Agents
Models that reason, call tools, and act across multiple steps.
17 tutorials, spanning 3 of the 17 learning layers — ordered so each builds on the last.
8Agents
- What Is an AI Agent?Strip the hype and an agent is three things: a model that can call tools, a loop, and the authority to decide what happens next.
- Agent Architectures: From Rules to AutonomyA spectrum runs from fixed pipelines to fully autonomous loops. Knowing where a task belongs on it is the single most important agent design decision.
- How Does Tool Calling Work?The model never runs anything. It emits a structured request, your code executes it, and the result goes back as text. Everything follows from that.
- The Agent LoopThink, act, observe, repeat. The cycle is trivial to implement and the hard parts are all about when to stop.
- How Agents FailSeven recurring failure modes, what causes each, and the specific guardrail that prevents it. Read this before shipping anything autonomous.
- Writing Good Tool DefinitionsThe tool description is a prompt, and it is the one the model relies on most. Vague descriptions cause more agent failures than weak reasoning.
- What Is MCP?A standard protocol for exposing tools to models, so integrations stop being written once per application.
- Multi-Step PlanningPlan upfront or decide as you go? The tradeoff is coherence against adaptability, and the practical answer is usually a shallow plan you revise.
- The ReAct PatternInterleave reasoning and action instead of separating them. The idea behind the modern agent loop, why it beats reasoning alone, and where it strains.
15Advanced Agents
- Evaluating AgentsScore the outcome, not the path. Agents are nondeterministic and multi-step, which breaks the evaluation habits that work for single calls.
- Agent MemoryModels have no memory between calls. Everything that feels like memory is retrieval, and designing what to store is the actual problem.
- Coding AgentsThe agent application that works best in practice, and the specific properties of software work that make it work.
- Computer-Use AgentsAgents that see a screen and click. Universal compatibility, unreliable execution, and a security model that demands isolation.
- Context EngineeringDeciding what goes into the context window, in what order. The discipline that replaced prompt engineering once systems got complicated.
- Human-in-the-Loop DesignWhere to put a person in an automated system. Gate by reversibility and confidence, not by task importance.
- Multi-Agent SystemsSeveral agents with different roles, coordinating. Sometimes better than one, often just a more expensive way to fail.