What an AI agent does for a marketing team
An AI agent is software that can read information, reason about it, and take a defined set of actions toward a goal. For marketing, that usually means watching data, preparing work and flagging what needs a human.
A chatbot answers questions when asked. An agent runs on a schedule or a trigger. It might check yesterday's ad spend every morning, draft the weekly performance summary, or score a new lead against your criteria and route it. We have built and run an agent for our own marketing operations, so we design from real day-to-day use.
Agents we build
We focus on three types of agent, because they save real time and their output is easy to check.
- Marketing research agents. They gather competitor ads, search results, reviews and pricing pages, then summarize what changed. Useful for competitive intelligence and campaign planning.
- Reporting agents. They pull numbers from GA4, ad platforms and your CRM, compare them with last period, and write a plain-English draft. A person reviews it before it goes to anyone.
- Lead qualification agents. They read form answers, call notes or chat transcripts, apply your qualification rules, and suggest a score and next step for your sales team.
Agents sit on top of solid data and workflows. If your tracking or CRM needs work first, we start with automation and CRM development.
The Green, Yellow and Red authority model
Every action an agent can take is sorted into one of three levels before the agent goes live. You approve the list.
| Level | What it covers | Examples |
|---|---|---|
| Green | Low-risk monitoring and routine actions you have pre-approved. The agent acts on its own. | Read ad and analytics data, flag a spend spike, save a report draft, tag a lead in the CRM. |
| Yellow | Actions that need a named human to approve before they happen. | Send a client report, pause a campaign, email a lead, change a CRM stage that triggers a sequence. |
| Red | Restricted, high-impact actions. The agent cannot take them at all. | Raise budgets, change billing, delete records, publish to the website, alter user access. |
The levels are enforced in the agent's permissions, so a Red action is blocked at the system level. We explain the thinking in more detail in our article on human-approved AI agents.
Audit logs on every action
Every agent keeps a log of what it read, what it decided, what it did, and who approved it. You can see exactly why something happened.
Each log entry records the time, the trigger, the data the agent used, the action taken or proposed, its authority level, and the approver for Yellow actions. Logs live in a place you own, such as a Google Sheet or a database table, and can be exported. If a result looks wrong, you can trace it back to the input and fix the rule instead of guessing.
How we build and launch an agent
We launch agents in stages, starting with read-only access and widening permissions only after the output proves reliable.
- Define the job. One agent, one clear task, written in plain English with examples of good and bad output.
- Assign authority. We list every action and agree its Green, Yellow or Red level with you.
- Shadow mode. The agent runs alongside your team and only suggests. We compare its suggestions with what people actually did.
- Go live. Green actions switch on. Yellow actions route to approvers by email or chat.
- Review. We check logs regularly and tune prompts and rules as your business changes.
Agent builds are quoted per project. Ongoing monitoring can be part of a monthly plan.