Agent Framework

Welcome to the IO Intelligence Agent Framework — our foundational architecture for building modular, production-grade AI agents. This framework guides how we design, orchestrate, and scale intelligent systems across various use cases.

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This framework is currently used internally and shared here to help developers understand our AI agent design philosophy.

Overview

This framework brings together the key layers of the modern AI stack and provides a structured approach to building agents that can:

• Retrieve relevant context and knowledge
• Reason and make decisions
• Use external tools and APIs
• Interact with users through intuitive UIs

It is designed for real-world use cases and is actively used in production.

Architecture

At the core of the framework is an orchestration layer, powered by Langchain and LlamaIndex. It acts as the central hub, coordinating data flow and interactions between the following components:

• Prompt Engineering
  Tools like Langsmith and Promptsmith help design structured prompts and prompt chains that drive agent behavior.
• Frontend / UI
  Built with platforms like Vercel AI and Stramship, allowing users to interact with agents seamlessly.
• AI Tools / Agents
  We integrate with services such as VertexAI and Postman to enable action-taking, API execution, and task automation.
• Vector Databases
  Pinecone and Deviate provide long-term, searchable memory via retrieval-augmented generation (RAG).
• LLMs
  Models from OpenAI and Snowflake power the core reasoning and natural language understanding behind the agents.

Why Orchestration Matters

AI agents often need to perform multiple tasks in a coordinated flow:

1. Query memory from a vector database
2. Call a tool or external API
3. Interpret results with an LLM
4. Return structured output to the user

The orchestration layer ensures all these components work together smoothly and reliably.

Use Cases

The IO Intelligence Agent Framework powers a wide variety of real-world applications, including:

• Autonomous customer support and helpdesk agents
• Internal copilots for devops, analytics, and operations
• Document Q&A and knowledge assistants
• Task agents that combine memory, reasoning, and API execution

Built With

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Get Involved

This framework is open source and available on GitHub repository. If you’re building agent-based systems or are exploring LLM orchestration, we invite you to explore, fork, and build with it.