Developing in Agentic AI Systems

The exam tests your ability to develop, integrate, oversee and govern AI agents in production-ready GitHub-based SDLC workflows. You are assessed on topics such as agent architecture, tool use and execution environments, memory and state management, and evaluating and tuning agent results. The exam also covers coordinating multi-agent systems and implementing guardrails, permissions and accountable approval processes. Passing the exam shows employers and customers that you can use GitHub as the control plane for secure, reliable and efficient agentic AI systems.

Exam focus areas:

• Prepare agent architecture and SDLC processes (15–20%)
• Implement tool use and environment interaction (20–25%)
• Manage memory, state, and execution (10–15%)
• Perform evaluation, error analysis, and tuning (15–20%)
• Orchestrate multi-agent coordination (15–20%)
• Implement guardrails and accountability (10–15%)