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Set AI-agent alerts from a user-facing service-level objective (SLO), not from an arbitrary error percentage or average response time. Define the outcome users need, measure the complete agent trace, and derive thresholds for tail latency, task failures, model and tool errors, timeouts, retries, cost, and quality or safety regressions. Streaming agents need time to first token (TTFT) and inter-token latency; task agents need successful completion and end-to-end duration; batch agents need completion within a promised window. Use phase-level budgets, behavioral baselines, and fast- and slow-burn error-budget alerts so incidents page quickly without turning normal model variability into alert noise.
What should I alert on for an AI agent?
Alert on the signals that represent a failed user outcome and the signals that explain why it failed. A healthy HTTP status or infrastructure uptime does not prove that an agent completed the right task, selected the right tool, or produced a safe answer.
- User outcome: successful request rate for conversations, successful task-completion rate for workflows, or completion-within-SLA for batch jobs.
- Tail latency: p95 and p99 end-to-end completion time; TTFT and inter-token latency for streaming responses.
- Execution failures: model-call errors, tool-call errors, invalid tool arguments, workflow failures, timeouts, cancellations, retries, and fallback use.
- Quality and safety: groundedness or relevance, tool-selection correctness, hallucination or harmful-output rate, guardrail blocks, and human-escalation rate.
- Efficiency: token volume, cost per task, context size, and loop or retry counts.
Attach a trace to every alert whenever possible. Metrics tell you that a threshold was crossed; a trace containing model calls, tool invocations, phase timings, and policy decisions tells the responder what to fix.
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Start with a measurable target that combines a service-level indicator (SLI), a threshold, an attainment goal, a measurement interval, and an error budget. This is the structure described in AWS reliability guidance and prevents a latency or error alert from becoming disconnected from business impact.
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Choose the user-visible SLI
- Conversational streaming: successful request rate, TTFT, inter-token latency, and final completion latency. A response that starts quickly but stalls or never finishes is not fully successful.
- Task-oriented agents: successful task completion, end-to-end completion time, and the percentage of tasks requiring escalation or manual correction.
- Batch agents: completion within the promised window, throughput, cost per task, and the number of jobs that need reruns.
Set the attainment goal from impact and evidence
Use representative production traffic, customer expectations, and the cost of an incident to set the initial goal. Do not copy a generic number and call it an SLO: a support triage agent, a code-review agent, and a nightly extraction job have different acceptable delays and failure consequences.
Google Cloud’s 2025 guidance gives illustrative starting examples of 99.9% successful API calls, p95 inference latency below 300 ms, TTFT below 500 ms for 99% of requests, and a harmful-output rate below 0.1%. These are examples to adapt and validate against your workload, not universal requirements for every model or agent.
Convert the SLO into an error budget
If the target is 99.9% successful outcomes over an interval, the remaining 0.1% is the allowed failure budget for that interval. Burn-rate alerts compare the observed failure rate with the rate at which that budget may be consumed. A fast-burn alert catches an acute incident; a slow-burn alert catches a degradation that is too gradual to page immediately but would exhaust the budget over time.
Which latency percentile should I use: p50, p95, or p99?
Use p95 and p99 for alerting because tail latency represents the slow experiences that averages and p50 hide. Keep p50 for capacity and regression analysis, but do not make it the only paging signal.
| Measure | What it tells you | Typical use |
|---|---|---|
| p50 | The response time experienced by the middle request | Detects broad performance shifts and helps estimate normal capacity |
| p95 | The upper tail affecting one request in twenty | Primary latency SLO for many interactive paths when occasional outliers are tolerable |
| p99 | The extreme tail affecting one request in one hundred | Protects latency-sensitive users and exposes queueing, retries, and slow tools |
There is no single correct percentile for all agents. A customer-facing assistant may use p95 for a broad responsiveness objective and p99 for a stricter reliability objective; a high-value transactional workflow may page on p99 completion time because one stuck execution is costly. Choose the percentile together with the business consequence and review it after observing real traffic.
Measure the right clock
- TTFT: time from request acceptance until the first output token is available. It is the first responsiveness signal in a streaming interface.
- Inter-token latency: the delay between streamed tokens. A low TTFT does not help if generation then pauses repeatedly.
- Completion latency: time until the final answer or structured result is delivered.
- End-to-end task time: request start through verified task completion, including retrieval, planning, tool calls, retries, and post-processing.
- Batch completion time: elapsed time until the job reaches its promised completion state, including failed-item handling.
How do I split an end-to-end latency budget?
Decompose the total target into phase budgets so an exceedance points to a component instead of producing an unhelpful “agent slow” page. The phases should add up to no more than the user-visible budget, with explicit allowance for queueing and variability.
| Phase | What to time | Useful diagnostic dimensions |
|---|---|---|
| Context retrieval | Embedding, search, reranking, cache lookup, and context assembly | Index, query type, corpus, cache hit or miss, region |
| Model inference | Queueing, provider time, first token, generation, and finish reason | Model and version, region, route, input and output tokens |
| Tool invocation | Connection, server processing, response parsing, and retries | Tool name, operation, dependency, status, timeout class |
| Orchestration | Planning, state transitions, inter-agent messages, and backoff | Workflow, step, attempt number, branch, fallback path |
| Output | Validation, guardrails, formatting, and delivery to the client | Schema, policy decision, channel, payload size |
For each phase, record both a percentile latency and an error count. A tool can meet its latency target while returning malformed data; a model can be fast while triggering repeated retries. Phase budgets should therefore be paired with success and quality checks rather than treated as a replacement for the end-to-end SLO.
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Establish a baseline first, then set thresholds that reflect meaningful deviation and business risk. A threshold should be difficult enough to protect the SLO but stable enough that normal variation does not page the on-call engineer.
- Collect representative traffic. Segment by workload, geography, model, tool path, and time of day. Include cold starts, cache misses, long prompts, and known peak periods.
- Calculate distributions, not only averages. Store p50, p95, p99, maximum, and sample counts for each latency measure. A percentile based on a tiny sample should not trigger a major response without a minimum-volume rule.
- Measure the baseline outcome. Verify task completion with a business or automated evaluator; do not infer success from HTTP 200 responses.
- Set an initial SLO and budget. Document the target, interval, exclusions, and what consumes the budget. Record why the target is appropriate for the workload.
- Add a warning and a page policy. A warning can open a ticket or annotate a deployment; a page should require sustained budget burn or a severe immediate failure.
- Test the policy. Replay an outage, inject tool timeouts, force a model error, and introduce a latency spike. Confirm that the right alert fires and that the trace contains enough context to diagnose it.
Keep thresholds separate by model version, region, and critical tool when those dimensions have materially different behavior. A global average can hide a failing deployment serving only one region.
What failure-rate alerts should an AI agent have?
Use separate alerts for the user-visible failure and the internal causes. The separation prevents a provider error, a single broken tool, and a workflow bug from being collapsed into one undiagnosable percentage.
Request and task failures
Alert on unsuccessful conversational requests and on tasks that do not reach a verified completion state. Count explicit failures, cancellations, deadline misses, and human escalations according to the SLO definition. Break the metric down by workflow, model, version, region, and customer tier where those dimensions affect impact.
Model-call failures
Track provider or deployment errors, throttling, refused requests, malformed responses, context-limit failures, and unexpected finish reasons. Alert on the rate and on sudden changes in latency or token usage for each model and version so a regression is not masked by traffic moving to a fallback.
Tool-call failures
For every tool, measure success, timeout, retry, invalid-argument, authentication, rate-limit, and server-error rates, plus tool latency percentiles. A tool-specific page should include the operation, dependency, attempt number, and the agent workflows affected; tool failures frequently propagate into apparently unrelated task failures.
Workflow and retry failures
Count state-machine errors, exceeded step limits, loops, dead-lettered executions, and fallback activation. Alert on retry volume as well as final failure: a workflow that eventually succeeds after several retries may still be consuming its latency and cost budget.
How do I alert on quality and safety regressions?
Pair availability and latency alerts with evaluators that test whether the agent did the right thing. Microsoft notes that “Uptime and error rates are not good indicators of quality and reliability in AI systems.” An agent can be online, fast, and consistently wrong.
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- Task-completion quality: verify required fields, state changes, or downstream business outcomes rather than relying on textual similarity.
- Tool-selection correctness: check whether the chosen tool and arguments matched the user intent and policy.
- Hallucination and harmful-output rate: use sampled evaluations, red-team cases, and production safety classifiers.
- Guardrail blocks: watch both the absolute block rate and sudden increases that may indicate prompt drift or an over-sensitive policy.
- Escalation rate: distinguish appropriate escalation from an agent that is abandoning work because of a new failure mode.
Use a stable quality SLO for contractual reliability and anomaly detection for unexpected drift. Microsoft Foundry supports quality and harmful-content threshold alerts; the exact evaluator and threshold still need to be calibrated to your application and risk tolerance.
How should multi-window burn-rate alerts work?
Use at least one fast-burn policy for immediate incidents and one slow-burn policy for sustained degradation. The exact window lengths and burn-rate multiples depend on your SLO interval and incident process; the important design is that both policies measure consumption of the same error budget.
Fast burn
Page when a high failure rate or severe latency breach would consume a large share of the budget quickly. Combine the burn-rate condition with a minimum request or task count so a handful of test requests cannot page production.
Slow burn
Create a ticket or lower-severity alert when a smaller but persistent breach continues long enough to threaten the budget. This catches gradual prompt growth, retrieval degradation, model drift, or a rising retry rate that may not appear as a sharp outage.
Anomaly overlays
Add anomaly detection for latency spikes, token surges, guardrail blocks, cost per task, and output-distribution changes. Keep the SLO-based alert as the stable contract; anomaly alerts should identify novel behavior and should not replace an explicit user-outcome target.
What telemetry is required to diagnose an alert?
Correlate metrics, logs, and traces with request, session, and trace identifiers. OpenTelemetry’s GenAI semantic conventions provide a common schema; the current page lists semantic-conventions version 1.44.0 as accessed in 2026.
Capture on every trace
- Request, session, trace, and parent-span identifiers.
- Model name and version, provider route, region, prompt and completion token counts, finish reason, and model status.
- Tool name and operation, sanitized arguments, outcome, status code, timeout, retry count, and dependency timing.
- Phase start and end times for retrieval, inference, tools, orchestration, validation, guardrails, and delivery.
- Task identifier, workflow step, completion state, fallback path, and escalation decision.
- Quality-evaluator results, policy decisions, and links to the test or reference set used.
Apply privacy and retention controls before storing prompts, tool arguments, or outputs. Redact secrets and personal data, restrict access to sensitive traces, and control metric cardinality so adding a customer or prompt dimension does not make telemetry unaffordable.
What is a practical starting alert matrix?
The following policy patterns provide coverage without prescribing one universal number. Replace each threshold with the value validated against your baseline and SLO.
| Signal | Initial policy pattern | Why it matters |
|---|---|---|
| Successful request or task rate | SLO with fast- and slow-burn windows; segment by workflow, model, version, and region | Captures acute incidents and sustained degradation |
| p95 and p99 completion latency | Workload-specific percentile thresholds with minimum sample volume | Tail latency drives the slow experiences users notice |
| TTFT and inter-token latency | Streaming-agent thresholds by model and route | Measures perceived responsiveness before final completion |
| Model-call error rate | Separate alerts by model, version, and region | Distinguishes provider or deployment regressions |
| Tool-call error and latency | Per-tool success, timeout, retry, and latency thresholds | Tool failures propagate into agent failures |
| Task-completion rate | Quality SLO plus regression alert from an evaluator or verified outcome | Measures business success rather than transport success |
| Groundedness, harmful output, and guardrail blocks | Quality or safety threshold plus anomaly detection | An agent can be available while incorrect or unsafe |
| Tokens and cost per task | Budget and anomaly alerts, with loop and retry dimensions | Detects prompt bloat, runaway loops, and unexpected spend |
How do I tell a failing agent from a bad answer?
Classify the event along two axes: execution and outcome. An execution failure means the agent could not complete its workflow; a quality failure means it completed but produced an incorrect, ungrounded, or unsafe result.
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| Observed behavior | Likely classification | First diagnostic action |
|---|---|---|
| Timeout, provider error, or exhausted retries | Execution failure | Inspect phase timings, dependency status, and retry spans |
| HTTP success but required state change is absent | Task-quality failure | Check tool selection, arguments, validation, and workflow state |
| Answer conflicts with retrieved evidence | Grounding failure | Review retrieval results, context assembly, and model output |
| Guardrail blocks rise without traffic change | Safety or policy regression | Compare policy version, prompt changes, and representative samples |
| Completion succeeds only after repeated retries | Latent reliability and cost issue | Measure retry-added latency, token use, and dependency errors |
Use separate counters for these classes, then define the user-facing SLO so that the classes roll up to the outcome that matters. This preserves diagnostic detail without allowing a large volume of technically successful but unusable answers to look healthy.
When should thresholds be revised?
Re-baseline after any change that can alter the agent’s behavior: model or model-version updates, prompt and system-policy edits, retrieval-index changes, tool or dependency releases, orchestration changes, traffic mix, or a new region. Review both the threshold and the evaluator; a new model may change the distribution of answers even when latency is unchanged.
Turn stable criteria into delivery checks where practical. Run load tests for latency and concurrency, evaluation sets for quality and safety, and failure-injection tests for tool and provider outages. Block or require approval for a release that would consume an unacceptable portion of the error budget before production traffic arrives.
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Keep a change log explaining why each SLO, percentile, evaluator, and burn-rate policy changed. That history lets the on-call team distinguish real regressions from an intentional workload or product shift.
Frequently Asked Questions
Is there one good latency threshold for every LLM agent?
No. Streaming assistants, multi-step task agents, and batch workers expose different user-visible clocks, so each needs its own SLO. A threshold is credible only after measuring representative traffic and confirming what delay users and downstream systems can tolerate.
Should I page on every individual model or tool error?
Usually not. Individual transient errors should contribute to a rate, budget-burn, or retry alert unless they represent a critical dependency or a known severe incident. Keep the raw event and trace for diagnosis while paging on sustained impact.
How many traces should an AI agent retain for quality monitoring?
Use full traces for failures, sampled traces for normal traffic, and a separate, access-controlled sample for quality evaluation. Sampling rates should be high enough to represent rare workflows and safety events, with retention limited by privacy, regulatory, and cost requirements.
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Can anomaly detection replace an SLO for agent monitoring?
No. Anomaly detection is useful for novel drift in tokens, latency, guardrail blocks, or outputs, but it has no fixed user-impact contract. Keep an explicit SLO and error budget, then use anomalies to find problems that the SLO has not yet captured.
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