Soren Lau-Espinoza

Agents committing code at machine speed expose infrastructure limits designed for human developers.

Shallow clones cost more than full clones in agent-heavy pipelines with workspace caching.

Agents expose four silent failures in sync layers built for human commit cadence.

Larger context windows make long-running agents worse, not better—here's why.

Agents need structured transcript logs that conventional logging architectures cannot provide.

Realistic testing reveals coding agents perform far worse than leaderboard scores suggest.

Larger skill libraries make AI agents pick the wrong tool more often, not because of context limits.

Agents need explicit escalation rules to avoid flooding merge queues with retries.

Merge queue failures cascade differently than branch failures and require triage before alerting.

Flaky tests in merge queues cascade across dozens of PRs at once, not just fail in isolation.

Agents fail silently where servers crash loudly, but their transcripts reveal why.