Tool Call Interface Design for Coding Agent Skills
Separating tool calling, tool search, and MCP prevents teams from optimizing the wrong layer.
Separating tool calling, tool search, and MCP prevents teams from optimizing the wrong layer.
Larger skill libraries make AI agents pick the wrong tool more often, not because of context limits.
Dead skills bloat AI agent prompts, wasting tokens and slowing inference with unused capabilities.
Pessimistic mode precomputes fallback states to dodge cascade failures when PRs conflict.
Agents need explicit escalation rules to avoid flooding merge queues with retries.
Merge queue performance now has measurable levers that most teams have never instrumented.
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 writing code in parallel need merge queues built for machine-speed concurrency.
AI agents navigate codebases differently than humans, making repo structure a technical constraint.
Agents outpaced CI/CD infrastructure built for human-speed development.
Agents fail silently where servers crash loudly, but their transcripts reveal why.