In an agentic world, software no longer just responds to user input — it acts on our behalf. Decisions are made by AI agents, workflows are executed autonomously, and systems continuously change themselves. When something goes wrong, “the state” is no longer a sufficient answer.

This talk explores why event sourcing becomes foundational in AI-driven systems. Event types don’t just record what changed, but capture why it changed, who initiated it, on whose behalf, and what caused it — including the originating prompt, model, and decision chain.

By preserving identity, intent, causation, and context as first-class data, event-sourced systems provide something traditional architectures cannot: a complete, trustworthy audit trail that survives autonomous execution. Not logs. Not snapshots. A factual history.

You’ll see how causation chains, correlated events, and rich metadata turn production systems into explainable systems — enabling accountability, debugging, governance, and trust when humans are no longer in the loop for every action.

If AI agents are going to act for us, we need architectures that can answer for them.