There’s a difference between monitoring and observability. Monitoring tells you that latency spiked at 14:07. Observability lets you answer the questions leadership actually asks: Which user? Which model and version? What context was retrieved? Which guardrails fired? Did a human approve the outcome? When systems are probabilistic and context-rich, those answers don’t fall out of generic web metrics—you have to design for them.

Start by writing the questions you’ll need to answer later

Before you choose tools, list the uncomfortable prompts you’ll face in incidents, audits, and board reviews. Example: “Why did the assistant send that paragraph externally?” “Which data sources influenced this recommendation?” “How often do we abstain because evidence is thin?” Designing logs becomes easier when they exist to answer concrete questions, not “capture everything just in case.”

Model your events like a story, not a firehose

Treat one user request as a narrative with named chapters: request received, sanitization, retrieval, generation, tool use, human review, final action. Assign a trace ID that follows the request across services. Each chapter records who/what acted, inputs and outputs (summarized where necessary), decisions taken, and timing. When you read it back, you should be able to reconstruct the event without phoning five teams.

Log payloads without logging secrets

Engineers prefer full payloads; regulators prefer restraint. The compromise: store hashed or tokenized versions of sensitive fields, keep format-preserving masks (so structure survives), and capture document references instead of raw text for retrieved context. Keep a sealed vault for rare, approved deep-dive samples with strict access controls and expiry. You want debuggability without a second copy of your crown jewels.

Capture the choices that change outcomes

For AI, the interesting parts are choices: model/provider and version, prompt/policy IDs, retrieval filters applied, documents selected, guardrails that blocked or rewrote content, and whether a human approved or edited the result. These aren’t “nice to have”––they explain behavior. If they’re missing, your post-mortems will devolve into guesswork.

Observability for retrieval is non-optional

Most “AI mistakes” originate in retrieval. Record which index answered, the scoring or re-ranking path, freshness of cited documents, and why candidates were excluded (entitlements, sensitivity labels, staleness). When a user disputes an answer, being able to show the exact passages—and why others didn’t qualify—is the difference between a fix and an argument.

Evaluation belongs in production, not only in labs

Keep a small, rotating set of live canary prompts per use case that check groundedness, policy adherence, and schema compliance. Score them continuously against your last good baseline. When a model, embedding, or prompt changes, require the canary to meet or beat the control before traffic ramps. This converts “we feel it’s better” into evidence.

SLOs that mean something to the business

Publish SLOs at the service level—availability and latency at P95, yes—but add quality SLOs tied to harm: abstain when evidence is insufficient, <X% policy-violating outputs, >Y% citation accuracy on assisted answers, zero unapproved writes to systems of record. Report burn-rate against these just like you do for uptime. Reliability isn’t only time and errors; it’s also not doing the wrong thing.

Make incidents legible in one page

When something breaks, responders shouldn’t spelunk. Standardize an incident view that shows: affected use cases and tenants, current SLO burn, top error classes, provider status, and the last three changes (model/prompt/index). Include a “degrade now” button that switches to safe fallbacks. Afterward, attach the annotated trace from a representative request to your post-mortem so the narrative is preserved, not re-imagined.

Privacy reviews get easier when telemetry is opinionated

If you can state, in writing, which fields are stored, masked, or dropped; who can see what; and how long anything persists, approvals speed up. Pair your event schema with a retention schedule (e.g., raw traces 7 days, summaries 90, metrics 13 months) and stick to it. Nothing builds trust faster than deleting on purpose.

Don’t bury stakeholders in dashboards

Executives need a single page: volume by use case, cost per task, SLO adherence, abstain rate, incident count and severity, and a short note on “what changed.” Engineers need traces and payload views keyed by trace ID. Auditors need exportable logs for a bounded period with a data dictionary. Three audiences, three products—resist the urge to make one page that pleases nobody.

Build vs. buy: choose control over convenience

Buy the plumbing (distributed tracing, log storage, metrics) and integrate it with your SIEM. Build the event schema, redaction rules, and quality evaluators that reflect your business and risk posture. Keep the interface between your AI services and the telemetry layer thin and versioned so you can evolve either side without rewrites.

Closing Thoughts

Observability is your credibility. If you can show what happened, why it happened, and how you’ll keep it from happening again, you earn the right to move quickly. Design logs as answers to specific questions, treat retrieval as a first-class citizen, and put quality signals next to latency and uptime. Do that, and you won’t just resolve incidents faster—you’ll make approvals and audits boring, which is the highest compliment an AI program can get.