Agentic AI Tools for Marketing: How to Tell a Real Agent From a Relabelled Chatbot

In June 2025 Gartner estimated that only about 130 of the thousands of vendors marketing agentic AI were offering the real thing. The rest, in its phrase, were “agent washing”: relabelling assistants, chatbots and older automation. The same release predicted that more than 40% of agentic AI projects would be cancelled by the end of 2027, because of rising costs, unclear business value or inadequate risk controls.

Marketing software is full of the label. Writing assistants, schedulers and template libraries now describe themselves as agentic. This gives you a way to see what a tool actually does, a trial task that exposes the difference, and a list of actions no marketing agent should take on its own.

What “agentic” should mean

The clearest working definition comes from Anthropic’s engineering guide, Building effective agents (December 2024). It separates two kinds of system:

• Workflows, where language models and tools are “orchestrated through predefined code paths”. Someone designed the steps in advance.
• Agents, where models “dynamically direct their own processes and tool usage”. The system decides what to do next based on what it has found.

In practice, an agent chooses its next step, uses tools to gather information or act, checks the result, and recognises when to stop or ask for help. None of that depends on a chat window, on how many models sit behind the product, or on the word “autonomous” in its name.

The same guide recommends finding “the simplest solution possible” and adding complexity only when needed. Agents cost more and take longer per task, and their mistakes can compound across steps. A tool that is not agentic may be the better choice.

A five-rung ladder for marketing tools

Most products marketed as agentic sit on rung 2 or 3. That is not a criticism: a Monday content workflow is easier to predict, cheaper to run and simpler to audit than an agent doing the same job. The problem is paying agent prices, and accepting agent-sized risk, for work a workflow would do.

Higher rungs earn their cost when the task cannot be scripted in advance. Investigating a drop in engagement is one example, because the useful next step depends on what the last step found. Rewriting a release note in three formats is not.

A trial task that exposes the difference

Feature lists will not tell you which rung a product is on. Give every tool you are evaluating the same goal-shaped task, with access to the same real accounts and data:

Our LinkedIn posts received fewer replies in [month] than in [previous month]. Find out why, and propose three posts for next week. Do not publish anything.

Then watch how it works, not just what it produces:

Plant one contradiction in the material to test the third row. For example, include a note that the main author was on holiday for two weeks of the slower month. An agent should account for that before blaming topics or timing.

Where an agent must stop and ask

The more a system decides for itself, the more carefully you need to draw the line around what it may do. In marketing, these actions should always wait for a named person’s approval:

• Publishing anything under a person’s name or the company’s.
• Sending emails, direct messages or replies.
• Spending money, including adjusting ad budgets or bids.
• Deleting posts, contacts, files or campaigns.
• Engaging from someone’s account, such as liking, commenting or resharing.

The last one is also a platform rule. Section 8.2 of LinkedIn’s User Agreement prohibits bots and unauthorised automated methods that create, comment on, like, share or re-share posts. An agent engaging from employee accounts breaks it however well the agent works.

Approve the artefact itself. Approving “three posts about onboarding next week” tells you little. Approving the exact text, image and time of each post is what protects you. A plan can work as a briefing that you amend.

Failure modes that only agents have

Each of these deserves a direct question to the vendor.

Compounding errors. A wrong assumption at step two shapes every step after it. Ask: can I see the intermediate steps, and can I correct one without starting again?

Stale context. An agent that remembers your brand can also remember last quarter’s pricing. Ask: where does it get facts from, and how do I update or remove one?

Silent partial success. “Scheduled” is not the same as “published”. Ask: how does the agent confirm that a post actually went live, and what happens if it did not?

Loops and cost. An agent can retry a failing step many times. Ask: what is the limit on iterations or spend per task, and who is told when it is reached?

A vendor that answers these with a demonstration, rather than a reassurance, is worth a longer trial.

Agentic AI in employee advocacy

Employee advocacy is a good test of where agents help, because the boundaries are clear. An agent can arrange short interviews, prepare a source pack for each contributor, draft from their own words, check drafts against those sources, and collect the questions readers asked. It should not post for people, engage on their behalf or decide what they believe.

Choosing the best agentic AI for your team

No product is best in general, because the right rung depends on the task. Start with the lowest rung that does the job. Run the goal-shaped trial on the tools that claim more. Before signing, check the stop list and the four failure-mode questions.

FeedSquad, which publishes this blog, builds a multi-agent marketing system on these rules: agents research, plan and draft, and a person approves each post before it is published.

Sources

• Anthropic: Building effective agents (19 December 2024)
• Gartner: Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 (25 June 2025)
• LinkedIn: User Agreement, section 8.2 (effective 3 November 2025)