A practical guide to the discovery questions that matter before you sell, scope, or build an AI agent workflow: where the pain is, what breaks, who owns exceptions, and whether the economics are actually worth it.

A practical guide to AI agent acceptance criteria: how to decide whether a workflow is actually ready for production, what to measure before sign-off, and how to avoid shipping on demo vibes.

A practical guide to AI agent caching: what to cache, what not to cache, how to set freshness rules, and how to reduce cost and latency without making your agent confidently wrong.

A practical guide to AI agent drift detection: what drift actually looks like in production, which metrics catch it early, and how to respond before a small behavior change turns into expensive cleanup.

A practical guide to choosing a vertical-specific wedge for an AI agent business: where to look, how to qualify a niche, what signals matter, and how to avoid building generic automation nobody buys.

A practical guide to AI agent feature flags: what to gate, how to roll changes out safely, and how to reduce blast radius when prompts, tools, routing, or approval logic change in production.

A practical guide to AI agent maintenance retainers: what ongoing work actually exists after launch, what to include, how to price it, and how to turn one-off builds into recurring revenue without bullshitting the client.

A practical guide to AI agent error budgets: how to define acceptable failure, protect margin, and decide when an agent can keep running, needs tighter controls, or should be turned off.

A practical guide to AI agent state machines: why they matter, which states to define, and how they make production workflows easier to debug, govern, and trust.

A practical guide to AI agent confidence: why fake percentages are dangerous, what to expose instead, and how to use confidence, freshness, provenance, and missing-data rules to make agent decisions safer in production.