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Grafana is applying a familiar reliability practice—service-level objectives (SLOs) and error budgets—to a harder question: whether an AI agent’s answers and actions are actually useful. The approach scores observable behaviour in conversations, messages or tool calls, gives each important behaviour its own target, and treats misses as a measurable budget for experimentation and corrective work.
Grafana Labs described the method on September 24, 2026. Its conceptual example targets 95% fulfilled conversations over 30 days, leaving a 5% error budget. In the documented product workflow, however, an online evaluation provisions an SLO in Grafana’s SLO application over a rolling 28-day window.
Why infrastructure metrics are not enough
An agent can respond quickly, avoid infrastructure errors and still give an incorrect answer, use the wrong tool or fail to complete the user’s request. Latency, availability and error counts therefore describe the delivery system, not the quality of the behaviour delivered through it.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Grafana’s method begins by defining behaviours that can be observed and evaluated. An evaluation produces a score or pass rate; that result becomes an SLO with a target. The difference between the target and actual performance is the error budget. Ivana Huckova of Grafana Labs describes the idea this way: “Evaluations turn behavior into a number, the number gets a target, and the gap becomes a budget: permission to experiment, an early warning for regressions, and a shared language a PM and an on-call engineer can both speak.”
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Measure each behaviour separately
Grafana warns against hiding every quality signal inside one blended score. Groundedness, fulfillment, toxicity, latency and cost can fail independently, so each material behaviour should have its own objective and budget.
| Behaviour or signal | Evaluation approach | What the separate objective reveals |
|---|---|---|
| Groundedness | Model-based judge | Whether the response is supported by the available information |
| Fulfillment | Model-based judge | Whether the conversation achieved the user’s requested outcome |
| Toxicity | Model-based judge | Whether generated content crosses the team’s safety threshold |
| API-key leakage | Deterministic evaluator | Whether a known secret pattern appears in output or tool data |
| Prompt-injection resistance | Model-based judge | Whether the agent resists adversarial instructions |
| Latency and cost | Operational measurements | Whether responsiveness and spending remain within their targets |
Deterministic checks are appropriate when the failure has a precise pattern, such as a leaked API key. Model-based judges can assess less mechanical properties, including groundedness, fulfillment, toxicity, privacy leakage and resistance to prompt injection. Grafana Cloud Agent Observability supports both kinds of checks.
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How the error budget governs changes
The 95% fulfillment example
Grafana’s illustrative target is 95% fulfilled conversations over 30 days. Five percent of conversations may therefore miss the target before the budget is exhausted. This is a conceptual example, not a claim about a universal production threshold. Teams must set targets that reflect the risk and purpose of their own agent.
Experiment while budget remains
A remaining budget creates explicit room to try a new prompt, model or feature. The team is not pretending that failures are impossible; it is deciding in advance how much failure is acceptable while learning.
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Investigate as the budget burns
When the budget is being consumed quickly, changes should slow and the team should investigate. The important alert is the burn rate and its trend, rather than a single bad conversation. Slow quality drift can accumulate quietly; tracking the budget makes that accumulation visible.
Grafana also describes alerts for operational objectives such as p95 latency and estimated daily cost. Those signals complement behavioural SLOs rather than replacing them.
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How Grafana provisions an online-evaluation SLO
The documented workflow connects an evaluation to Grafana’s SLO tooling instead of leaving the result as an isolated test report.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Choose an observable behaviour. Define what counts as success for a conversation, message or tool call, such as a fulfilled request or grounded answer.
- Select the evaluator. Use a deterministic check for a known pattern or a model-based judge for qualities that require contextual assessment.
- Run the online evaluation. The evaluation assigns its result and labels to the observed agent activity.
- Provision the SLO. Grafana creates an SLO in the SLO application for the evaluation, using a rolling 28-day window in the documented implementation.
- Watch performance and burn rate. Follow the target, misses and trend over time; use alerts for deteriorating quality rather than reacting only to individual transcripts.
- Replay known conversations before release. Grafana recommends replaying representative, previously seen conversations before shipping prompt or model changes. This checks whether the proposed change remains inside the established targets.
What Agent Observability puts in one workflow
Grafana describes Agent Observability as tracing an interaction end to end: from the user request through planning, tool invocations and LLM calls to the final response. The same workflow records the context needed to diagnose a regression.
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- Agent versions and prompt changes
- Tool usage and individual calls
- Quality scores from evaluations
- Latency and errors
- Token usage and estimated costs
- Conversation inspection
- Online evaluations and guards
- Offline experiments
The product documentation’s stated purpose is to “understand agent behavior from development through production.” That development-to-production span matters because a prompt can pass a small offline test and still behave differently when tools, live data and changing user requests are introduced.
Framework integrations
Grafana lists integrations for LangChain, LangGraph, OpenAI Agents, LlamaIndex, Google ADK and the Vercel AI SDK. The practical value is that teams can apply the same tracing and evaluation concepts across supported agent stacks instead of redesigning their quality instrumentation for each framework.
How usage is billed
Grafana describes usage billing across three dimensions:
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- Generations produced by monitored agents
- Tokens consumed by LLM-based evaluations and guards
- Standard telemetry emitted by the software development kits
There is no single universal price in the cited product material because the amount charged depends on usage. Teams estimating cost should account for both the agent’s own generations and the additional model calls made by evaluators and guards, as well as ordinary telemetry volume.
A practical rollout plan
- Start with a small set of high-risk behaviours. Fulfillment, groundedness, secret leakage and prompt-injection resistance are more actionable starting points than an all-purpose quality score.
- Write an explicit success rule for each behaviour. The evaluator needs a definition that can be applied consistently to the relevant conversation, message or tool call.
- Keep quality and operations distinct. Track behaviour SLOs alongside latency, errors and cost so a fast but wrong agent cannot appear healthy.
- Set a target and window. Record whether a figure is a conceptual planning target, such as 95% over 30 days, or the 28-day rolling window used by the online-evaluation workflow.
- Attach labels and inspect failures. Version, prompt and tool labels make it possible to connect a budget burn to a specific change.
- Use replay as a release gate. Run known conversations against the candidate prompt or model before production rollout.
- Define the response to burn. Decide in advance when to pause experimentation, roll back a change or investigate a tool, prompt or model.
What this announcement does—and does not—establish
Grafana’s September 2026 material establishes a product approach for turning agent evaluations into SLOs and error budgets, with tracing, online and offline evaluation capabilities. It does not provide an independent benchmark or a customer-outcome statistic proving that one target or evaluator works universally. The useful decision is therefore not whether every agent should copy the 95% example, but which behaviours matter for a particular agent and how much failure its users and business can tolerate.
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