Agent Reliability

When your agent gets worse, know what caused it.

AI agents change constantly, and they degrade for reasons no diff shows. Tessary watches your agent in production, catches degradation as it happens, and traces it back to the change responsible, fast enough to fix it before it does damage.

Find what broke your agent See the ingest options

The index · 23 pages

Every reliability page, grouped by the job it does.

Detect in production

See degradation as it happens.

Agent observability setup Wire OTLP or your existing traces into a reliability layer that catches degradation and names its cause. Monitoring and alerting Alerts that carry the suspected change, not just the score that moved. Behavior drift Catch an agent drifting over weeks, including drift no deploy explains. Failure replay Land on the failing turn with the responsible change already identified. Multiple agents in production One reliability graph across agents, so an upstream shift shows up as the cause it is.

Predict before deploy

The same engine, run before the merge.

Regression detection Detect a real regression from noisy graders, then trace it to the change that caused it. Pre-deploy failure prediction The same attribution engine pointed at the next PR before it merges. Continuous agent evaluation Evaluation that runs on production traffic instead of a frozen test set. Evals that stay current Graders drafted from observed behavior that sharpen as bugs get fixed. Evals without labeled data Start grading real traffic without hand-labeling a dataset first. Task completion graders Measure whether the agent finished the job, and find out what changed when it stops finishing.

By agent type

Coverage shaped to your agent.

Tool-calling agents Grade each tool call, and catch a tool whose API changed underneath your agent. Code-generation agents Coverage for agents that write code, from PR gates to production trends. RAG pipelines Stage-level grading that names the re-index, embedding swap, or chunker change behind a slide. Multi-turn agents Session-level graders for failures that only appear across turns. Multi-agent systems Watch the handoffs, and trace agent B’s drop to agent A’s shift three turns upstream. Multimodal agents Perception and action graders for agents that read screens and images. Multi-provider agents One eval layer across providers, including silent provider-side model updates.

Integrate and compare

Start from the stack you have.

Connect Langfuse Pull a slice of your existing Langfuse traces. No re-instrumentation to start. Ingest and SDKs OTLP receiver plus TypeScript and Python SDKs for capturing the agent’s nodes. Tessary vs. Langfuse and Braintrust Score tracking and cause finding are different jobs. Where each tool stops. Agent reliability vs. LLM observability How cause-finding differs from watching dashboards and reading traces. For CTOs The case for reliability tooling when you own the agent and the pager.

Questions

Agent reliability, plainly.

What is agent reliability?

Agent reliability is keeping an AI agent working correctly in production as it changes. Agents ship fast and change often, and they degrade for reasons that are hard to see: a prompt edit, a dependency bump, a tool returning different data, or a model provider update. An agent reliability platform detects the degradation as it happens and traces it back to the change that caused it.

How is this different from running evals?

Evals score outputs. That tells you a score moved, not why it moved. Tessary runs graders over production traffic, reads the trend to detect real change even when graders are imperfect, and then traces a failing quality dimension back through the agent’s chain to the responsible change. The scoring is the setup; naming the cause is the product.

Where should I start?

If your agent already sends traces to Langfuse or Braintrust, connect it as a source and start without re-instrumenting. Otherwise the SDK captures your agent’s nodes via OpenTelemetry; with a coding agent the setup takes hours. Either way, the first grader trends appear on real traffic, not on a dataset you have to build first.

Find out what caused your last regression.

Connect a Langfuse or Braintrust source, or start with your repo. Grader trends run on your real traffic, and when one drops, Tessary names the change behind it. No credit card required.

Find what broke your agent

Already instrumented? Pull a trace slice from Langfuse or Braintrust without re-instrumenting.