Agentic AI Design Patterns

When building agentic AI systems, developers use several key design patterns. Let's explore each one:

1. Goal Decomposition Breaking complex objectives into smaller, manageable tasks. This allows agents to tackle problems systematically.

Example: An AI travel planner decomposing "Plan a vacation" into:

• Research destinations
• Check flight availability
• Find accommodations
• Create itinerary
• Book reservations

2. Reinforcement Learning Training agents through a system of rewards and penalties to optimize behavior over time.

3. Hierarchical Planning Organizing tasks into multiple levels, from high-level strategy to low-level actions.

4. Multi-Agent Coordination Enabling multiple AI agents to collaborate toward common goals.

When would you use multi-agent coordination?

When a problem requires diverse expertise or parallel processingWhen you have a simple, single-task problemWhen you want to reduce system complexityWhen working with limited computational resources

Introduction to Agentic Frameworks

Several frameworks help developers build agentic AI systems. Each has unique strengths:

• LangChain: Provides tools for building language model applications
• LangGraph: Specializes in creating stateful, multi-agent workflows
• AGNO: Focuses on autonomous goal-oriented agents
• CrewAI: Enables role-based multi-agent systems
• Semantic Kernel: Microsoft's framework for AI orchestration

For building complex workflows with multiple decision points, which framework would be most suitable?

LangGraphLangChainAGNOSemantic Kernel