Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley
Frank Coyle reviews six production scenarios from Anthropic's Claude Certified Architect exam, demonstrating what NOT to do when building with AI agents.
A critical mistake is simply using the model's response directly—you must instead check the "stop reason" to know whether the model actually succeeded in running a tool or ran out of tokens. Other common errors include giving a single agent too many tools (better to have small specialized agents with one or two tools each), and allowing agents to see each other's thinking process (which causes them to converge on the same idea). Coyle also shows practical tricks like running batch jobs for half the token cost, and isolating subtask results in separate context rows to save tokens.
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