The Log Is Your Agent, and Your Deepest Lock-In

Ishaan Sehgal argues that an AI agent's identity is its append-only log—not the model or runtime. The log records every input, output, tool call, and permission prompt, making execution disposable. Any worker can reconstruct state and resume, so the agent survives crashes, scaling, and provider swaps because its history is separate from its executor.

This architecture makes reliability and forking trivial. Scaling becomes stateless; sharing means granting log access. Migration across providers is just a projection problem.

But the critical stake is ownership: if a managed provider holds your log, they own your agent. Log lock-in—not model lock-in—is the hardest to break because the log encodes all your workflows and decisions. Compaction is lossy; the raw log must survive or the agent loses history.

The log cannot capture external side effects (e.g., sent emails), only the agent's view. The insights come from fragility in existing tools like Claude Code and Open Code, but the proposal is untested at scale. It doubles as a pitch for Omara's open-source managed agents platform.