Growth delivery programme
AI Agent Deployment and Managed Services
Deploy AI agents around a defined business workflow, with the inputs, review points and operating owner clear. I personally implement the agreed workflow, with optional ongoing management after deployment.
Start with the workflow the agent should support
Choose the task, available inputs and the output people need to use. Research, drafting, reporting and connected marketing operations can require different permissions and review rules. Define those differences before choosing a model or deployment approach.
An agent deployment is not permission for every action it could technically take. Agree the permitted sources, tools, destinations and human decisions, then review the implemented behavior against that scope.
What the agreed work can cover
Use-case and workflow design
Define the task, current process, intended output and evidence needed to evaluate whether the agent is useful.
Deployment and integration
Implement the agreed agent and authorized connections. Platform choices and access requirements follow the client workflow and technical constraints.
Evaluation and handoff
Review output quality, permitted actions, failure visibility and recovery. Agree the documentation and operator responsibilities needed after deployment.
Optional managed service
Scope continuing management, review and maintenance after deployment when the team needs that support. Define exactly which responsibilities remain with the client.
How the engagement comes together
Define the operating boundary
Review the business task, current process, source access and decisions requiring a person. Establish the acceptance criteria.
Implement and evaluate
Build and connect the agreed workflow, inspect the outputs and test the expected behavior and failure handling within the authorized scope.
Hand over or manage
Provide the agreed handoff, or continue under an explicitly scoped managed service. Review actual observations before expanding the workflow.
Deployment and ongoing management are separate choices
A client can commission a deployment and retain operation internally, or agree ongoing management. The choice should follow who will review output, maintain connections, investigate failures and approve changes.
Existing AI Growth System and Content Compounder offers remain relevant when the need is a connected growth or content system. A specific agent engagement can be narrower. No universal platform support, unattended external execution or measured savings is assumed.
Questions about the service
Do you deploy agents or only advise?
Yes. I personally deploy the agreed AI agent workflow and provide any ongoing management included in the scope, with responsibilities defined before work starts.
Can you manage the agents afterwards?
Yes. Optional ongoing management can be scoped after deployment. Agree the review, maintenance and client responsibilities rather than assuming a fixed service level.
Can agents take external actions automatically?
Only actions explicitly included and authorized in the scope. Define which decisions need human review, which tools and destinations are permitted and how failures are handled.
Which platforms and integrations are supported?
Select and verify the relevant platforms, source access and integration requirements for the engagement. The service does not assume that every client stack is already connected.
How is the work agreed?
Scope, responsibilities, deliverables and review points are agreed per engagement. Continuing engagements are month-to-month with no minimum term; either side can end with 30 days notice.
Related scopes and evidence
Next step
Discuss your AI agent workflow
Bring the business question, the evidence you already have and the people who will own the work. Agree the scope, implementation responsibilities and review points before starting.
Explore the decision
Related GTM implementation