Olivier Wulveryck presented a framework on June 22, 2026, for applying Team Topologies to agentic platforms, focusing on how organizations can distribute cognitive load when using AI agents to produce applications. The approach addresses the shift where agents, unlike human roles that work sequentially, operate in parallel and produce outputs continuously, creating a cognitive throughput problem. The framework proposes that a platform should absorb this technical complexity, allowing business teams to focus on domain-specific decisions while automating guardrails for security, brand consistency, and reliability.

The model leverages four team types to organize agentic production. Stream-aligned teams, often composed of non-technical business domain experts, define business intent and orchestrate agents. Platform teams industrialize systemic capabilities, providing instructions and guardrails as X-as-a-Service, while complicated subsystem teams handle advanced technical tasks like model optimization, cost management, and fine-tuning. Enabling teams serve a temporary role to bridge knowledge gaps, focusing on environment provisioning and training until product teams reach autonomy.

Success in this model depends on platform maturity, measured by criteria such as the automated coverage of guardrails, the reliability of CI/CD pipelines, and the auditability of deployment decisions. As maturity increases, the primary interaction mode shifts toward X-as-a-Service, where capabilities are consumable without friction. The framework also emphasizes governance to prevent shadow IT, suggesting that ease of production must be balanced with centralized oversight of the application portfolio and lifecycle management.

To scale development, Wulveryck suggests a graduation path where repetitive guardrails identified by three or more teams are promoted to systemic platform features. This allows organizations to move from team-specific solutions to industrialized capabilities. Ultimately, the framework aims to regulate the dynamic load of agentic production, ensuring that human judgment remains the driver for structural decisions while technical risks are handled by an automated, self-service infrastructure.

Olivier Wulveryck presented a framework on June 22, 2026, for applying Team Topologies to agentic platforms, focusing on how organizations can distribute cognitive load when using AI agents to produce applications. The approach addresses the shift where agents, unlike human roles that work sequentially, operate in parallel and produce outputs continuously, creating a cognitive throughput problem. The framework proposes that a platform should absorb this technical complexity, allowing business teams to focus on domain-specific decisions while automating guardrails for security, brand consistency, and reliability.

The model leverages four team types to organize agentic production. Stream-aligned teams, often composed of non-technical business domain experts, define business intent and orchestrate agents. Platform teams industrialize systemic capabilities, providing instructions and guardrails as X-as-a-Service, while complicated subsystem teams handle advanced technical tasks like model optimization, cost management, and fine-tuning. Enabling teams serve a temporary role to bridge knowledge gaps, focusing on environment provisioning and training until product teams reach autonomy.

Success in this model depends on platform maturity, measured by criteria such as the automated coverage of guardrails, the reliability of CI/CD pipelines, and the auditability of deployment decisions. As maturity increases, the primary interaction mode shifts toward X-as-a-Service, where capabilities are consumable without friction. The framework also emphasizes governance to prevent shadow IT, suggesting that ease of production must be balanced with centralized oversight of the application portfolio and lifecycle management.

To scale development, Wulveryck suggests a graduation path where repetitive guardrails identified by three or more teams are promoted to systemic platform features. This allows organizations to move from team-specific solutions to industrialized capabilities. Ultimately, the framework aims to regulate the dynamic load of agentic production, ensuring that human judgment remains the driver for structural decisions while technical risks are handled by an automated, self-service infrastructure.