50 AI engineering concepts CTOs must explain before agents hit production
alex_verem · x · 2026-11-05
LimestoneHQ published a checklist of 50 AI engineering concepts every CTO should be able to explain before an agent touches production, spanning:
- Evals & metrics: baseline metric, cost of a wrong answer, precision/recall, regression evals, offline vs online evals, data drift
- Context & prompt engineering: context engineering, prompt caching, compaction, prompt versioning, tool descriptions, schema validation
- Agent architecture: agent harness, coordinator-workers, subagent isolation, idempotent tools, durable execution, checkpoints/resume, stop conditions, escalation paths
- Release & ops: shadow mode, canary releases, model version pinning, trace replay, batch processing, rate limits, per-run spend caps, model deprecation
- Security: least-privilege tool access, untrusted content boundaries, agent identity, secrets management
The core thesis: engineering teams must have a crisp answer for each item before deploying agents in production.
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