Agentic AI
Agentic AI is AI that can take several steps toward a goal, not only answer one question. In marketing that might mean checking an ads account, spotting a problem, and drafting the next action. It is not the same as software that spends money with nobody asked. A person should still approve spend.
Theodor Lindfors, Founding Marketer ·
Why the word spread
Chat answers a question. Agentic systems run a loop: plan, tool, observe, repeat. Marketers met the word when tools started promising they would not only draft a report, but fetch the data, write it, and send it. That loop is useful. The word is now on every homepage.
Let's take a software company as an example. They sell a monthly tool to plumbing businesses. The software is CRM (customer relationship software) that helps a plumber keep jobs and follow-ups in one place. Useful loops look boring: watch demo CPA (cost per acquisition: ad spend divided by demo requests), flag a tired LinkedIn ad, send the Monday digest, propose shifting $2,000 from LinkedIn to Google. The loop is the point. The brand word is not.
Agentic AI vs generative AI
Generative AI answers a prompt: a draft, a summary, an image. Agentic AI keeps going after the first answer: it calls tools, checks the result, and takes the next step. Let's take a software company as an example. They sell a monthly tool to plumbing businesses. A model that writes the software company's LinkedIn ad copy is generative. A system that pulls yesterday's CPA (cost per acquisition) of $90, up from $50, flags the tired ads, and drafts the Slack note is agentic. Most useful marketing setups are both, with a person still in the loop on spend.
How to read agentic AI
Ask what the system is allowed to do. Read-only tool use is still a loop, but it cannot hurt an account. Write access without human in the loop is a different product. Lemonado's lead noun is still AI co-worker, not agent. The co-worker can be agentic in how it works. Let's take a software company as an example. They sell a monthly tool to plumbing businesses. The software company's marketers still own the goal. MCP (Model Context Protocol) connections into ChatGPT or Claude stay read-only. The work that writes happens inside Lemonado.
If a vendor cannot say what the system is allowed to write, treat it as a demo. Multi-step read is still useful. Multi-step spend without a person in the loop is a different product than most teams want on day one. Let's take a software company as an example. They sell a monthly tool to plumbing businesses. The software company should not let an unsupervised loop raise Google bids overnight.
Common agentic-AI mistakes
- Treating agentic as unsupervised. Most good setups still ask before they spend.
- Wiring ChatGPT to an ads API with MCP (Model Context Protocol) and calling it done. Context and permissions are the product. Let's take a software company as an example. They sell a monthly tool to plumbing businesses. A read-only connection for the software company is not a budget bot.
Ask three questions of any agentic pitch:
- What can the system read?
- What can the system write?
- Who gets the changelog of what it did?
If those answers are vague, you are buying a demo. Let's take a software company as an example. They sell a monthly tool to plumbing businesses. An AI co-worker that watches the software company's CPA (cost per acquisition) and waits for approval on the spend is still agentic. It is also usable on a live account.
Lemonado
How Lemonado helps with agentic work
Lemonado is not an agentic platform in the pitch-deck sense. It is an AI co-worker that can take multi-step jobs: watch CPA (cost per acquisition), flag fatigue, rebalance a budget you approved. You reach it in the app, in Slack, or via MCP (Model Context Protocol). Writes stay inside Lemonado. Outside AI tools stay read-only.
Tasks are how you hand those jobs off. Approval stays yours on the moves that spend money. People keep judgment, strategy, relationships, and taste.