Agent observability, down to the cause
If your agent emits OpenTelemetry gen_ai spans, or your team already runs Langfuse or Braintrust, Tessary reads the traces you already capture without touching your agent code. It catches degradation as it happens and traces it back to the change that caused it.
Most setups stop at tracing
LangChain's State of Agent Engineering survey (2025, 1,340 respondents) found that 89% of teams had implemented observability for their agents. Among those running agents in production, 71.5% had full tracing in place. Tracing, in other words, is mostly solved. What most setups still miss is the step after the dashboard: graders that catch a degradation on real traffic, and a way to say what changed when one fires.
LangChain State of Agent Engineering, 2025 (1,340 respondents) ↗89%
of AI teams had some form of agent observability in place
71.5%
of production teams had full tracing in place
Build vs connect
Most teams face two options when adding more signal to a production agent. Both require significant work. Neither is required if your agent already emits gen_ai spans or your team runs Langfuse or Braintrust.
Build from raw spans
Write queries and maintain thresholds by hand
- Hand-write span queries for each failure pattern you want to track
- Maintain thresholds manually with no judgment layer for what changed
- Watch dashboards with no automation to detect behavioral regressions
- Ongoing engineering maintenance as the agent and model evolve
Connect Tessary
Detection and cause-finding over what you already ship
- Point your OTLP exporter or connect Langfuse or Braintrust in minutes
- Tool-error auto-extraction runs without any signal definitions
- Write failure patterns in natural language, not span queries
- Alerts carry the suspected cause, not just the failing trace
From first trace to first cause
Tessary accepts production traces two ways. Neither requires changes to your agent code.
Connect your source
Point an OTLP exporter at the receiver on gRPC port 4317 or HTTP port 4318. If your agent framework emits OpenTelemetry gen_ai spans natively, that is the only step required. Your agent code stays unchanged.
Pull from an existing source
If your team already runs Langfuse or Braintrust, connect either as an upstream pull connector. Authenticate the source, select the trace slice you want, and pull on demand. No framework migration required.
Detection runs automatically
Once traces land, Tessary assembles sessions, turns, and tool calls into one graph of the agent's behavior. Tool-error extraction runs without any configuration, and you can add graders written in natural language.
Alerts arrive with the suspected cause
When a grader trend drops on live traffic, Tessary traces the failing quality dimension back through the agent's chain and attaches the change that lines up with the drop: a prompt edit, a tool update, a dependency bump, or an upstream shift. The alert routes to Slack, Sentry, Linear, PagerDuty, or a webhook.
Signal beyond error rates
Graders over real production traces surface failure modes that error rates and latency metrics cannot see.
Tool call failures under HTTP 200
Tool calls that return a well-formed HTTP 200 but carry a failure payload are flagged automatically. No signal definition required for this failure mode.
Reasoning sequences with wrong output
An agent that completes a reasoning sequence but returns an incorrect result passes error-rate checks. Graders over the session catch this where latency and error metrics cannot.
Hallucinated facts in tool results
When a tool result contains facts the underlying model fabricated, graders flag the session. Output-level metrics miss this because the call completed successfully.
Model version regressions
Behavioral shifts that follow a model upgrade, a non-prompt code change, or a dependency bump appear as session-outcome changes. Graders detect them against the same production records.
Multi-agent coordination failures
In pipelines where agents hand off work to each other, coordination failures show up at the session level. Graders trace the full handoff sequence, not individual agent responses.
Pre-deploy risk prediction
The same attribution engine runs before a merge: a report on the PR names which failure patterns the changed code is most likely to affect, and a GitHub Action can hold the merge past your threshold. Production detection stays on, because a CI gate only covers changes that ship through a PR; a provider model update or an upstream shift can reintroduce a failure with no deploy at all.
Built on the OpenTelemetry GenAI semantic conventions
The gen_ai semantic conventions define standard span shapes for LLM calls, agent reasoning, tool invocations, and session-level metrics. Most agent frameworks emit these spans natively, so a platform that accepts OTLP can read them without additional instrumentation.
OpenTelemetry GenAI semantic conventions ↗Questions about observability setup
Related pages
Agent monitoring and alerting setup
Routing, digests, and threshold rules for the team that owns the pager.
Langfuse to Tessary reliability signals
Connect an existing Langfuse project and get causes from the traces you already capture.
Agent evals SDK
Capture the nodes your traces miss when a cause hunt needs more depth.
From setup to answers
Connect your Langfuse or Braintrust source, or point an OTLP exporter, and the first traces land in minutes. The next time your agent degrades, you get the change that caused it, with no credit card to start.