What is an AI agent?

Agents move AI from isolated generation toward workflow execution. In GTM, an agent might monitor an account signal, research the company, compare it with an ideal customer profile, recommend an action, draft an asset, update a system, and route the work for review.

Definitions vary because the technology is evolving. Anthropic draws a useful distinction between workflows, where code follows predefined paths, and agents, where the model dynamically directs its own process and tool use. OpenAI’s practical guide similarly emphasizes models, tools, instructions, guardrails, and orchestration as core building blocks.

• Goal: The outcome the agent is trying to advance.
• Context: The information, instructions, memory, and constraints it can use.
• Reasoning loop: The ability to choose a next step based on the current state.
• Tools: Search, data access, software actions, code, communication, or other capabilities.
• Guardrails: Limits, permissions, checks, and escalation rules.
• Feedback: Evidence that helps the agent judge progress and adapt.

A weekly planning agent reviews campaign performance, open pipeline, buyer-intent signals, product releases, sales-call themes, and available team capacity. It identifies three priorities, explains why each matters, drafts a proposed workback plan, and creates tasks only after the marketing leader approves the recommendation. Its value is not that it wrote a plan. Its value is that it connected evidence to coordinated next actions.

“Agent” is often used as a fashionable label for a prompt, chatbot, or automation. A persona and a text box do not create agency. Useful agents need a goal, tools, decision logic, state, action, and a way to evaluate what happened.