Building deterministic MCP Agents

About this course

Learn to build deterministic AI agents using the Model Context Protocol (MCP) and structured quality metrics for repeatable, verifiable outputs. You will explore PMAT as a quality assessment tool for software projects, applying lean manufacturing principles from the Toyota Way including continuous improvement and waste elimination to software quality engineering. The course covers the certainty-scope tradeoff for balancing test coverage and confidence, finite state machine models for deterministic agent behavior, and MCP protocol architecture for structured agent-tool communication. You will analyze survivorship bias in programming language popularity rankings and apply six essential quality metrics for comprehensive project assessment and automated scoring. The testing module covers six essential test types for agent validation, property-based testing for verifying behavioral invariants, and fuzz testing for discovering edge cases using agentic AI. You will use Claude Code as an MCP client integrated with PMAT for automated quality analysis and walk through real-world project examples demonstrating quality scoring across multiple codebases. By completing this course, you will be able to design deterministic agent systems using MCP, apply comprehensive quality metrics with PMAT, and implement property and fuzz testing strategies for robust agent validation.

Course objectives

• understand the certainty-scope tradeoff
• analyze survivorship bias in programming language popularity
• design agent-tool communication architectures