A chatbot answers. An AI agent acts. It receives a goal, breaks it into steps, uses tools — search, code execution, APIs, a web browser, your company’s systems — observes the results and continues until the goal is reached or it needs help. “Agentic AI” has become one of the most important directions in the industry.

The agent loop

At its core, an agent repeats a simple cycle:

  1. Think: decide what to do next given the goal and what has happened so far.
  2. Act: call a tool — search the web, read a file, run code, send a request.
  3. Observe: read the tool’s result.
  4. Repeat until done, then report.

The 2022 ReAct paper (Reasoning + Acting) by Shunyu Yao and colleagues showed that interleaving reasoning steps with actions made language models more effective and more interpretable on tasks requiring external information. In 2023, Toolformer from Meta researchers showed models could learn when and how to call tools such as calculators and search engines.

Tool use: the key ingredient

Modern models support function calling (also called tool use): the developer describes available tools with names, descriptions and parameter schemas; the model decides when to call one and outputs structured arguments; the application executes the call and returns the result. Standards such as the Model Context Protocol make it easier to connect agents to many tools.

Workflows vs. agents

Anthropic’s guide Building effective agents draws a useful distinction:

  • Workflows follow predefined paths: the developer decides the steps, and the model fills them in (for example, classify a ticket, then draft a reply, then check it).
  • Agents decide the steps themselves dynamically.

Workflows are more predictable and cheaper; agents are more flexible. The guide recommends starting with the simplest approach that works and adding autonomy only when needed.

Common patterns

  • Prompt chaining: a sequence of model calls, each processing the previous output.
  • Routing: classify the input and send it to a specialized path.
  • Parallelization: run sub-tasks simultaneously and combine results.
  • Orchestrator–workers: a lead model splits a task and delegates parts to other model calls.
  • Evaluator–optimizer: one model produces, another critiques, in a loop.

What agents do today

  • Coding agents that implement features, run tests and fix failures.
  • Research agents that search many sources and write reports.
  • Computer-use agents that operate a browser or desktop interface.
  • Customer service agents that look up orders and process requests.
  • Data agents that query databases and build analyses.

The risks

  • Compounding errors: small mistakes early in a long task can derail everything.
  • Prompt injection: malicious instructions hidden in web pages, emails or documents can hijack an agent that reads them.
  • Excessive permissions: an agent that can delete files, send emails or spend money can cause real damage.
  • Cost and latency: many steps mean many model calls.

Common mistakes to avoid

  • Building an agent when a workflow would do. If the steps are known in advance, a fixed workflow is cheaper, faster and easier to test.
  • Giving broad permissions. Grant the minimum access needed, and require human approval for irreversible actions such as payments or deletions.
  • Trusting content the agent reads. Treat web pages, emails and documents as untrusted input that may contain injected instructions.
  • Skipping evaluation. Test agents on realistic tasks and measure success rates, cost and failure modes before deployment.

Bottom line

Agents are most useful today for well-defined tasks with clear checks and human oversight.

Key takeaways

  • Agents pursue goals through a loop of reasoning, tool use and observation.
  • Function calling lets models trigger tools with structured arguments.
  • Predefined workflows are often better than fully autonomous agents.
  • Common patterns include chaining, routing, parallelization, orchestrator–workers and evaluator–optimizer.
  • Prompt injection, compounding errors and excessive permissions are the main risks.

Frequently asked questions

Is an AI agent the same as a chatbot?

No. A chatbot responds to messages; an agent takes actions using tools to reach a goal, often over many steps without a human prompt at each one.

Are AI agents reliable enough for business use?

For well-scoped tasks with good tools, guardrails and human review of important actions, yes. Fully autonomous agents on open-ended tasks still fail often.

What is prompt injection?

An attack in which text the agent reads — on a website or in a document — contains instructions that try to override the user’s goals, for example to leak data.