AI Agent & Workflow Patterns FAQs

Question 1

What are the best practices for LLM tool design?

Accepted Answer

Tools should be self-contained, clearly described to guide the LLM, error-robust (returning informative errors instead of crashing), and scoped to perform a single specific task well.

Question 2

When should I use an agent instead of a workflow?

Accepted Answer

Use workflows for predictable, repeatable tasks with predefined code paths. Use agents for open-ended, adaptive problems where the LLM must dynamically determine the next steps and tool usage.

Question 3

What is the ReAct pattern in AI agents?

Accepted Answer

ReAct (Reasoning and Acting) is a pattern where the LLM alternates between explicit reasoning steps (thoughts) and taking actions (tool calls) to solve complex, multi-step tasks.

Question 4

How does the orchestrator-worker pattern work?

Accepted Answer

A central LLM orchestrator decomposes a complex task into smaller subtasks, delegates them to specialized worker LLMs, and then synthesizes the individual results into a coherent final output.

Question 5

How can I prevent runaway costs or loops in autonomous agents?

Accepted Answer

Implement strict resource limits including maximum iteration caps, token usage limits, and human-in-the-loop approvals for expensive or high-impact tool executions.

AI Agent & Workflow Patterns

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Key Features

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AI Agent & Workflow Patterns

About

Key Features

Use Cases