Deciding how to integrate AI agents into business operations presents a fundamental choice: build internally, configure a platform, or opt for managed delivery. Each approach carries distinct implications for resource allocation, risk management, and operational control. Understanding these differences is crucial for executives and transformation leaders aiming to enhance operational capacity effectively.
This guide provides a workflow-specific framework to evaluate these options, moving beyond generic build-versus-buy considerations. We will examine the conditions under which each model excels, the evidence required for successful implementation, and the critical accountabilities that always remain with the buyer, regardless of the chosen delivery method.
Defining Your AI Agent Operating Model
The choice of an AI agent operating model is paramount for successful implementation and sustained value. It is not merely about technology, but about aligning your organizational structure, resources, and risk appetite with the specific demands of the workflow. Each model—internal build, platform configuration, or managed delivery—offers distinct advantages and challenges.
A pragmatic approach begins with a thorough diagnosis of the target workflow. Identifying high-friction points, understanding data sensitivity, and assessing internal capacity are critical first steps. This diagnostic phase ensures that the selected model genuinely addresses operational needs rather than simply adopting a trendy solution, leading to more effective deployment.
When to Build AI Agents Internally
Internal build is the preferred model for workflows that are highly unique, involve proprietary intellectual property, or are core to your competitive advantage. This approach grants maximum control over customization, security, and integration with legacy systems. It demands significant investment in specialized AI talent, infrastructure, and ongoing maintenance capabilities.
Success with an internal build hinges on robust internal capacity and a clear governance strategy. Organizations must possess dedicated engineering teams, data scientists, and AI ethicists. The ability to manage the entire lifecycle, from development to continuous improvement and risk mitigation, is non-negotiable for this resource-intensive approach.
- High IP sensitivity
- Unique workflow requirements
- Deep in-house AI expertise
- Full control desired
Leveraging Platform Configuration for AI Agents
Platform configuration offers a balanced approach for standardized workflows that benefit from AI agent capabilities but do not require ground-up development. This model involves leveraging existing AI platforms or low-code/no-code tools to configure agents. It accelerates deployment and reduces the need for extensive coding expertise compared to an internal build.
This option is best suited when workflows have moderate complexity and can adapt to platform capabilities. While configuration reduces development burden, it still requires internal technical staff to manage the platform, integrate with existing tools, and ensure the agent's performance. The buyer retains accountability for accurate configuration and data handling.
- Standardized workflows
- Moderate customisation
- Faster deployment
- Existing platform use
The Managed Delivery Model for AI Agents
Managed delivery provides a distinct operating model for organizations facing complex, cross-functional workflows with constrained internal delivery capacity. This approach involves partnering with an external provider, like Kaza, to diagnose workflows, design, deploy, and continuously improve AI agents. It reduces the internal burden of cross-functional coordination and technical execution.
Crucially, in a managed delivery model, the buyer retains all decision rights, risk acceptance, process ownership, and evidence requirements. The provider acts as an extension, offering execution capacity while the organization maintains strategic oversight and accountability for outcomes. This model requires clear performance metrics and defined human review loops for effective governance.
- Complex, high-friction workflows
- Constrained internal capacity
- External expertise needed
- Buyer retains full accountability
Ensuring Governance and Accountability Across Models
Regardless of the chosen operating model, robust governance is paramount. Organizations must establish clear frameworks for AI agent development, deployment, and oversight. This includes defining ethical guidelines, data privacy protocols, and mechanisms for human review and intervention. The NIST AI Risk Management Framework [1] provides a voluntary guide for incorporating trustworthiness.
Buyer accountability is non-negotiable. For every AI agent, the organization remains responsible for its operational impact, data security, and compliance with regulations. This involves setting performance thresholds, monitoring outcomes, and being prepared to intervene or adapt the agent as needed. Clear evidence requirements must be established from the outset to demonstrate responsible operation.
- Clear governance frameworks
- Data privacy protocols
- Human review mechanisms
- Continuous monitoring
To make the next decision regarding your AI agent operating model, you must first assess your workflow's unique characteristics and your organization's internal capacity. If your workflow is highly unique and you possess deep in-house AI expertise, an internal build may be appropriate. Conversely, if you face complex, cross-functional workflows with constrained internal delivery capacity, a managed delivery model warrants serious consideration, provided you are prepared to retain full decision rights and oversight.
Frequently asked questions
What is the primary difference between platform configuration and managed delivery?
Platform configuration relies on your internal team to set up and maintain agents using existing tools. Managed delivery involves an external partner handling the diagnosis, design, deployment, and ongoing improvement, significantly reducing your internal execution burden while you retain strategic oversight.
How does 'retained buyer accountability' work in a managed delivery model?
Even with managed delivery, your organization remains responsible for the AI agent's overall impact. This means defining desired outcomes, accepting risks, owning the underlying business process, and ensuring compliance. The provider executes, but you govern and oversee the results.
What kind of evidence should I prepare before deciding on an AI agent model?
Gather detailed workflow maps, document specific pain points, identify data sources and access protocols, and assess your internal team's current capacity and expertise. Clear performance metrics for the target workflow are also essential for any model.
Can I switch models if my needs change after initial deployment?
Yes, but switching models can incur significant costs and disruption. It's crucial to thoroughly diagnose your workflow and anticipate future needs during the initial decision phase. A well-defined strategy upfront minimizes the need for costly transitions later on.
