Forking data for AI agents: The missing primitive for safe, scalable systems
Teams deploying agentic systems routinely face the identical failure mode: nondeterministic agent habits with no clear causal hint. The situation isn’t the mannequin or immediate; it is nearly at all times the state the agent reads and mutates. Agents execute multi-step workflows, invoke exterior instruments, and repeatedly replace shared objects. Without snapshot isolation and version-aware…
