An AI agent is a software system that uses an AI model to interpret a goal, plan or choose actions, use tools or data, observe results, and continue working toward an outcome.
Suppose a system is asked to review an existing onboarding flow and propose improvements.
A simple generative model might produce suggestions from one prompt.
An agent could inspect existing screens, read a PRD, analyze analytics, map the flow, identify missing states, generate a prototype, compare the result with the design system, and revise it before returning the final artifact.
The key difference is the multi-step loop between reasoning, action, observation, and adjustment.
A chatbot primarily conducts a conversation. An agent can take actions outside the conversation through tools and can maintain a multi-step objective.
The same product can include both conversational and agentic behavior.
Generative AI is a capability for creating outputs. An agent wraps that capability in a process that can decide what to do next.
An agent acting on an existing product needs the right product context and limited tool permissions. Otherwise it can make locally plausible decisions that violate product rules or perform unintended actions.
One-shot generation is not necessarily agentic.
Actions should match the task and user authorization.
Agents need checks for whether intermediate and final results are acceptable.
An AI agent asks: what sequence of information gathering and actions should the system take to accomplish this goal, and how should it adapt after each result?
Generative AI · Context engineering · Product context · Multimodal AI
Read AI for Product Design for agentic design workflows grounded in existing products.