Event Sourcing on AIBussin — AI applications, systems and books

A Successful Call Is Not Finished Work

Mon, 14 Sep 2026 05:00:14 +0100

Part 2 — Get the Model Out of the Chat Box

The model is not the process

An AI pipeline gets a response back, the response looks right, and something marks the work done. That step is where many AI-enabled processes quietly lose track of the truth.

A successful model call tells you the call worked. It does not tell you that anyone checked the output against what the task required, that the thing checked is the thing being used, that someone with standing agreed, or that the agreement was written down. Collapse those into one status field and “done” starts to mean “the model returned something plausible”.

Restart Is Not Resume

Mon, 14 Sep 2026 05:00:16 +0100

Part 3 — Give Intelligence a Runtime

Externalize working memory

When an AI process dies partway through, the usual fix is to restart it and re-run whatever did not finish. For a model call, that can mean paying for the same request twice. For an agent that sends email, opens tickets, charges a card or edits files, it can mean doing something twice that cannot be undone.

The capability you actually need is a different one:

Preserve Before You Interpret

Mon, 14 Sep 2026 05:00:17 +0100

Part 3 — Give Intelligence a Runtime

Raw output first

Every rule an AI application applies to a model’s response is an interpretation. Did generation finish? Is this valid JSON? Is this a refusal? How many tokens did it cost? What does the answer actually say? Those rules can turn out to be wrong: a provider adds a field, a parser misses a case, a failure shows up that nobody anticipated.

Cognitive Graphs: A General Architecture for Replayable Reasoning

Mon, 11 May 2026 10:50:29 +0100

A Cognitive Graph is an enhanced chat log one that doesn’t just record what was said, but structurally preserves how thoughts, decisions, alternatives, and artifacts evolve over time, making the whole history replayable and auditable.

TL;DR: A Cognitive Graph turns AI-assisted work from lossy chat logs into event-sourced, replayable reasoning state. Instead of preserving only the final output, it preserves the decisions, alternatives, rationales, mutations, hashes, and snapshots that explain how the output came to exist.