Introduction:

AI is no longer limited to answering questions or providing suggestions — it’s evolving into systems that take real action.

Action Agents: intelligent, agentic AI systems that don’t just assist, but execute. These agents interpret user intent, seek clarifications when needed, and complete real-world tasks using APIs and tools — functioning more like digital teammates than passive chatbots.

In this blog, we will walk through how to build a GitHub Action Agent using Crew AI, an open-source framework for orchestrating teams of AI agents.

What Are AI Agents?

AI agents are autonomous systems that pursue defined goals by processing input, reasoning, and executing actions — often with the help of APIs, scripts, or other tools. They integrate the power of language models with decision-making capabilities, memory, and environmental interaction.

Key Components of an AI Agent:

• A defined goal: The task it aims to accomplish.
• Tool or environment access: APIs, file systems, scripts, etc.
• Reasoning capability: Makes informed decisions on next steps,
• Optional human interaction: Requests clarification when needed.

Traditional LLMs vs AI Agents

Traditional LLMs: Language models like ChatGPT are excellent at understanding and generating text. However, they’re inherently passive — they tell you what to do, but don’t do it for you.

Example:

You: “How do I create a private GitHub repository?”
LLM: “Go to GitHub, click on ‘New’, enter a repo name, and select ‘Private’.”

AI Agents: AI agents go further. They understand your goal and take action.

• Ask for missing information (e.g., repo name)
• Call the GitHub API to create or delete the repository
• Report back with the outcome

Simplifying AI Agent Workflows with Crew AI

Crew AI is a lightweight, open-source agentic framework for building collaborative AI agents. It enables developers to design modular, role-specific agents that work together to complete complex tasks. Whether you’re building a research assistant or a software automation bot, Crew AI provides the infrastructure to build agent-driven workflows.

Use Case: GitHub Repository Manager Agent

We have built a GitHub Action Agent to automate common tasks like creating and deleting repositories.

Capabilities:

• Ask the user what GitHub task to perform
• Clarify missing details (e.g., repo name or visibility.
• Use the GitHub API to perform the action
• Return a success or error message

Step-by-Step Implementation

1. Tools Setup

We define tools that the agent can use to interact with the user and GitHub:

python code:

2. Agent Definition

Configure the agent’s role, goal, tools, and behavior.

python code:

3. Task Configuration and Crew Initialization

Define what the agent needs to do and what output is expected.

python code:

Output Screenshots:

Conclusion:

With frameworks like Crew AI, we are entering an era where AI systems don’t just think — they act. The GitHub Action Agent is a small but powerful example of what’s possible:

• Goal-driven automation
• Real-time decision-making
• Tool-enabled execution
• Human-AI collaboration

Sanjay N