Code Storage Observability for Agent Commit Patterns
Tracking agent commit sequences reveals execution patterns that individual diffs obscure.
Tracking agent commit sequences reveals execution patterns that individual diffs obscure.
Agents need separate storage for ephemeral outputs while keeping project assets in Git LFS.
Agents committing code at machine speed expose infrastructure limits designed for human developers.
Automated policies replace human naming conventions to manage hundreds of agent-created branches.
Webhooks enable agents to react to repository events in real time without continuous polling.
Benchmarks designed for factory agents must measure process discipline, not just final correctness.
Trajectory-level evaluation catches agent failures that outcome metrics hide before merge.
Shallow clones cost more than full clones in agent-heavy pipelines with workspace caching.
Traditional velocity metrics hide AI-generated code decay behind inflated output numbers.
AI agents stress storage systems differently than humans do, requiring new infrastructure design.
Agents expose four silent failures in sync layers built for human commit cadence.
Agents shift infrastructure bottlenecks downstream, where queues and idle capacity hide real costs.