# Grafana Agent Observability links live agent telemetry to evals and CI regression gates Grafana Cloud’s Agent Observability is now generally available with a workflow that connects production conversations, token/cost telemetry, online evaluators, failure collections, offline experiments and CI/CD checks. The useful shift is from dashboards about agents to a repeatable observe–test–release loop. Grafana’s GA agent-observability stack can track latency, tokens, cost and conversations, score live traffic with deterministic or LLM-based evaluators, route failures into test collections, compare experiments and use those results as pull-request gates. Evaluator quality and instrumentation coverage remain the main limits. - Status: Active - Published: 2026-08-21T16:57:23+12:00 - Updated: 2026-08-21T16:57:23+12:00 - Categories: Artificial Intelligence, Cloud & Infrastructure, AI Agents, Observability - Tags: AI agents, AI evals, Grafana, Observability - Canonical HTML: https://beyondthe.news/dossiers/grafana-agent-observability-ga-evals-ci ## What changed Grafana Labs made Agent Observability generally available in Grafana Cloud on July 30. The product goes beyond a purpose-built agent dashboard: instrumented agents can surface latency, token usage, cost, errors and conversation traces; deterministic and LLM-judge evaluators can score live traffic; evaluator outputs become Grafana metrics that can drive dashboards and alerts; failed or interesting conversations can be collected into test suites; and offline experiments can compare agent versions before a change is merged. Grafana’s documented workflow explicitly connects production failures back into regression tests and shows experiments being used as CI/CD gates. ## Why it matters Agent teams often split production telemetry, qualitative conversation review and offline evals across separate systems. That makes it easy for the testing corpus to drift away from failures customers are actually seeing. Grafana’s stronger proposition is the feedback loop: observe real traffic, identify failure patterns, turn those conversations into test cases, compare a proposed agent version and gate deployment on the result. This is especially useful to builders already using Grafana because evaluator scores become ordinary Cloud Metrics and can participate in the same alerting and operational workflows as latency or error rate. The limitation is equally important: an automated judge is another model, not ground truth, and bad instrumentation or narrow criteria can make a polished dashboard look safer than the agent really is. ## The production layer includes cost and conversation behavior Agent Observability can track conventional engineering signals such as latency, errors, token consumption and model cost alongside instrumented conversation traces. Grafana argues that both are necessary: aggregate metrics expose cost or reliability drift, while individual conversations provide the context needed to understand why an agent went wrong. ## Evaluators turn behavior into alertable metrics Teams can apply deterministic checks such as regex or structured validation and LLM-judge evaluators to live traffic. Rules can run over a full conversation or selected messages/tool calls, including checks for topics such as PII exposure, toxicity or custom fulfillment criteria. Evaluator results are published into Grafana Cloud Metrics, so declining scores can feed dashboards and Grafana Alerting rather than living only inside a separate eval product. ## Production failures can become regression cases Grafana’s workflow lets teams collect low-scoring or otherwise interesting conversations, annotate them and add them to test suites. That creates a direct path from an observed customer failure to a case that future prompt, tool or harness changes must survive. The important practice is curating the collection rather than blindly turning every judge failure into a test. ## Offline experiments can be part of CI/CD The Agent Observability SDK can run a test suite against a candidate agent configuration, score the results and compare two runs using stable test-case identifiers. Grafana shows this as a pull-request gate: run an experiment, check for regressions, and attach the result before merging. The same system can compare model quality against cost, which helps when a cheaper or faster model is being considered for routing. ## Versioning closes the operational loop Once a change is deployed, Agent Observability can track the agent version alongside live behavior so a team can see whether the production distribution matches offline expectations and identify a rollback target. This is closer to ordinary software release engineering than one-off prompt evaluation: test evidence and production telemetry stay connected through the lifecycle. ## Judge quality is the main source of false confidence Grafana explicitly notes that general LLM judges can be unreliable and recommends custom evaluators plus deterministic checks where possible. Teams still need human review for high-impact behaviors, representative production sampling and outcome metrics that the judge cannot infer. The value of the platform depends on the quality of the instrumentation, rubrics and failure corpus put into it. ## Key details - Grafana Agent Observability became generally available in Grafana Cloud on July 30, 2026. - It tracks agent engineering metrics including latency, token usage, cost and errors alongside instrumented conversation traces. - Live traffic can be scored with deterministic checks and LLM-judge evaluators. - Evaluator results publish to Grafana Cloud Metrics and can trigger Grafana Alerting. - Low-scoring production conversations can be collected and promoted into test suites. - Offline experiments can compare agent/model versions and be used as CI/CD or pull-request gates. - Agent versions remain visible after deployment so production behavior can be compared with the evaluated change. ## Builder takeaways - Instrument agent cost, latency, tool/conversation behavior and version identifiers together; isolated token dashboards cannot explain why a quality change happened. - Build eval suites from real production failures and high-risk workflows, not only synthetic happy paths. - Use deterministic validators for properties that can be checked exactly and reserve LLM judges for genuinely semantic criteria. - Track judge quality itself by regularly sampling passes and failures with humans, especially before making an automated alert or deploy gate authoritative. - Compare agent changes on both quality and inference cost so model-routing or prompt improvements do not optimize one dimension invisibly at the expense of another. - Make a failed production case reproducible in an offline suite before closing the incident, so the same class of regression has a durable test. ## What to watch - Whether Grafana publishes pricing or usage mechanics specific to evaluator runs and stored agent conversations as adoption grows. - Independent evidence on how well its built-in and custom evaluators correlate with human assessments across different agent types. - Broader framework and OpenTelemetry-native instrumentation support that reduces bespoke SDK integration work. - Whether experiment results gain stronger native deployment/rollback integrations beyond being used as CI evidence. ## Uncertainties - Grafana’s GA workflow is described primarily through its own product and internal Assistant experience; independent evidence on large external agent fleets is still limited. - LLM-judge scores can be unstable or biased, and Grafana itself warns that generic evaluators may be unreliable. - The platform can only observe behavior that is instrumented and retained, so tool calls, hidden state or external side effects outside that telemetry can remain blind spots. ## Sources - [Say goodbye to black-box agents with Agent Observability](https://grafana.com/whats-new/) — Grafana Labs · primary · 2026-07-30T00:00:00+12:00. GA release record for Grafana Cloud Agent Observability. - [How to build a trust platform for your agent with Grafana Agent Observability](https://grafana.com/blog/how-to-build-a-trust-platform-for-your-agent-with-grafana-agent-observability/) — Grafana Labs · primary · 2026-07-30T00:00:00+12:00. Primary workflow description covering live traffic metrics, conversation inspection, evaluators, collections, test suites, experiments, CI/CD and version tracking.