AI agent costs scale with context complexity more than output volume, which makes them harder to predict than traditional compute. Per-task instrumentation is what makes the spend legible and optimizable.

Transient agent failures yield to retry. Structural failures do not. A seven-phase recovery pipeline that classifies failures, routes them to the right response, and converts each recovery into prevention.

AI-assisted development can lead to gradual quality regressions. The "ratchet pattern" enforces monotonically non-decreasing quality metrics, blocking merges that lower any measured value below a recorded floor.

Spawning a vendor CLI as a subprocess is the fast path to AI agent integration, and it breaks down at scale. Direct API calls behind a thin adapter make production orchestration more reliable and far easier to debug.