Multi-Agent AI Operations Assistant
Multi-Agent AI Operations Assistant
Developed a multi-agent AI system that plans, executes, and verifies complex user requests using a structured agent pipeline powered by an LLM. The system follows a Planner–Executor–Verifier architecture, where the planner decomposes user queries into structured steps, the executor interacts with external tools to perform tasks, and the verifier validates outputs to ensure they satisfy the original request. The assistant integrates external services including the OpenWeather API for real-time weather data and the GitHub REST API for repository discovery and metadata retrieval. Internal communication between agents is handled through structured JSON outputs, enabling clear coordination and traceability across the task pipeline. The project also implements secure API key management using environment variables to protect sensitive credentials. Built with Python and the OpenAI API, the system demonstrates how LLM-driven agents can orchestrate tool usage and automate multi-step operations through a modular and extensible architecture. Tech Stack: Python, OpenAI API, REST APIs, OpenWeather API, GitHub API, JSON, Environment Variables
Project Overview
Key Features
1. Multi-Agent Task Processing
The system is built using a multi-agent architecture where different agents collaborate to complete complex tasks. Instead of relying on a single AI component, the system distributes responsibilities among specialized agents, improving clarity, modularity, and maintainability.
2. Planner Agent
The Planner Agent analyzes the user’s natural language request and breaks it down into structured steps. It generates a JSON-based execution plan that specifies the sequence of actions required to complete the task and identifies which tools or APIs should be used.
3. Executor Agent
The Executor Agent takes the plan created by the Planner and executes each step sequentially. It interacts with external tools and APIs, gathers the required data, and compiles the results for further validation.
4. Verifier Agent
The Verifier Agent evaluates the outputs generated during execution. It ensures that the results match the original user request and synthesizes a clear, human-readable response for the user.
5. Tool Integration
The system integrates with external services to retrieve real-world data. Current integrations include: OpenWeather API for real-time weather information. GitHub REST API for searching repositories and retrieving metadata.
6. Structured JSON Communication
Agents communicate internally using structured JSON objects. This structured communication ensures that each agent clearly understands the task plan, execution results, and validation outputs, making the workflow transparent and reliable.
7. Dynamic API Key Management
The project supports secure API key handling using environment variables stored in a .env file. This prevents sensitive credentials from being exposed in the codebase and allows flexible configuration across different environments.
8. Modular Architecture
The project follows a modular design, separating agents, tools, and configuration components. This structure allows developers to easily add new agents, integrate additional APIs, or modify the workflow without affecting the entire system.
9. Command-Line Interaction
The assistant can be executed directly from the command line, allowing users to quickly test queries and observe how the system processes requests through the agent pipeline.
10. Multi-Step Query Handling
The system can handle compound queries that require multiple steps and tool interactions, such as retrieving weather data and then performing repository searches based on related keywords.
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