Agentic Prompting

Introduction

Agentic prompting turns the model into a decision-maker that picks tools, calls them, observes results, and decides what to do next. It's the ReAct loop.

Beginner analogy: Like a new employee with access to your tools — you tell them the goal, they figure out the steps.

Understanding the topic

Core concepts to understand:

• Loop: Thought → Action → Observation → Thought → ...
• Prompt includes available tools + when to use each.
• Stops when goal achieved or max steps reached.
• Powers GitHub Copilot Workspace, Devin, Cursor Agent.

Syntax reference

Visual workflow / architecture:

Thought: I need the weather to answer.
Action: get_weather("NYC")
Observation: 22°C, sunny
Thought: I have the info.
Action: respond_to_user("It's 22°C and sunny in NYC.")

Real-world use

Every modern AI agent (Devin, OpenAI Operator, Claude Computer Use) uses an agentic prompt loop.

Best practices

• Cap max iterations to prevent runaway loops.
• Make tool names and descriptions clear in the prompt.
• Log every Thought/Action/Observation for debugging.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is agentic prompting?
• Q2. What is ReAct?
• Q3. How prevent infinite loops?
• Q4. How do you debug an agent that takes wrong actions?