Observability and LLMOps roadmap

Running an AI system in production: seeing what it did, knowing when it degrades, and shipping changes to it safely. A model doesn't throw an exception when it starts being wrong - detecting silent failure is the whole discipline.

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The boundary with its neighbours: evaluation is measuring whether output is good, inference is making the model fast, and this is operating the system around both. Section 2 is the foundation - if you can't reconstruct a single request end to end, nothing else in here will work.

1

What Makes This Different

Standard observability assumes failure is visible. Here the system succeeds mechanically while failing at its job, which changes what you instrument and what you alert on.

          2

          Tracing

          The foundation. Everything else in this roadmap assumes you can reconstruct a request end to end.

                      3

                      Logging and Data Handling

                              4

                              Metrics

                                          5

                                          Monitoring and Alerting

                                                      6

                                                      Learning From Production

                                                      The loop that makes a system improve rather than merely be observed. Most teams instrument and then never look.

                                                                  7

                                                                  Versioning and Release

                                                                  In an AI system the deployable unit is not just code. Prompt, model, tools and retrieval config all change behaviour, and all of them need versions.

                                                                          8

                                                                          Incident Response

                                                                                  9

                                                                                  Reliability Engineering

                                                                                              Instrumenting from nothing

                                                                                              In this order. Teams that start with dashboards end up with charts nobody can act on.

                                                                                              1. Trace one request completely. Every model call, tool call, retrieval and their inputs and outputs, tied together by one ID. Until this exists you are debugging blind.
                                                                                              2. Make traces searchable. By user, session, status, and latency. A trace you can't find is a log file.
                                                                                              3. Add cost and tokens per span. Cheap to record, and the first thing anyone asks when the bill arrives.
                                                                                              4. Capture feedback with the trace attached. A thumbs-down without the trace is an anecdote.
                                                                                              5. Sample and read traces weekly, by hand. Automated metrics tell you what changed; reading traces tells you why.
                                                                                              6. Alert on the things you'd be embarrassed to learn from a user. Error rate, cost spikes, latency, refusal rate.
                                                                                              7. Wire failures into the eval set. This is the loop that makes the system improve rather than just stay observed.

                                                                                              Then version everything together - prompt, model, tools, retrieval config - so that when quality moves you can say what changed.

                                                                                              The characteristic AI incident has no stack trace and no error rate spike. The system ran successfully, cost what it usually costs, and produced something confidently wrong for three weeks. Everything in this roadmap exists to shorten that three weeks.