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Practical guides on the tools developers use every day — JSON, regex, JWT, cron, and more.
30 posts matching · page 1 of 3
Looks at the near-term, technically grounded trends shaping AI agents, from better evaluation tooling to narrower, more reliable deployments.
Surveys the full technical stack behind a production AI agent, covering the model layer, tool layer, memory layer, and orchestration.
Explains why standard error handling patterns are not enough for AI agents and what an agent-aware error handling strategy looks like.
Breaks down the think, act, observe loop that powers most AI agents, and explains what actually determines when it stops.
Explains the mechanics of tool calling step by step, from schema definition to execution, and why it is the foundation of every AI agent.
Explains how AI agents break a high-level goal down into a sequence of concrete actions, and where planning approaches differ.
Compares single-agent and multi-agent architectures directly on reliability, cost, and complexity to help you pick the right one.
Explains what multi-agent systems actually are beneath the marketing language, and what problems they solve that a single agent cannot.
Explains how to build uncertainty awareness into an AI agent so it escalates to a human instead of guessing when it should not.
Offers a practical framework for scoping what data and systems an AI agent should be allowed to touch, based on risk and reversibility.
Explains the layers of guardrails that keep AI agents from taking destructive or unauthorized actions, from permissions to runtime checks.
A hands-on guide to building a practical AI developer stack for 2026: models, agents, orchestration, and observability with copy-paste code.