Getting AI Agent Architecture Right with MCP (decodingml.substack.com)

The third lesson of the open-source course *Designing Enterprise MCP Systems* focuses on mastering AI agent architecture to build scalable, maintainable AI-powered developer workflows without unnecessary complexity. Using a real-world example of an AI Pull Request (PR) Reviewer Assistant, the lesson emphasizes treating problems as workflows first rather than defaulting to multi-layered agent systems. It highlights that overly agentic designs—adding planners, dynamic routing, and memory—can inflate latency, cost, and debugging difficulty without meaningful benefit, urging developers to distinguish between deterministic steps and tasks requiring reasoning. Key technical insights include balancing agentic reasoning with straightforward workflow controls: deterministic actions (e.g., fetching diffs from a GitHub MCP server) should be handled directly, reserving AI agents for ambiguous interpretation (e.g., linking Asana tasks). The lesson breaks down five core AI agent patterns such as the Self-Reflective Agent, which improves review quality through iterative critique without excessive complexity. This framework guides engineers in weighing latency, cost, and adaptability to choose architectures fit for enterprise needs, avoiding common pitfalls like unpredictable behavior and scalability challenges. Overall, the course empowers AI/ML practitioners to build efficient, robust agents by applying principled architectural decisions tailored to the true requirements of their workflows.