Building Agentic Workflows

From Chatbots to Agents

A standard LLM application is a single-turn process: input goes in, output comes out. An Agent, on the other hand, is an autonomous loop. It uses an LLM as its “brain” to determine a sequence of actions, observe the results, and course-correct until the goal is achieved.

The ReAct Framework

The most popular paradigm for agents is ReAct (Reasoning and Acting). The prompt is structured to force the LLM to write down its thoughts before deciding on an action.

Thought: I need to find the company's Q3 revenue. First, I should search the web.
Action: web_search
Action Input: "Apple Q3 2024 revenue"
Observation: Apple posted $85.8 billion in revenue for Q3 2024.
Thought: Now I need to compare it to Q2.
...

Routing and Multi-Agent Systems

Complex tasks often confuse a single, monolithic agent. Modern architectures use Multi-Agent Systems. You might have a “Researcher Agent” that browses the web, a “Coder Agent” that writes scripts, and a “Manager Agent” that delegates tasks and reviews the final output. Frameworks like LangGraph, AutoGen, and CrewAI make building these workflows significantly easier.

Common Failure Modes

Agents are incredibly powerful but prone to infinite loops. They might get stuck trying the same failed API call 10 times. Always implement max_iterations limits and ensure that error messages are passed back to the agent so it can understand why an action failed and try a different approach.