Choosing an AI marketing agent

Assistant, agent, automation: what's the difference?

Vendors use “agent” for almost anything with AI in it. Three distinct things hide behind the word:

You usually want all three. Automations are predictable and cheap for recurring checks. Assistants are best for open questions. Agents shine on multi-step jobs that are tedious but well-defined. See AI agent in the glossary.

What marketing agents do well today

• Cross-platform analysis: pull Google Ads, Meta, Microsoft Ads and GA4 into one answer about where performance changed and why.
• Account hygiene: sweep for wasted spend, disapproved ads, broken landing pages and tracking gaps.
• Drafting: ad variants, negative lists, SEO titles and meta descriptions, client report commentary.
• Routine changes: pausing, budget adjustments within caps, adding negatives, creating paused ads.
• SEO legwork: finding striking-distance queries and drafting page fixes for review.

What they still do badly: anything that needs context outside the data, such as margins, inventory, a client's mood, or what the brand would never say. That's why the checklist below cares more about controls than about clever demos.

The buyer's checklist

1. Reach: what can it actually touch?

List the platforms you need and check each one is supported for both reading and the writes you want, and whether it's available now or pending a platform review. Ask how it signs in: per-person OAuth is better than shared API keys.

2. Control: what happens before a change?

This is the question that separates tools. Look for: an exact preview (before and after values), a human approval step that the agent cannot skip, role-based permissions for who can approve, and the option to keep approvals out of chat entirely. In MegaMCP, every write is a plan a person approves; see change approval.

3. Guardrails: what stops a bad day?

Server-side spend caps per account that the agent can't edit, safe defaults (new ads created paused), plan expiry so stale proposals can't be applied later, and undo for reversible changes. See guardrails.

4. Visibility: can you see what it did?

An audit log of every tool call, approval and reversal, with who asked and from which app. Agencies also need per-client scoping, so an agent working on one client can't touch another.

5. Cost: how is it priced?

Three models dominate: per-seat or flat plans, credits or “AI units” per action, and bring-your-own-key (BYOK) where your AI provider bills you directly. Credits make heavy analysis hard to budget. MegaMCP uses flat plans (Solo $19/mo, Crew $149/mo for 10 seats) with no per-run credits; your AI app or provider bills its own usage.

6. Where it runs

Some agents live only in the vendor's interface. Others work inside the AI app you already use (Claude, ChatGPT, Cursor) through MCP, plus a hosted agent for scheduled or unattended work. Being able to use both means you don't have to change how your team works.

7. Proof

Ask for a sandbox or trial with fake money and run your own test: a real question you already know the answer to, and a change you reject, approve and then undo.

Red flags

• “Fully autonomous” budget changes with no approval option.
• Setup that asks you to paste ad-platform API keys into a shared file.
• No way to see what the agent changed last Tuesday.
• Pricing you can't estimate before the month starts.
• Claims of platform support that are “coming soon” for the platforms you need.

Running a two-week pilot

1. Pick two jobs.

   One reporting job (the weekly review) and one change job (search term negatives). Write down how long each takes today.

2. Connect read-only first.

   Week one: questions and reports only. Check answers against your own numbers.

3. Set guardrails, then allow writes.

   Week two: spend caps on, approvals required, and only the one change type.

4. Measure.

   Time saved, proposals approved versus rejected, any change you had to undo, and whether the team kept using it unprompted.

MegaMCP's 5-day trial includes a sandbox ad account with no credit card. For a longer pilot, the Free plan keeps read-only access at 100 reads a day; continuing reviewed changes after the trial requires a paid plan.

Example prompts

Copy these into Claude, ChatGPT or Cursor with MegaMCP connected. Each lists the tools it typically calls; more are in the prompt library.

• Reads data

  Across every ad account, what changed most in the last 7 days compared with the 7 before? Give me the three things worth acting on.

  Tools it calls: megamcp_cross_channel_summarygoogle_ads_anomaliesmicrosoft_ads_anomalies

• Proposes a change

  Find wasted spend in Google Ads and propose negatives, but group them by theme first so I can review faster.

  Tools it calls: ads_waste_sweepgoogle_ads_add_negative_keywords

  Any change becomes a plan you review and approve. Nothing moves until you do.

• Reads data

  Which Search Console queries are we close to ranking for, and which WordPress pages should we update?

  Tools it calls: gsc_seo_opportunitieswordpress_get_content

• Reads data

  What did we approve and apply for this client in the last month, and what were the results?

  Tools it calls: megamcp_search_historymegamcp_get_outcomes