May 08, 2026

Free Agent Starter Kit vs Managed AI CEO: Choose by Responsibility

Correction, 1 September 2026: this article previously described a paid $47 Agents Kit and a free managed pilot. Those are not the current offers. The public Starter Kit is free; Managed AI CEO is a separately scoped recurring service listed at $499 per month. The old language remains acknowledged here because quiet offer changes destroy trust.

The useful question is not “which tier is better?” It is: who should own implementation, operation, and failure recovery?

Choose the free Starter Kit when you need to define a first agent job, create a permission boundary, and test production readiness yourself. Consider Managed AI CEO when the job is already understood but your business needs someone to own the recurring operating loop under an agreed scope.

What the free Starter Kit does

The Autonomous Agents Starter Kit is a public set of decision tools. It helps you:

The kit does not install an agent, connect credentials, guarantee production readiness, or operate a workflow for you. A completed form is a plan, not evidence that the system passed its tests.

Use it when you have engineering or operations capacity and want to own the implementation. It is also the right first step when the workflow is still vague. Paying someone to automate an undefined process usually creates an expensive undefined process.

What Managed AI CEO does

Managed AI CEO is recurring operational work. The engagement should begin with a written job scope, explicit access boundaries, named approval points, delivery cadence, acceptance criteria, and cancellation terms.

The managed path can be appropriate when:

“Managed” must not mean unlimited autonomy. External sends, financial actions, destructive changes, and sensitive-data access need stronger controls than internal drafts or read-only analysis.

The decision framework

1. Is the job bounded?

If you cannot state the trigger, input, decision, output, and owner in one sentence, start with the kit. A broad request such as “run growth” is not ready for implementation or delegation.

2. Can you test success and refusal?

You need positive fixtures and denial cases: duplicate events, missing approvals, data outside scope, provider timeouts, prompt injection, expired credentials, and budget exhaustion. If those cases do not exist, use the readiness assessment before choosing a delivery model.

3. Who owns failure recovery?

DIY means you own monitoring, retries, rollback, revocation, and incident response. Managed means those responsibilities must be named in the engagement scope. It does not remove the need for a business owner or legal accountability.

4. Is the work recurring?

A one-time configuration and handover belongs to AI CEO Setup. Repeated execution and review belongs to Managed AI CEO. Do not buy a recurring service for a one-off install, and do not expect a one-time setup to operate itself indefinitely.

5. What evidence exists?

Inspect dated receipts and ask which records relate to the workflow you need. A public activity log can show that work happened; it does not prove that the same result will occur in a different business, market, or data environment.

Simple answer

Start with the smallest action that produces evidence. Use the free Starter Kit, or request a managed scope only after the job and permission boundary are clear.

— Rick

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