Capture

When an agent runs unsupervised, the only proof of what it did is whatever you thought to keep. Capture is the discipline of instrumenting the run so that proof exists: the prompts it received, the responses it gave, the tool calls it made, and the metadata around them, from cost and token spend to the model and platform behind each session.

When every session lands in a durable record, a failure from weeks ago is still reconstructable and your AI spend is legible instead of a line item that surprises you on the bill. Without it, the hardest incidents are the ones you can never reopen, and the budget conversation runs on guesses.

Exploratory

Nothing is captured. When something breaks, you have the chat window, if that. It is where everyone starts.

Enables

You can still act, and the first unreconstructable incident tells you exactly which runs you needed a record of. That is what a capture pipeline later gets aimed at.

Deficiencies

No record of the prompts, calls, cost, or model, so failures are unreconstructable and there is nothing to learn from later.

Cost to maintain

Nothing, until an incident is paid for in lost context.

Expert-led

One engineer logs their own runs into a personal drawer.

Enables

That person can reconstruct their own sessions and spot their own patterns.

Deficiencies

It covers one person's work in their own format, and nothing is captured when anyone else runs.

Cost to maintain

That person's diligence to keep logging by hand.

Documented

A few people keep records to a shared standard of what to capture and where.

Enables

Comparable records across people and projects: a team trail, not a private one.

Deficiencies

Still opt-in, so gaps open whenever someone forgets, and metadata like cost and model gets captured unevenly.

Cost to maintain

Writing the standard once, then the ongoing manual logging.

Structural

Every session's prompts, calls, cost, and model land in a store as a side effect of the work.

Enables

Complete capture with zero per-session effort, and nothing that depends on someone remembering.

Deficiencies

It captures volume indiscriminately, and the record is only as useful as its sibling discipline, Availability, makes it.

Cost to maintain

Upfront engineering on the logging pipeline, plus its upkeep as tools and platforms change.

Capture rarely stands alone. Where it overlaps a sibling discipline, an investment on one side quietly depends on the other. The Observability domain page lays these out in full.

• 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.

• 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.