AI agent
An AI agent is a system in which a language model decides, step by step, which actions to take to reach a goal: it calls tools, reads the results, then chooses the next action until it judges the task done or hits a limit. Unlike a fixed workflow, the sequence of steps is not written in advance. Anthropic, among others, separates workflows, in which code orchestrates the model, from agents, in which the model steers the process itself.
Why it matters for a PM
Autonomy has a price: higher cost per task, more latency, less predictable results and a larger blast radius when something goes wrong. The PM decides whether a task really needs that flexibility, which tools the agent may use, when a human must approve an action, and how success will be measured before launch.
Example
A sales operations agent receives “prepare the renewal for Acme”. It looks up the contract in the CRM, pulls usage data, drafts a renewal proposal and stops before sending it, because sending an email to a customer requires approval from the account owner.
Key points
- Many use cases are better served by a simple workflow or a single model call; an agent pays off when the path to the goal varies from case to case.
- An agent is only as reliable as its tools: clear descriptions, explicit errors and safe retries.
- Every agent loop needs stop conditions: a maximum number of steps, a budget, a time limit.
- Irreversible or sensitive actions (payments, emails, deletions) call for human approval.
- Agents are evaluated on whole tasks and on their trajectories, not only on the final answer.
Common mistakes
- Calling any chatbot an agent, which blurs the risk discussion.
- Granting broad permissions “to see what it can do” in a production environment.
- Ignoring prompt injection through content the agent reads, such as emails, web pages or tickets.
- Shipping without a trace of each run, which makes failures impossible to diagnose.
Go further with Module
The courses and lessons that cover this concept: