Anthropic: Building effective agents
Primary guidance on simple workflows, tool use, orchestration, and adding autonomy only when it creates measured value.
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What is an AI agent?
Understand an AI agent as a model-driven system that observes context, chooses actions, and works toward a goal.
Plain-English definition
An AI agent is a system that uses a model to choose steps toward a goal while interacting with tools, data, or an environment. The model is one component; instructions, state, permissions, tools, stopping rules, and evaluation determine what the system can actually do.
Memory trick: An agent is a model inside a controlled loop.
Why it matters
Calling every model workflow an agent hides important design choices. A controlled agent can be useful when the path is uncertain, but it also creates more calls, more state, and more ways to fail than a fixed workflow.
Simple example
A research assistant receives a question, searches approved sources, extracts evidence, asks for clarification when necessary, and drafts a cited answer. It cannot send messages, change records, or access sources outside its allowlist.
Example figures are illustrative calculations, not current quoted market prices.
Current example
Source material
Common mistake
An agent is more than a smarter prompt. It is a system with tools, state, permissions, and failure handling; adding a loop changes cost and risk.
Practical takeaway
What you can do with this
Draw the proposed agent as model, instructions, tools, state, environment, and evaluator. Mark which actions are read-only, reversible, or irreversible before building.
Decision check: can you explain what the agent may do, what stops it, and how a human can inspect the result?
Compute College learning path
AI Engineering
Step 27 of 48: What is an AI agent?