Agentic AI

What is Agentic AI?

Agentic AI refers to AI systems that can:

• Reason about problems and break them into steps
• Plan sequences of actions to achieve goals
• Use tools to interact with external systems
• Make decisions autonomously based on context
• Learn from feedback and adapt behavior

Core Capabilities

New Features Implemented

We've extended the basic agent with two powerful new features to make it more practical and production-ready:

Feature 1: Persistent Memory System

What it does:

• Automatic Task History - Every task and result is automatically saved
• History Folder - All memories persisted to disk as JSON files
• Auto-Load - Previous memories loaded when agent restarts
• Memory Recall - Query and search through past tasks
• Memory Statistics - Track total tasks and recent activity
• Memory Management - Clear history when needed

How it works:

Feature 2: GitHub Integration

You: Create a GitHub issue about adding unit tests
Agent: ✓ Created issue #42: Add unit tests for memory feature

You: Show me all open pull requests
Agent: Found 3 open pull requests:
      #41 - Add GitHub integration (waiting for review)
      #40 - Fix memory persistence bug (approved)
      #39 - Update documentation (2 comments)

You: Check the status of our GitHub Actions workflows
Agent: Latest workflow runs:
      ✓ CI Pipeline - Success (3 minutes ago)
      ✗ Deploy to Production - Failed (15 minutes ago)
      ✓ Run Tests - Success (1 hour ago)

Setup & Installation

Prerequisites

• Python 3.9 or higher
• OpenAI API key
• (Optional) GitHub Personal Access Token for GitHub features

Installation Steps

python -m venv venv
venv\Scripts\Activate.ps1  # Windows
source venv/bin/activate   # Linux/Mac

pip install -r requirements.txt

OPENAI_API_KEY=your_api_key_here

# Optional: For GitHub features
GITHUB_TOKEN=your_github_token_here
GITHUB_OWNER=your_username_or_org
GITHUB_REPO=your_repository_name

python agent.py

deactivate

ReAct Pattern Demonstration

This POC demonstrates the ReAct (Reasoning + Acting) pattern, where the agent:

• Reasons - Thinks through problems step-by-step
• Acts - Uses tools to accomplish tasks
• Plans - Breaks complex tasks into manageable steps
• Remembers - Maintains context across interactions

You: What's the weather in Seattle and save it to a file?
Agent: [Thinks] I need to get weather data first, then write to file
      [Acts] Calling get_weather("Seattle")...
      [Acts] Writing to weather.txt...
      Done! Weather saved to weather.txt

You: What did I just ask you to save?
Agent: [Uses memory] You asked me to get the weather for Seattle and save it

Project Structure

agent/
├── agent.py              # Main agent (ReAct pattern)
├── tools.py              # Core tools (weather, files, etc)
├── github_tools.py       # GitHub integration (NEW)
├── memory.py             # Persistent memory system (NEW)
├── requirements.txt      # Python dependencies
├── .env                  # API keys (create this)
├── README.md             # Documentation
├── GITHUB_INTEGRATION.md # GitHub setup guide
└── History/              # Memory storage (auto-created)
    ├── 20260123_143022_task1.json
    └── 20260123_151530_task2.json

Next Steps & Extensions

After understanding this POC, you can extend it with:

• More sophisticated planning algorithms (Tree of Thoughts, Chain of Thoughts)
• Vector database for semantic memory search (Pinecone, Weaviate)
• Multi-agent coordination and collaboration
• Error handling and retry logic with exponential backoff
• Streaming responses for better UX
• Additional integrations (Jira, Slack, email, databases)
• Web interface or REST API endpoint
• Authentication and multi-user support

Learning Resources

• LangChain Documentation
• ReAct Paper (Arxiv)
• OpenAI Function Calling
• GitHub REST API

Version History