Workflows · Knowledge Management

Knowledge Management: Workflows

How knowledge is captured, made authoritative, retrieved and retired, and why this function is load-bearing in an AI-operated company.

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In a conventional company this function is administrative. In one where agents read the documents to decide how to act, it is load-bearing, and a stale page stops being an inconvenience and becomes an instruction.

What arrives#

FromWhatBecomes
Every functionDecisions and their reasoningDecision records, within 24 hours
EngineeringArchitecture decisions, and rejected optionsThe record that stops re-argument
Security and DevOpsPostmortemsLessons, and detection improvements
ResearchFindings, especially negative onesThe record that stops paying twice
SalesProposals, and what wonReusable material
PMOProject history and client contextDeparture-proof knowledge
People leavingWhat only they knowCaptured in the first week of notice, not the last

What leaves#

ToWhat
Anyone askingAn answer with citations, or an honest "not here"
Every agentThe context bundle it reads, reviewed quarterly
New joinersA path from nothing to useful
MarketingMaterial worth publishing, once cleared
LeadershipThe knowledge risk register: what exists in one head only

The cycle#

Capture, authority, retrieval, retirement In: Decision or lesson () → Drafted in 24h () → Owner confirms (). Live: One authority () → Review date set () → Retrieved with sources (). Out: Past review date () → Confirm, update or retire () → Superseded, pointer kept (). In Decision or lesson Drafted in 24h Owner confirms Live One authority Review date set Retrieved with sources Out Past review date Confirm, update or retire Superseded, pointer kept
Capture is nearly free now. Retirement is the hard part and the step every knowledge base skips, which is why the share of true content falls year on year.

The third lane is the one nobody builds, and it is the difference between a knowledge base and an archive.

Handoff contracts#

With every function. Decisions arrive within 24 hours with their reasoning. The agent drafts; the decision owner confirms. Nobody is asked to write a document from scratch, because that is the requirement that quietly ends every knowledge programme.

With Engineering. Architecture decisions arrive with rejected options. Without them, the same debate recurs every eighteen months among people who cannot tell whether the current design was chosen or defaulted into.

With HR. Departure capture starts in the first week of a notice period.

With AI Engineering and Product. What each agent reads is agreed here and reviewed quarterly. It is the highest-consequence text in the company and the easiest to let accumulate.

Cadence#

ContinuousCapture
MonthlyStaleness, duplicate authorities
QuarterlyRetirement pass, context review, knowledge risk register
On noticeDeparture capture, first week

The failure this design is built against#

A wiki with four thousand pages, of which perhaps eight hundred are still true, and nobody able to say which. People stop using it, ask a colleague instead, and the knowledge that was supposed to be institutional is once again personal.

In an AI-operated company that failure escalates: the agents cannot ask a colleague. They read what is there, believe it, and produce confident, consistent, incorrect work at volume.

Retirement is the whole answer, and it is why the retirement rate is a headline metric rather than housekeeping.

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