Agentic Workflows
Agentic Workflows are automated processes orchestrated by AI agents that execute multi-step tasks, make decisions between steps, and adapt to outcomes. They combine language understanding with action execution.
Definition
Agentic workflows chain together multiple operations: the agent perceives a situation, decides on an action, executes it, observes the outcome, and decides on the next step. Unlike linear pipelines that execute the same sequence every time, agentic workflows adapt based on intermediate results.
Examples include customer service agents that troubleshoot issues by checking documentation and system status, or research agents that investigate topics by performing multiple searches and synthesizing findings. These workflows enable AI systems to handle complex, multi-step processes that require decision-making.
Why it matters for AI visibility
Agentic workflows in answer engines mean your brand's content might be checked, validated against other sources, and compared side-by-side with competitors within a single answer generation process. Your claims will be verified, and comprehensive, well-supported content is more likely to survive agent scrutiny.
Related terms
AI Agents
AI Agents are autonomous systems that use LLMs to perceive their environment, make decisions, and take actions to accomplish goals. They can use tools, run code, and execute multi-step workflows without human intervention.
AIAgentic Search
Agentic Search refers to search and answer engines powered by AI agents that actively research topics by performing multiple searches, visiting pages, and synthesizing information autonomously.
AIFunction Calling (Tool Use)
Function Calling is the capability of LLMs to call external functions or APIs as part of generating responses. It enables agents to take actions, query databases, or access real-time information beyond the model's training.
AIAI Inference
AI Inference is the process of running a trained model on new input to generate output. It is the runtime execution phase, as opposed to training, where the model learns patterns from data.