Writing postmortems often feels like filing taxes.
You know it is necessary. You know the intent is good. You carefully reconstruct timelines, fill in templates, identify root causes, and track action items.
Then you move on.
Not because you do not care about learning, but because experience has taught you that the document itself rarely changes future outcomes.
This is not an argument against postmortems.
It is an argument that the postmortem document has quietly been asked to do a job it cannot do at scale.
Postmortems exist for good reasons.
They aim to:
When facilitated well, postmortems are valuable conversations.
The failure happens later, when the document produced by that conversation is expected to function as durable organizational memory across months, teams, and architectural changes.
That expectation does not hold.
In 2019, Cloudflare experienced a global outage triggered by a WAF rule deployment. A regular expression with excessive backtracking consumed CPU across their edge network. The postmortem was exemplary: a clear timeline, a deep technical explanation of regex behavior, and action items around rule testing and staged rollouts.
In 2024, Cloudflare experienced another global outage. This time the trigger was different. A BGP configuration change during a datacenter remediation effort unintentionally withdrew routes, propagating globally faster than humans could react.
Different systems. Different mechanisms.
But the underlying pattern was structurally similar: a configuration change with insufficient blast radius controls moving faster than the organization's ability to detect and intervene.
Both incidents produced strong postmortems. Both were well-facilitated. Both generated action items.
The real question is whether the lessons from 2019 were architecturally available to the engineers making decisions in 2024.
Not as a document someone might remember to search for. As active memory the organization could query under pressure:
What do we know about changes that propagate globally?
What has hurt us before?
What signals mattered last time?
That is a different capability than writing good postmortems.
This outcome is not caused by apathy or lack of rigor. It is a predictable consequence of how postmortems function in practice.
Postmortems optimize for completeness at write time, not retrieval at decision time. They are excellent records and poor memory indexes.
Incidents are reconstructed after the fact. Slack conversations are summarized. Signals that felt important in the moment are compressed or discarded.
David Woods and colleagues documented this in Behind Human Error. Post hoc reconstructions systematically distort how work actually unfolded under pressure because they are built backward from known outcomes rather than forward from the uncertainty operators actually faced.
Once outcomes are known, causes appear obvious. Alternate interpretations and uncertainty disappear.
Sidney Dekker describes this in Field Guide to Understanding Human Error as the "bad apple" trap. Traditional accident analysis identifies where someone deviated from what retrospectively seems like the correct path, erasing the messiness of real-time decision-making.
Action items are tracked locally. They rarely compound into system-level signals. Organizations struggle to see which mitigations recur, stall, or quietly fail across incidents.
Learning is only useful if it arrives before the next failure. In most organizations, the loop between "we learned this" and "we saw it again" is measured in quarters.
Humans do not retain that memory reliably. Systems must.
The Learning from Incidents community has documented this pattern extensively. Organizations are not short on reflection. They are short on mechanisms that preserve and reuse learning across time and context.
Postmortems capture reflection. They do not create memory.
Memory architecture means:
Archives do not do this. Living systems do.
Automation does not replace human judgment or facilitated discussion.
It helps with what humans are structurally bad at.
Systems like COEhub automatically generate postmortem content by ingesting Slack conversations, Zoom, Google Meet, and Teams transcripts, and incident timelines. This preserves what people actually saw, what they were uncertain about, and which signals existed before outcomes were known.
From there, automation can:
Trust does not come from automation being correct. It comes from automation being grounded in original context and open to inspection.
No system should attempt to automate:
Those remain human responsibilities.
The goal is not to replace postmortems. It is to stop asking the postmortem document to function as the learning system.
The question is not whether your postmortems are well written.
It is whether your organization can answer, under pressure:
If the answer depends on someone remembering a document from last year, the problem is not rigor.
It is memory architecture.
Memory architecture means treating organizational learning as a systems problem, not a documentation problem. It requires:
Postmortems remain valuable as facilitated reflection. They should not be asked to serve as the retrieval system, the pattern detector, and the institutional knowledge base simultaneously.
That work belongs to infrastructure purpose-built for memory; not documents.