ai-agents-engineering

To build production AI agents, you orchestrate multi-agent systems using frameworks like CrewAI and LangGraph. This approach handles complex state management, allowing agents to reason, act, and learn from interactions in a scalable production environment.

Implementing memory systems in AI agents requires dividing architecture into short-term and long-term memory. This division allows agents to maintain context during immediate interactions and retain learned information for future reasoning and production deployment.

The ReAct pattern enables AI agents to reason and act simultaneously, while Plan-and-Execute separates planning from execution. Choosing between them depends on whether your multi-agent workflow needs dynamic reasoning or structured task execution.

Yes, designing tools for multi-agent systems requires proficiency in AI frameworks and production systems. It involves integrating custom tools into workflows while managing complex state machines and maintaining observability.

Production patterns for AI agents require implementing observability, error handling, and cost control. These patterns ensure stable deployment by providing decision frameworks to manage complex state machines and multi-agent interactions.

You should avoid multi-agent orchestration when your workflow lacks complex state management needs or cannot support production overhead. If tasks require simple reasoning without tool design or memory systems, a single agent approach is better.