Observability

The question

Are we recording what our agents do: the prompts, the responses, the tool calls, the cost?

The question

When we want to look, is the data actually queryable, or buried where no one can reach it?

The question

When something goes wrong, can we reconstruct exactly what the agent did and why?

Within Observability

• Capture and Trace

  A trace is only ever a reading of what Capture bothered to write down: reconstruct a decision and you are replaying the record, not the run, so reasoning-level Trace demands reasoning-level Capture. Instrument only the prompts and the tool calls and the trail can say what happened but never why, which is the half of an incident that actually costs you.

  Invest in both and a cold case reopens down to the judgment behind it. Under-fund Capture and Trace becomes a courtroom with no evidence: the event on the record, the reason for it gone.

• Availability and Trace

  Availability and Trace answer opposite questions of the same record. Availability reads across every run at once while Trace goes vertical on one of them, the full walk of a single case down to the reasoning behind it.

  You need both postures, because a query that flags a spike in failures cannot tell you why any single one broke, and reconstructing that one broken run cannot tell you whether it is a fluke or a pattern. Fund only breadth and you are watching a trend you cannot diagnose; fund only depth and you are explaining a case you cannot place.

• Capture and Availability

  Capture writes the record; Availability decides whether anyone can ever ask it anything. A session logged and then sealed away is the worst line on the bill: you paid for the instrumentation and the storage and bought back nothing, because the answer is technically in there and functionally gone.

  The two only pay off together: capture wide, then make it reachable. Log nothing and there is nothing to ask; log everything into a pit and you have funded a haystack no one will search.

Where Observability reaches across

• Delegation and Trace

  ↗Control

  You can only safely hand over a decision you are able to reconstruct later. Delegation is how much you let the agent settle on its own; Trace is what keeps those solo calls something you can still answer for when one goes wrong.

  Push the line out past what you can trace and every autonomous decision becomes a call with no path back to the reasoning behind it, which is exactly the call an incident or an auditor will make you explain. So the honest ceiling on how far you delegate is how deep you can reconstruct: widen the line and the record together, or the reach you handed over is reach you cannot account for.

• Trace and Convergence

  ↗Improvement

  A reconstruction tells you exactly what went wrong, and by itself changes nothing about what happens next. Trace is the practice that produces the finding: the walk back through a run to the decision that broke it. Convergence is the practice that turns that finding into something the whole team now does differently.

  Same evidence, two jobs: fund the first alone and you accumulate immaculate postmortems on incidents you are still on course to repeat, because a lesson learned in one investigation is not a lesson the team has adopted until someone makes it spread.

• Capture and Evaluation

  ↗Improvement

  Capture supplies the raw record of what the agent did; Evaluation is the judgment of whether it was any good. The record is the evidence Evaluation reasons from: with nothing captured to compare against, a claimed improvement is just a better-feeling week, and the evals fire on data someone had to record first.

  But a full log still cannot render the verdict, because good is judged against a standard the record does not contain: the rubric, a senior engineer's read, whether the output served a customer the log never saw. Capture the work without ever judging it and you are archiving activity no one grades; judge with no captured record and you are grading from memory, the exact feeling Evaluation exists to replace.

• Availability and Prioritization

  ↗Improvement

  Prioritization runs on the signal it can actually pull, and Availability is what decides whether that signal is reachable at all. A queryable record answers the questions a sound investment call rests on: where the spend is piling up, which failures recur, what a workflow really costs the team.

  Leave the record unreachable and Prioritization has nothing to read, so it quietly falls back to whoever argues hardest, the very failure mode both disciplines are built to prevent. Make the record queryable and the next bet gets chosen from evidence anyone can check, not from the meeting's center of gravity.

• Collection and Capture

  ↗Context

  Collection and Capture are one instinct aimed in opposite directions: Collection gathers what an agent needs before a run, Capture records what it did after. They rhyme because the record Capture writes down (the decision made, the reasoning behind it, the transcript of how the work actually went) is exactly the kind of artifact Collection exists to feed the next agent.

  Instrument every run but never gather from what you logged and a true account of how your team works with AI piles up unread; gather diligently while capturing nothing and you curate only the sources that predate your agents, missing the richest one. The payoff is a loop: the exhaust of one session becomes the context that orients the next.