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### Key components of agentic workflows
1. Task Decomposition: Central to agentic workflows is the division of complex tasks into smaller subtasks. This modular approach allows for specialized agents to manage distinct aspects of a problem, enhancing efficiency and accuracy in execution. 2. Iterative Learning: AI agents within these workflows don’t settle for a single output; they refine their actions through iterative cycles, learning from previous steps and improving their outputs. This iterative refinement is especially prominent in applications like code generation, where multiple agents generate, review, and test code until an optimal solution is reached. 3. Tool Integration and External Resources: Modern agentic workflows incorporate various external tools, allowing AI agents to perform diverse actions such as data gathering, real-time analysis, or even automated task execution beyond their native capabilities. This enhances their versatility and ability to adapt to evolving task requirements. 4. Multi-Agent Collaboration: In advanced workflows, multiple agents collaborate, each focusing on specific roles or subtasks. For example, in supply chain management, one agent may predict demand while another optimizes inventory and logistics. This collaborative dynamic enhances operational efficiency and allows for parallel processing, increasing overall system effectiveness.

### Key components of agentic workflows

1. Task Decomposition: Central to agentic workflows is the division of complex tasks into smaller subtasks. This modular approach allows for specialized agents to manage distinct aspects of a problem, enhancing efficiency and accuracy in execution.

2. Iterative Learning: AI agents within these workflows don’t settle for a single output; they refine their actions through iterative cycles, learning from previous steps and improving their outputs. This iterative refinement is especially prominent in applications like code generation, where multiple agents generate, review, and test code until an optimal solution is reached.

3. Tool Integration and External Resources: Modern agentic workflows incorporate various external tools, allowing AI agents to perform diverse actions such as data gathering, real-time analysis, or even automated task execution beyond their native capabilities. This enhances their versatility and ability to adapt to evolving task requirements.

4. Multi-Agent Collaboration: In advanced workflows, multiple agents collaborate, each focusing on specific roles or subtasks. For example, in supply chain management, one agent may predict demand while another optimizes inventory and logistics. This collaborative dynamic enhances operational efficiency and allows for parallel processing, increasing overall system effectiveness.