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Title

Autonomous Task Planner – Agentic AI (Module 3)

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Overview

This project implements an autonomous task planning system based on a
Plan → Execute → Evaluate → Retry/Replan loop.

The system accepts a high-level user goal, decomposes it into tasks, executes them step by step, evaluates the results, retries or replans when necessary, and terminates safely using predefined guardrails.

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Problem Statement

Traditional scripts execute predefined steps and fail silently when conditions change.
There is a need for systems that can reason about progress, evaluate outcomes, and adapt autonomously instead of following a static flow.

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Solution

The Autonomous Task Planner addresses this by implementing:

• A Planner Agent to decompose goals into tasks
• An Executor Agent to execute tasks sequentially
• An Evaluator Agent to decide whether to accept, retry, or replan
• A shared state manager to track execution progress
• Safety limits to prevent infinite loops and uncontrolled execution

The system continues autonomously until the goal is completed or a safe termination condition is reached.

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Key Agentic AI Concepts Demonstrated

• Autonomous decision-making
• Plan–Execute–Evaluate control loop
• Retry vs Replan logic
• State-driven execution
• Modular agent architecture
• Guardrails and termination conditions

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Execution Example

Goal:
Prepare a 4-week Python interview preparation plan

Result:

• Tasks were planned and executed autonomously
• Evaluator validated the output
• System terminated successfully without manual intervention

Repository

GitHub:
https://github.com/prithvi-18/Agentic-AI-Developer-Certification-Program

📁 Module Folder: Autonomous_Task_Planner/

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