AI Observability Checklist

An AI system that is up, fast, and error-free can still be confidently wrong, quietly expensive, or drifting away from the world it was evaluated on. Observability that includes quality, cost, and drift is what makes AI operable. This checklist covers what must be in place. It is the operational form of the AI observability whitepaper and complements how to monitor ai in production and ai agent observability.

Who should use this checklist?

Engineering owners and platform teams operating LLM applications and agents, and business owners and governance functions who depend on the signals.

Are traces complete?

Trace element	Captured?
Request identity, tenant, session, feature
Inputs after redaction
Query processing: rewrites, routing decisions
Retrieval: sources, chunks retrieved and reranked, permission filters applied
Model calls: model and prompt versions, parameters, prompts, outputs, tokens, latency
Tool calls: tool, arguments, result summary, latency, errors
Validation outcomes: schema, policy, content checks
Gate decisions: confidence, decision, approver, latency
Final output or action and effect
Cost per step and total
Trajectory linkage for multi-step agents

Reference: how to build an llm gateway and how to build an ai audit trail.

Are operational metrics live?

1. Volume by feature and tenant.
2. Latency percentiles per step and end to end against budgets.
3. Error, timeout, and retry rates.
4. Cache hit rate and fallback activation rate.
5. Escalation and gate rates; approval queue wait time.
6. Tool call success rate.

Reference: what is latency in ai systems.

Are quality signals captured?

1. Sampled automated scoring of live traffic with the same graders as the golden set.
2. Scheduled human review of stratified samples weighted by consequence.
3. User feedback explicit and implicit.
4. Downstream outcomes where available.
5. Quality reported per category and per version.

Reference: the AI evaluation and testing whitepaper.

Is cost attributed?

1. Tokens and cost per request from versioned pricing tables.
2. Attribution to feature, tenant, application, model, and step.
3. Retrieval, hosting, and review costs included.
4. Budgets and anomaly alerts on spend.
5. Unit economics joined with quality and volume.

Reference: how to build an ai cost dashboard.

Are drift indicators in place?

1. Baselines from launch and the golden set.
2. Input drift: topic, language, length, format distributions.
3. Confidence drift and escalation rate changes.
4. Output drift: category distribution, refusal rates, citation patterns.
5. Quality drift: sampled scores and review pass rates.
6. Dependency drift: provider model versions, index age, source schema changes.
7. Alerts on meaningful shifts, confirmed by review before action.

Reference: what is model drift.

Are agents observed at trajectory level?

1. Trajectory view showing steps, decisions, branches, and handoffs as one record.
2. Behavioral metrics: steps per task, tool patterns, loop detection, cost per task.
3. Safety telemetry: validation failures, blocked actions, unusual tool sequences, permission denials, flowing to security.
4. Outcome attribution to the step where failures originate.

Reference: the AI agent security architecture whitepaper.

Is alerting actionable?

Alert	Routed to	Runbook exists?
Error, timeout, or fallback rate breach	Engineering on-call
Latency budget breach	Engineering on-call
Cost anomaly or budget threshold	Engineering owner and finance
Sampled quality below threshold in any category	Business and engineering owners
Drift indicator beyond baseline	Business and engineering owners
Safety validation failures or blocked-action spike	Security and engineering
Approval queue wait above target	Business owner
Provider model version change detected	Engineering owner (triggers evaluation)

Are privacy, retention, and access handled?

1. Redaction or tokenization of sensitive content at capture.
2. Access control on trace stores with access logging.
3. Retention aligned with record-keeping and minimization obligations.
4. Residency requirements met.
5. Separation of operational telemetry from raw content.

Reference: ai data leakage prevention and ai data residency.

Does observability feed evaluation and governance?

1. Production failures are triaged into the golden dataset.
2. Quality, incident, cost, and autonomy metrics flow to the agent register and governance reviews.
3. Evidence for autonomy-level changes comes from observability data.
4. Finance reads the same cost numbers as engineering.

Reference: the agentic AI governance whitepaper.

How should gaps be prioritized?

Traces with redaction first, because everything else derives from them. Then operational metrics and cost attribution, which are cheap once traces exist. Then quality sampling and human review, which is the single largest improvement in control. Then drift indicators, alert routing with runbooks, and integrations.

How FISTA Solutions implements observability

FISTA Solutions instruments every LLM application and agent to this checklist: gateway-captured traces with redaction, operational metrics, quality sampling with calibrated graders, cost attribution, drift baselines and indicators, trajectory views for agents, alerts routed to owners with runbooks, and feeds into evaluation and governance. The AI enablement practice delivers the shared observability layer, AI agents inherit it, and forward deployed engineers integrate it with your monitoring, security, and finance tooling. The record behind the approach is 150+ projects with 99.9% uptime.

To assess observability on an existing AI system, message FISTA on WhatsApp, or read ai evaluation vs ai monitoring for how the two disciplines relate.