An autonomous AI agent built from scratch that can reason through tasks, decide when to use external tools, execute actions, and generate responses based on real-time observations.
This project implements an AI agent architecture capable of:
- Understanding user queries
- Planning actions using a reasoning loop
- Calling external tools when required
- Processing tool outputs
- Maintaining conversational context
- Generating final responses
The agent follows a Thought → Action → Observation workflow, allowing it to solve tasks that require external information rather than relying solely on model knowledge.
- Multi-step reasoning
- Dynamic tool calling
- Context-aware conversations
- Real-time data retrieval
- Extensible tool architecture
- Error handling and recovery
- Modular agent design
User Input ↓ Agent Planner ↓ Reasoning Engine ↓ Tool Selection ↓ Tool Execution ↓ Observation Processing ↓ Final Response
- Node.js
- Express.js
- Gemini API
- Function Calling
- Prompt Engineering
- User submits a query.
- The agent analyzes the request.
- If external information is needed, the agent selects an appropriate tool.
- The tool is executed.
- Results are returned to the agent.
- The agent incorporates observations into its reasoning.
- A final response is generated.
User: "What's the weather in Bangalore and should I carry an umbrella?"
Agent:
- Determines weather information is required
- Calls weather tool
- Receives weather data
- Analyzes conditions
- Generates recommendation
git clone <repository-url>
cd AI_Agent
npm installAPI_KEY=your_api_key
PORT=3000- Long-term memory
- Multi-agent collaboration
- RAG integration
- Voice interaction
- Agent evaluation framework
- Autonomous task execution
- Agent architecture design
- Function calling workflows
- Tool orchestration
- Prompt engineering
- API integration
- Context management
Mohammed Abrar Software Engineering Intern | Full Stack Developer