Tag
The article shares seven practical code-based guards to make AI agents more reliable by mitigating failures, such as verifying actions before execution and ensuring state consistency, which complement prompt improvements.
The author experimented with a multi-agent workflow using oh-my-subagents to build a product over five days, emphasizing that supervision in such systems makes failures visible and recoverable rather than eliminating them.
The article discusses strategies for capping retries in AI agents to balance cost and performance, emphasizing the need to differentiate between retryable errors and cases requiring escalation to more capable models in production.