Define prompt chaining in the context of agentic AI.

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Prompt chaining in the context of Agentic AI refers to the technique of connecting multiple prompts together so that the output of one prompt becomes the input to the next. This creates a structured sequence of reasoning or task execution, allowing an AI agent to solve complex problems step by step instead of trying to generate everything in one shot.

How It Works

1. Decomposition – A big task is broken down into smaller subtasks.

2. Chaining – Each subtask is handled by a separate prompt. The response is passed along the chain.

3. Iteration – The agent refines or builds on previous outputs until the goal is achieved.

Example

Suppose an agent needs to write a blog post:

• Prompt 1: Generate an outline.

• Prompt 2: Expand each heading into a detailed section.

• Prompt 3: Proofread and improve tone.
  Each step feeds into the next, forming a chain of prompts.

Role in Agentic AI

• Complex Reasoning: Agents can solve multi-step problems more effectively.

• Memory & Context Handling: By chaining, agents keep track of intermediate outputs.

• Modularity: Easier to debug, update, and optimize smaller steps.

• Autonomy: Enables AI agents to act like workflows—planning, executing, and refining actions automatically.

Applications

• Research assistants (breaking down research → summarizing → generating insights).

• Coding agents (plan → generate code → test → debug).

• Business automation (extract data → analyze → generate report).

Summary

Prompt chaining is a workflow design pattern in agentic AI where multiple prompts are linked together, allowing agents to perform structured, stepwise reasoning and handle complex, multi-stage tasks more reliably than with a single large prompt.

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