Write-Ahead Logging for AI Agents: Borrowing Database Recovery Patterns for Crash-Safe Execution
Your agent is on step 7 of a 12-step workflow — it has already queried three APIs, written two files, and sent a Slack notification — when the process crashes. What happens next? If your answer is "restart from step 1," you're about to re-send that Slack message, re-write those files, and burn through your LLM token budget a second time. Databases solved this exact problem decades ago with write-ahead logging. The pattern translates to agent architectures with surprising fidelity.
The core insight is simple: before an agent executes any step, it records what it intends to do. Before it moves on, it records what happened. This append-only log becomes the single source of truth for recovery — not the agent's in-memory state, not a snapshot of the world, but a sequential record of intentions and outcomes that can be replayed deterministically.
