AI Agents
Agents are language models that choose their own next step, call tools, and keep going until the job is done. These articles cover how the loop works, how to design tools and memory for it, when several agents help, and how to measure whether an agent is reliable enough to ship.
16 articles tagged AI Agents.
A practical roadmap for becoming an AI engineer in 2026
You don't need a research degree to build with language models. This roadmap lays out what to learn, in what order, which projects to build, and how to keep your own skills sharp while you use AI tools every day.
The state of AI agents: what works, what doesn't, what's next
A grounded look at where AI agents stand: the categories that deliver value today, the persistent problems with reliability, cost, and security, and how to place sensible bets without buying into either the hype or the dismissal.
Computer use and browser agents: what they can and can't do yet
Agents that look at a screen and click, type, and scroll can operate software that has no API. Here's how they work, where they help, why they're slower and less reliable than API-based agents, and how to run them safely.
Claude Code skills, hooks, and subagents: what each is for
Claude Code has several ways to extend and steer it, and they overlap enough to be confusing. This guide explains skills, hooks, subagents, slash commands, and MCP servers, with examples and a simple rule for choosing.
Least privilege for AI agents: permissions, sandboxes, approvals
An agent is only as safe as the access you hand it. This guide covers scoped credentials, tool permissions, sandboxing, human approvals, and audit trails, with a simple way to match controls to risk.
Prompt injection and the lethal trifecta, explained
Prompt injection is the defining security problem for AI agents, and there's no clean fix yet. This post explains why it works, how the lethal trifecta turns it into data theft, and the design choices that limit the damage.
How to evaluate an AI agent without fooling yourself
An agent that works in a demo can fail one run in three in production. This guide shows how to build a small evaluation suite, grade results honestly, handle nondeterminism, and avoid the traps that make scores look better than reality.
Multi-agent systems: when they help and when they don't
Splitting work across several cooperating agents can improve results and speed, and it can just as easily multiply cost and confusion. This post covers the common patterns, when they pay off, and a rule for starting simple.
Agent memory: short-term, long-term, and files
Models forget everything between calls, so any memory an agent has is something you build. This post covers the practical patterns, from summaries and notes files to searchable stores, plus the privacy and safety questions that come with them.