AI Agent Tutorials

What makes something an "agent"

A simple chatbot takes one input and produces one output. An agent is different: it perceives its environment, reasons about what to do next, takes an action (often by calling a tool), observes the result, and repeats — looping toward a goal rather than stopping after one response. The defining trait isn't the model itself, it's this perceive → reason → act → observe loop wrapped around it.

Core components of an agent

• The model (the "brain") — the LLM doing the reasoning and deciding what to do next at each step.
• Tools — the actions the agent can actually take: searching the web, calling an API, running code, querying a database. See MCP Explained for how tools are commonly exposed to agents in a standardized way.
• Memory — short-term (the current conversation/task context) and sometimes long-term (persisted facts or history retrieved across sessions).
• Planning — breaking a high-level goal into smaller steps, and adjusting the plan as new information comes in.

The ReAct pattern

One of the most common agent design patterns is ReAct (Reason + Act): at each step, the model produces a Thought (its reasoning about what to do next), takes an Action (usually a tool call), receives an Observation (the tool's result), and uses that to inform the next Thought. This loop repeats until the model decides it has enough information to produce a final answer. It's a simple pattern, but it's the conceptual backbone of most modern agent frameworks.

Thought: I need to check if this domain is currently registered before I can advise on next steps.
Action: whois_lookup(domain="example.com")
Observation: registered, expires in 45 days
Thought: I have what I need to answer.

Types of agents

• Single-agent task automation — one agent handles an entire task end-to-end using its available tools.
• Multi-agent systems — multiple specialized agents collaborate, often with one acting as an orchestrator that delegates sub-tasks to others (e.g. a "researcher" agent and a "writer" agent working together).
• Human-in-the-loop agents — the agent proposes actions but requires human approval before executing anything sensitive or irreversible.

Common design patterns

• Tool-use agent — the basic pattern: reason, call a tool, observe, repeat (ReAct, above).
• Planner-executor — a planning step first produces a full multi-step plan, then a separate execution phase carries it out, which tends to be more reliable for complex tasks than deciding one step at a time.
• Reflection / self-critique — the agent reviews its own output or intermediate results and revises before continuing, catching some errors before they compound.
• Orchestrator-worker (multi-agent) — a coordinating agent breaks a goal into sub-tasks and dispatches them to specialized worker agents, then combines the results.

Where agents struggle

• Long-horizon planning drift — small errors early in a long task chain compound, and the agent can lose track of the original goal.
• Tool error handling — real-world tools fail, time out, or return unexpected data, and agents don't always recover gracefully.
• Cost and latency — each reasoning step is a model call; long loops get slow and expensive.
• Security exposure — more autonomy and more tool access means a bigger blast radius if the agent is manipulated (see AI Security, specifically "excessive agency").

Best practices

• Scope tools narrowly — give an agent only the specific tools its task requires, not broad access "just in case."
• Gate irreversible actions — require explicit human approval before an agent sends something, deletes something, or spends money.
• Log every step — full traceability of thoughts, actions, and observations makes debugging and auditing possible.
• Set hard limits — cap the number of iterations, tool calls, or total spend to prevent runaway loops.
• Validate tool outputs — don't assume a tool result is well-formed or trustworthy; check it before acting on it further.