AI Agents Explained

TL;DR

An AI agent is a program that uses a language model as its reasoning engine to complete multi-step tasks autonomously — calling tools, observing results, and deciding what to do next until the goal is met. Use an agent when a task requires planning, multiple tool calls, or conditional logic that changes based on intermediate results. For single-question lookups or fixed pipelines, a plain LLM call or a prompt chain is simpler and cheaper.

Quick facts:

• An agent loops: reason → act → observe → reason again
• Tools can be anything: web search, code execution, database queries, API calls
• Top frameworks: LangChain, AutoGen, CrewAI, Anthropic Agent SDK, LlamaIndex
• Agents cost more per task than single completions — design with a step budget

What Is an AI Agent?

A standard LLM call is a single turn: you send a prompt, the model returns a response, done. An agent replaces that single turn with a loop:

1. Perceive — receive a goal and any available context
2. Reason — the model decides what action to take next
3. Act — call a tool (search, run code, fetch a URL, write a file)
4. Observe — receive the tool's result
5. Repeat — feed the result back to the model and reason again
6. Stop — when the model decides the goal is complete

This loop lets agents tackle tasks that have unknown complexity at the start — you do not need to know in advance how many steps the solution requires.

Framework Comparison

| Framework | Language | Multi-Agent | Tool Use | Best For | |-----------|----------|-------------|----------|----------| | LangChain | Python / JS | Yes | Yes (LangChain tools ecosystem) | General-purpose, large plugin library | | AutoGen | Python | Yes (native) | Yes | Multi-agent conversations and debate | | CrewAI | Python | Yes (role-based) | Yes | Structured teams of specialized agents | | Anthropic Agent SDK | Python | Planned | Yes (native tool use) | Claude-first, production safety controls | | LlamaIndex | Python / TS | Limited | Yes (query engines) | RAG-heavy agents, document QA |

Recommendation: Start with the Anthropic Agent SDK if you are already using Claude — it maps directly to the Claude API's native tool use with no extra abstraction. Use LangChain if you need a large pre-built tool library quickly.

When to Use an Agent vs. a Simpler Approach

| Scenario | Recommended Approach | |----------|----------------------| | Answer a factual question from a document | Single prompt + context | | Summarize a fixed list of articles | Prompt chain (map → reduce) | | Research a topic by searching the web | Agent with search tool | | Write code, run it, fix errors until it passes | Agent with code execution tool | | Fill out a form from a PDF | Single prompt (structured output) | | Book a flight by navigating a website | Agent with browser tool | | Generate 100 product descriptions | Batch prompt (no agent needed) | | Debug a production incident across logs and dashboards | Agent with multiple tool integrations |

Rule of thumb: if you can write the steps out in advance and they never branch, use a chain. If the steps depend on what you find along the way, use an agent.

FAQ

Are AI agents reliable enough for production? Yes, for well-scoped tasks with guardrails. Reliability breaks down when the goal is vague, the tool set is too large, or there is no step budget. Define a clear success condition, limit the available tools to what the task actually needs, and set a maximum iteration count.

How do I prevent an agent from running forever? Set a hard max_iterations or max_tokens budget before the loop starts. Most frameworks expose this as a parameter. For critical workflows, add a human-in-the-loop checkpoint before irreversible actions (sending email, writing to a database, deploying code).

What tools can an agent use? Any function you expose to it. Common tools include: web search, code interpreter, file read/write, database queries, REST API calls, and browser automation. The model receives a description of each tool and decides when to call them — it never executes code directly, only requests a call.

How much does running an agent cost? Significantly more than a single prompt. Each iteration consumes input tokens (full conversation history + tool results) plus output tokens. A 10-step agent task might use 10–50× the tokens of a single-turn response. Track usage per run and set spending alerts during development.

Can agents work together? Yes — multi-agent systems assign specialized roles (researcher, writer, reviewer) to separate model instances that pass structured messages between them. AutoGen and CrewAI are built around this pattern. Coordination overhead adds latency and cost, so only split into multiple agents when specialization meaningfully improves output quality.

Further Reading

For the model fundamentals that power agents, see Understanding Large Language Models. To wire an agent into a real API, Building AI-Powered Applications covers the completion, tool use, and streaming patterns you will need. If you are completely new here, start with Getting Started with AI.