An AI system that can plan, take multi-step actions, and use tools to accomplish a goal. Not just respond to one prompt at a time. For ad agencies, an AI agent is the difference between a chatbot that answers questions and a digital coordinator that actually moves work forward.
Also known as autonomous agent, AI assistant agent, software agent, agentic AI
An AI agent is software that uses a large language model as a reasoning engine, plus a planning loop and a set of tools, to accomplish goals that require more than one step. Where a chatbot responds to a single prompt and stops, an agent reads its goal, decides what to do first, takes an action, observes the result, decides what to do next, and continues until the goal is met or a defined boundary is hit.
The tools an agent can call vary by use case: search the web, query a database, send an email, post to a chat channel, call an API, run code. Modern agent frameworks let teams build narrow agents (one job, well-bounded) or general agents (broader autonomy, more risk). For most agency work, narrow agents with explicit guardrails are the only kind worth deploying.
Agency work is full of multi-step coordination that doesn’t quite need a human, but historically had no alternative. AI agents close that gap. Three specific places this changes the math.
Production coordination. A research agent can scan client mentions, summarize sentiment, draft a weekly digest, and flag anomalies. Without a producer running the same query manually every Monday. The producer reviews the agent’s output and intervenes when something looks off.
Asset generation pipelines. An agent can take a brief, generate three creative directions, render variations across image and copy, format them into a deck template, and post to a review channel. The team reacts to options instead of building them from scratch.
Risk is real and specific. Agents take actions. They can send emails, post messages, spend money on API calls. Without explicit boundaries, an agent left running can compound mistakes faster than a human can catch them. Guardrails, scoping, and review checkpoints are not optional.
A studio builds a small research agent that takes a brief and produces a one-page competitive scan: top competitors, recent campaigns, positioning summary, and a list of differentiators. The agent calls web search, summarizes results, and formats output to a template. A strategist reviews and refines. What used to take half a day takes twenty minutes. The agent never sends anything externally. Outputs land in an internal channel where humans decide what’s worth keeping.
The agent is bounded. The judgment is human. That ratio is the design pattern that holds up.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.