Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
Agentic workflows represent the shift from AI as a question-answering tool to AI as a task-completion system. In an agentic workflow, the model receives a high-level objective, breaks it into subtasks, selects and invokes tools, evaluates intermediate results, and adjusts its approach until the goal is achieved. This loop of plan-act-observe-reflect is what makes agents genuinely useful for real work.
Growth teams benefit from agentic workflows in scenarios like automated competitor research, multi-channel campaign setup, or data pipeline debugging. The key architectural decision is how much autonomy to grant: fully autonomous agents move fast but can go off-track, while human-in-the-loop checkpoints add latency but ensure quality. Start with tightly scoped workflows where the agent has clear success criteria, then gradually expand autonomy as you build confidence in the system's reliability.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.
Tree of Thought
An advanced reasoning framework where the model explores multiple solution paths simultaneously, evaluates each branch, and selects the most promising approach. Tree of thought enables more thorough problem-solving than linear chain-of-thought reasoning.