Frank Coyle presents the Claude Certified Architect exam as a map of production skills for agentic AI rather than merely a credential. Its scenario-based domains cover agent architecture, Claude Code configuration, prompt and output structure, tool and Model Context Protocol design, context management and reliability, with anti-patterns used to show why plausible implementations fail.
Frank Coyle explains that a language model proposes tool calls but external code executes them, so an agent loop must inspect the stop reason, run the requested tool, return the result and check for incomplete output or token limits. Human escalation remains appropriate when confidence is low. Hierarchical project instructions can constrain coding behavior at global, project and directory levels.
Frank Coyle recommends small, specialized agents with limited tools and isolated context rather than one overloaded agent or unrestricted transcript sharing. Context forks, summaries and compaction keep long tasks manageable and reduce groupthink, while continuous-integration workflows should avoid interactive pauses and may use batch processing when latency is acceptable. His broader advice is to learn by building, observing failures and turning them into explicit anti-patterns.
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