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
Architecture
- 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:- Query memory from a vector database
- Call a tool or external API
- Interpret results with an LLM
- Return structured output to the user
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
| Layer | Technologies Used |
|---|---|
| Orchestration | Langchain, LlamaIndex |
| Prompt Engineering | Langsmith, Promptsmith |
| Frontend | Vercel AI, Stramship |
| Tools | VertexAI, Postman |
| Vector DBs | Pinecone, Deviate |
| LLMs | OpenAI, Snowflake |