Agent Reliability

Every agent gets an owner. Reliability doesn't.

Your support agent's quality dropped Tuesday. Its code didn't change; the routing agent upstream did. Once a team runs multiple agents in production, each one gets its own owner, thresholds, and deploy gate, and no one can see a cause that crosses agent boundaries. Tessary watches every connected agent on one graph and traces a drop to the change that caused it, in whichever agent it happened.

Find what broke your agent

The shift

40% already run more than one agent

Zapier's 2026 State of AI Agents survey found that 40% of enterprises already running AI agents have multiple agents in production, not one. Each of those agents tends to ship with its own owner, its own thresholds, and its own channel, and the causes of a degradation don't respect any of those boundaries.

Zapier 2026 State of AI Agents survey ↗

40%

of enterprises running AI agents already have multiple in production

What breaks first

Alert fatigue compounds per agent

Every agent tends to ship with its own owner, and every owner sets their own alert thresholds. Add a second agent with its own channel and its own thresholds, then a third, and on-call has to work out whose alert fired before they can even decide whether to act on it.

77%

of on-call teams already get at least ten alerts a day, inside a single system

NeuBird AI, 2026 State of Production Reliability Report

57%

say fewer than 30% of those alerts are worth acting on

NeuBird AI, 2026 State of Production Reliability Report

Two ways to scale reliability

Rebuilt per team, or shared once

The same gap opens at deploy time. A pull request that touches a tool wrapper shared by two agents usually gets gated by only one team's risk model, because the second agent's failure modes were never in scope for whoever wrote the check first.

Per-team tooling

What happens by default

Each agent ships with its own owner, thresholds, and alert channel
A grader like a tool-error pattern gets rebuilt from scratch by every team
A cause in one agent surfaces as a symptom in another team's dashboard, and nobody traces across the boundary

Tessary

Shared reliability graph

One set of graders, every agent

A grader defined once runs against every connected agent
Alerts still route to the channel the owning team actually watches
A drop in any agent traces back to the change that caused it, in whichever agent it happened

How it works

One graph, every agent's traces

Tessary ingests each agent's production traffic into one shared model, then ties every grader verdict and alert to the agent and the change that produced it.

One ingestion model

OTLP traffic or a pulled slice from Langfuse or Braintrust normalizes into Session, Turn, Trace, and Observation nodes, regardless of which framework or model provider produced it.

One grader, every agent

A grader defined once, a tool-error pattern, a hallucination check, a cost anomaly, runs against every connected agent instead of being rebuilt per team. Tessary reads each grader as a trend over time per agent, so a real drift surfaces even when the grader itself is imperfect.

Alerts keyed to agent and grader

Routes to Slack, PagerDuty, Sentry, or Linear, keyed to the specific agent and grader that fired, so on-call sees which agent needs it, not a wall of undifferentiated pages.

Change lineage

Every verdict traces back to the change that produced it: a commit, a prompt edit, a tool's shifted output, an upstream agent, or a model provider update that never appears in any diff. A pre-deploy check still reads which agent a pull request touches and gates that agent specifically, for the causes that do ship through one.

The failure mode, named

There is no centralized AI platform, and each team manages its own prompts, evaluations, and model integrations.

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FAQ

Questions about multiple agents

The causes stop respecting team boundaries. With one agent, a degradation usually traces to a change that team shipped. With several, an agent can degrade because another agent feeding it shifted its output, and each team watches only its own dashboards and its own diffs, so nothing the affected team can see explains the drop. Tessary reads the chain across agents: it catches the degradation from the grader trend, then traces it back through the handoffs to the change that caused it, whichever team shipped it.

The individual agent teams still own their own product decisions, but reliability infrastructure, the graders, alert routing, and pre-deploy risk gating, works better as a shared layer rather than something each team rebuilds. Each agent's owner keeps the intent for what their agent should do; the shared graph is what engineering runs underneath, so every owner learns what caused a drop in their agent, not just that a score moved. A grader defined once (a tool-error pattern, a hallucination check) applies to every connected agent, and alerts still route to the team that actually owns the response.

Alert fatigue compounds when each agent has its own thresholds tuned independently, because near-duplicate alerts from related agents land in different channels with no way to tell they're connected. Routing alerts by agent and signal, on a shared detection layer instead of per-team tooling, means an on-call engineer sees which agent and which signal fired instead of triaging a wall of undifferentiated pages.

Yes, if the platform ingests traces in a provider-agnostic format rather than one tied to a specific framework. Tessary takes production traffic as OTLP in gen_ai OpenTelemetry conventions, or pulls a slice from an existing Langfuse or Braintrust source, and normalizes it into one Session, Turn, Trace, Observation model regardless of which model provider or agent framework produced it. Langfuse and Braintrust will show you each agent’s scores moving; Tessary reads those same traces to tell you which change, in which agent, moved them.

It's a single data model where every agent's production traces and every grader's verdict live together, instead of each agent having its own siloed monitoring setup. A grader that catches a failure mode on one agent can be checked against every other connected agent without redefining it, and every verdict traces back to the agent and the change that produced it, including changes that never appear in a diff, like an upstream agent's shift or a provider model update.

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Connect two agents' traces, with no credit card required, and the next time one of them drops you'll know which change, in which agent, caused it.