How to Evaluate Agentic AI for Media Buying: The RFP Checklist

Why you need a rubric

Every vendor in paid media now markets an "AI agent." Under the label you will find execution agents, monitoring copilots, and dashboards with a chat window, and their demos look nearly identical. Write your criteria down before you watch any of them.

This checklist gives you eleven criteria, the questions to ask under each, and what a strong answer looks like. Paste it into an RFP or a vendor scorecard as-is. Score each criterion 0 to 2 (absent, partial, proven live in your accounts) and weight by what your team needs.

The checklist

1. Who does the clicking after 90 days?

The single most predictive question. Once the tool is fully adopted, is your team still operating the ad platforms (faster, with better checklists), or does the agent operate them while your team sets strategy, budgets, and guardrails, and approves?

• Ask the vendor to describe a customer's week in month four.
• Ask which tasks the agent completes with zero human steps.
• Strong answer: named workflows the agent runs end to end, with humans approving rather than doing. "We surface it and your team applies it" means your team is still the operator.

2. Real read+write execution through public APIs

• Does the product write changes into ad platforms (create campaigns, adjust budgets, pause ads), or only read and recommend?
• Does it use each platform's public API? Browser automation risks account enforcement action.
• Which platforms are read-only today? Get the list in writing.
• Strong answer: live write execution on the platforms you use, demonstrated in a sandbox or pilot account during evaluation.

3. Approval gates and role permissions

• Can writes be approval-gated per account, per user, and per action type?
• Is there a read-only mode for junior staff or clients?
• Can approval requirements differ by change size, such as budget moves above a threshold?
• Strong answer: configurable autonomy levels (draft, ask-first, autonomous) rather than one global setting.

4. Account-scoped context

• Is context (memory, connected data, conversation history) isolated per advertiser account?
• How is cross-client leakage prevented?
• Is there a portfolio-level view, and is it read-only?
• Strong answer: hard per-account scoping with an explicit, limited cross-account view.

5. Playbooks and memory

• Can you encode agency-specific rules (blocklists, QA steps, naming conventions) once and have every task honor them?
• Do rules exist at both the organization level and the account level?
• Does the system learn from how your team works and propose additions you can accept or reject?
• Strong answer: editable org-level and account-level playbooks the agent provably follows.

6. Creative and landing page generation

• Does the product generate ad creative (image, video, text)? Landing pages?
• If not, which tools will your team still need, and what is the combined cost?
• Strong answer: built-in generation, or an honest "no" with a clean handoff. Penalize vendors that imply creative capability they do not have.

7. Planning support

• Given a goal and budget, can the product propose a channel plan?
• Can it ingest an existing media plan (spreadsheet, project tool) and translate it into platform builds?
• Strong answer: both directions.

8. Self-serve trial availability

• Can your team try the product on real accounts without a sales cycle?
• If onboarding requires vendor staff, what happens when they roll off?
• Strong answer: self-serve signup with a free tier, so your evaluation is hands-on.

9. Extensibility

• Is there an API or MCP server so the agent works inside your tools (Claude Desktop, Claude Code, Cursor, internal scripts)?
• Can you bring your own model keys or choose models?
• Strong answer: documented MCP or API access. A closed UI caps what your best operators can build.

10. Transparency of agent actions

• Is every agent action logged with what changed, when, and why?
• Can you export that history for client reporting or audits?
• Do recommendations cite the underlying data?
• Strong answer: a complete activity log with rationale, available to clients.

11. Pricing transparency

• Is pricing published? If not, why not?
• What is the unit (per seat, per advertiser, per action or credit, percent of media), and how does it scale at your real account count?
• Are there onboarding fees, minimums, or annual commitments?
• Strong answer: published pricing you can model in a spreadsheet before the first call.

Copy-paste RFP block

Vendor evaluation: agentic AI for media buying
Score each 0 (absent) / 1 (partial or roadmap) / 2 (proven live in our accounts)

[ ] 1. After 90 days, the agent does the clicking; our team approves
[ ] 2. Write execution via public APIs on our platforms (list per platform)
[ ] 3. Approval gates + role permissions (draft / ask-first / autonomous)
[ ] 4. Per-account context isolation; read-only portfolio view
[ ] 5. Org- and account-level playbooks the agent follows
[ ] 6. Creative + landing page generation (or explicit exclusion + cost of gap)
[ ] 7. Planning: goal-to-plan AND plan-file-to-platform-build
[ ] 8. Self-serve trial on real accounts, free tier
[ ] 9. Extensibility: MCP server / API / model choice
[ ] 10. Full activity log with rationale, exportable
[ ] 11. Published pricing; unit economics at our account count

Red flags

• The demo uses only fictional accounts and the vendor cannot run against a sandbox of yours.
• "AI-powered" claims with no write access anywhere. That is a reporting tool.
• Integration lists that mix live, beta, and roadmap without labels.
• Pricing that cannot be stated until "we understand your needs."
• No logged rationale for changes the agent makes.

How Synter answers this checklist

We publish this rubric because we score well on it. Synter executes across 20+ platforms via public APIs, ships draft, ask-first, and autonomous modes, generates creative and landing pages, plans from a goal or a plan file, and exposes an MCP server so the agent runs in Claude Desktop, Claude Code, or Cursor. Pricing is published: 1,000 free credits per month, then $25 per 1,000, pay as you go. Where a copilot fits your team better, the checklist will show that too.

For the category landscape, read AI Agents for Media Buying: What's Real in 2026. For head-to-heads, see Synter vs Kovva and the Kovva alternative breakdown.