Hard problems.
Practical solutions.
Building useful AI agents is the easy part. Keeping them safe, recoverable, fast, and maintainable is the real challenge. These practical guides turn production problems into concrete engineering decisions.
Start with the action boundary.
The first difficult problem every tool-using agent must solve.
Even the latest AI models can act unsafely. Here’s the solution.
A model follows hostile instructions and requests a dangerous tool action.
Enforce tool policy, approval, and sandbox boundaries independently of model output.
Explore the solutions.
Build smarter, without sacrificing control.
Best AI agent frameworks in 2026: a practical way to choose.
A framework looks impressive in demos but does not fit the workflow.
Choose by orchestration style, trust boundaries, persistence, and operational cost.
Your AI agent crashed halfway through. Resume without duplicating work.
A run crashes after writing to a service but before reporting completion.
Persist checkpoints and event history, and reconcile external side effects with idempotency keys.
AI agents are too slow and expensive. Fix the harness first.
The agent spends time and tokens on tool schemas, context, and unnecessary loops.
Measure overhead and quality, narrow context and capabilities, and remove redundant model calls.