AI agentic workflows

AI agentic workflows are structured sequences of autonomous actions and decision-making processes that enable AI agents to complete complex, multi-step tasks without human intervention. These workflows orchestrate intelligent agents through dynamic task decomposition, where agents analyze objectives, plan execution strategies, select appropriate tools, and adapt their approach based on real-time feedback and changing conditions. Unlike traditional rule-based automation, agentic workflows incorporate reasoning capabilities, allowing agents to handle exceptions, make contextual decisions, and collaborate with other agents or systems. Key components include goal-oriented planning, tool selection and usage, memory management for maintaining context across workflow steps, and error handling mechanisms. These workflows can span multiple domains simultaneously, such as data retrieval, analysis, communication, and action execution, while maintaining transparency through audit trails and decision logging. Implementation typically involves orchestration frameworks, agent coordination protocols, and integration with existing business systems, enabling organizations to automate complex processes that previously required human cognitive abilities and judgment.