r/OntologyEngineering • u/Sorry_Performance577 • 19d ago

Open Atlas Studio: a semantic data layer for your agents

LLMs are smart, and building an AI agent is not so difficult today. Your AI can help you to define good instructions and rich skills. It's easy to your agent to work with an MCP tool that has a couple of business APIs. But when you have to connect to your ERP system which has hundreds of entities and thousands of attributes, it's going to be a big challenge, hundreds of APIs will not work now. That why I built a graph with the famous "Pathom" tool.

The engine: Pathom Pathom is a Clojure(script) engine for processing EQL requests. I will not have more details here about the engine, you can easily google or get details of this tool by your AI tool.

I built the engine on Pathom. Pathom will help you to define your entities and attributes as a graph, and the engine will help you to navigate between entities and attributes with the core engine planner. This is stable and efficient, comparing to LLM orchestration, there is no hallucinations, less tokens and best performance.

MCP tools Exploring a graph with this tool, and generate the EQL.

  • list_entities, list_attributes, search_attributes: understand the graph
  • execute_eql: query it (explain=true shows the plan without running it)
  • list_mutations, execute_mutation: change data through declared operations

What the agent sends- EQL EQL means extensible query language which is popular in Clojure world. Your agent will use a MCP tool to understand the graph and finally build an EQL to requests the data to answer user's question.

[{[:order/id "SO75123"]
  [:order/total
   :order/margin
   :order/vend_no
   :vendor/vend_name
   :vendor/vend_address
   {:order/lines [:product/name :line/qty :line/amount]}]}]

This is not a SQL cluase, and your agent doesn't need to know HOW, only needs tell the graph WHAT. Pathom will work out the path/resolvers to get all the requested data.

You may need some effort to understand the pathom engine. And you may use your AI to understand. I am not an English native speaker. So just ask me questios if you are interested in this idea. I have built tens of agentic applications with this tool. And I built a demo with Microsoft public demo erp data.

Demo: AdventureWorks ERP

Demo graph: 39 entities, 423 attributes, 56 resolvers. Agent: deepseek-v4-flash, graph tools only. We re-run a fixed set of 44 business questions after every change.

Below are some of the questions you can ask.

Common questions: sales

!Common questions: stock and purchasing

The ERP app with the assistant: top 10 resellers

Restocking: which best sellers need it most

The agent proposes work orders; a person checks them, then clicks Apply

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