Blog Archive
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Prompts Are Production Assets.
Prompts in production AI systems should be versioned, reviewed, tested, owned, and released with the same discipline as other behavior-changing assets.
Shadow Mode Is Where AI Agents Grow Up.
Before giving AI agents production authority, run them beside the real workflow, compare decisions, and learn where trust actually holds.
Your AI System Needs a Boring Admin Panel.
Production AI systems need operational controls: pause buttons, model routing, queue visibility, failure reasons, policy flags, and audit views.
Your AI Workflow Needs a Dead Letter Queue.
AI workflows need a place for failed, ambiguous, unsafe, or unprocessable tasks to land without disappearing into logs or retry loops.
Evals Are Not Unit Tests for Vibes.
AI evaluations only work when they are tied to real decisions, failure modes, datasets, thresholds, and product risk.
Model Upgrades Are Not a Release Strategy.
Changing models can improve quality, but production AI systems need versioning, evals, rollout gates, fallbacks, and rollback plans.
The AI Feature That Should Have Been a Cron Job.
Many AI features are overbuilt. If the task is deterministic, scheduled, and rule-based, use boring automation before adding a model.
Your LLM Cost Problem Is a Workflow Problem.
Most runaway AI costs are not caused by expensive models alone. They come from bad routing, repeated context, retries, and missing workflow discipline.
Your Support Bot Needs an Escape Hatch.
AI support systems should know when to stop, escalate, preserve context, and hand the customer to a human without making things worse.
Full-Stack Engineers Are Becoming Workflow Engineers.
AI is changing full-stack work from screens and APIs toward orchestration, tools, state, permissions, and operational workflows.