Building Reliable AI Agents

Most teams build AI agents the way you'd hand a new hire the company credit card on day one, with no limit, and hope they have good judgment. It works in the demo. Then one day the agent does something expensive, irreversible, or both — and "we told it to be careful" turns out to have never been a control.

Building Reliable AI Agents is the field manual for the part of agent engineering almost no one teaches: not how to give an agent more power, but how to bound it so its autonomy is survivable. Its argument is one sentence — capability is not control. You don't make an agent safe by making it smart; you make it safe by building the boundary the model cannot reason, persuade, or be injected past.

Built around original, deployed frameworks:

• The Capability–Control Gap — why a smarter model is more dangerous, not less
• The Bounded Agent — Brain, Hands, Envelope, Ledger, and the one rule that ties them together
• The Permission Envelope — a deterministic boundary the model can't cross
• The Injection Boundary — read structure, never instructions
• The Autonomy Ladder, the Action Budget, and the Hard Boundary

Every framework is proven by a real system the author built, attacked, and held — including an agent that owns and spends real Bitcoin, bounded by a cryptographic envelope a fully compromised host cannot bypass.

For developers, technical founders, and engineers building autonomous systems that will touch production, money, and consequence. The second book in the Empire Publishing Reliable AI series.