Patterns for Building AI Agents

<p><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>The gap between prototype and production is where most AI teams stumble. Written by the co-creator of a popular agent framework </span><em style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Patterns for Building AI Agents</em><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> captures practical strategies emerging in the year of agents:</span></p><p></p><ul><li><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Agent design patterns</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>: Evolving architectures creating dynamic agents and building human-in-the-loop workflows</span></li><li><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Context engineering</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>: Parallelization context compression and avoiding failure modes</span></li><li><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Eval workflows</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>: Creating eval suites cross-referencing failure modes with metrics and leveraging domain experts</span></li><li><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Security fundamentals</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>: Preventing prompt injection sandboxing code execution and implementing agent access control</span></li></ul><p></p>