and lineage you can audit.
Risk read before the merge
The diff classifier and change-history risk model post a pre-deploy trouble report on the PR, and the GitHub Action can block the merge. A gate only fires on changes that ship through a PR, so production detection stays on for the causes that never touch your repo.
Quality you can delegate
Regressions surface as grader verdicts on real traffic and alerts in Slack or PagerDuty, so agent quality is a team default instead of a manual review you personally own.
Works with your existing stack
Already on Langfuse or Braintrust? Connect either as an upstream source and start from the traces you already collect. When a cause hunt hits a gap in those traces, that gap is the next node worth instrumenting, and the next hunt gets further.
Provider independence
Your agent can run across multiple model providers, and the reliability platform works across them and across trace stacks too.
One graph for monitoring and evals
Eval verdicts and production behavior live on one graph, versioned by commit. When a score drops, you walk straight from the failing grader to the production traces behind it, and to the change behind those.
Lineage you can audit
Every run ties back to what produced it: the commit, the prompt version, the tool version, and the model that served the turn. When the question about a broken flow comes to you, you point at the change, not guess.
Questions CTOs ask about agent reliability
Related pages
All Agent Reliability
The full loop: catch the drop in production and follow it to the cause.
Multiple agents in production
When several teams ship agents, quality ownership fragments. What a shared reliability layer looks like.
Reliability vs. LLM observability
Where tracing and dashboards stop, and where cause-finding starts.
Regression detection
The pre-deploy read: which production failures a PR puts at risk, before the merge.
Make agent quality a system, not a fire drill.
Land the traffic from your production agent, and the next time a flow degrades you get the cause named, in time to fix it before a customer writes in. Starting needs your traffic and no credit card.