Designing AI Agent Architecture and SDLC Integration

As AI agents transition from simple assistants to active contributors, understanding how to design their architecture and safely integrate them into your development pipeline is a critical modern skill. Operating autonomous systems within a codebase requires a delicate balance of automation and control. This written course guides you through the foundational concepts of agentic systems, from basic planning and tool-use architectures to automated repository integration. What you'll learn: - Understand the core components of AI agent architecture, including planning, state management, and tool calling. - Configure secure GitHub workflows, branch protections, and repository rules tailored for autonomous agents. - Implement automated CI/CD checks to validate and test agent-generated code modifications. - Apply evaluation patterns and safety guardrails to monitor and control agent behavior. - Design structured pull request environments that seamlessly combine machine execution with human oversight. We start with key terminology and foundational concepts of autonomous agents before exploring architectural design, safety patterns, and step-by-step SDLC integration. This course is designed for software engineers, system designers, and tech-savvy beginners looking to understand the future of automated development, with no prior AI engineering experience required. Start reading today to build secure, reliable, and automated development workflows.