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What is an Atlas AI agent?

Atlas AI agents use generative AI (GenAI) language models to solve specific industrial business problems, like providing insights into historical and planned maintenance and analysis of time series data for root cause analysis.

They include prompts with instructions, industry-relevant tools, and access to industrial data stored in the Cognite Data Fusion (CDF) knowledge graph.

Atlas AI agent

Agents interact with humans to meet predetermined objectives. Humans provide instructions, and the AI agent suggests the best actions to take to achieve those objectives.

Language models​

Language models are one of the main ingredients of industrial AI agents. They're probabilistic models designed to understand and generate human language. Large language models (LLMs) are trained on vast amounts of data, making them versatile and useful for general purposes.

The Language model library has detailed reference information about the available language models to help you choose.

Knowledge graphs​

Cognite Data Fusion (CDF) has data modeling features and out-of-the-box data models to get you started building a structured, flexible, and contextualized knowledge graph of your industrial data.

Leveraging the knowledge graph, AI agents have the context they need to deliver more deterministic and accurate insights and recommendations, improving their effectiveness in tasks such as data analysis and problem-solving. The decisions are traceable and transparent so that you can always see how the AI agent arrived at its answers.

Industrial data is not static, and the knowledge graph ensures that the language models always work with the most recent data. Access control is another advantage of using knowledge graphs, controlling what data is provided to the language model.

Language models and knowledge graphs complement each other, and the strengths of one technology compensate for the limitations of the other.

For guidance on making your data model agent-ready, see Optimizing data models for AI.

Prompts and prompt engineering​

Prompts—prompt engineering—is how you optimize the output from the agents by helping the language models understand what output you want them to produce. Instructions are crucial for effectively guiding AI models.

Instructions are the specific directives and guidelines provided to the agent that define both what you want to achieve and how to achieve it. They detail the desired outcomes, objectives, and the specific approach the language model should take. For instance, an instruction might specify that you want to generate a summary, define the format of that summary, and include the key points to cover.

Instructions should clearly outline both what you want to accomplish and how to get there.

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Tools​

Atlas AI agents can interact with tools and functions, allowing the agent to perform complex tasks or interact with your application. We provide built-in tools for Atlas AI agents, but you can also define your own tools to extend their capabilities.

Typically, tools fall into these broad categories:

  • Querying and exploring data from the industrial knowledge graph ensuring that the information is relevant and task-specific.

  • Running code and algorithms, or perform computations in real time based on input or operational requirements.

  • Integration with external systems via APIs to pull data, trigger workflows, or update records.

  • Integration with collaboration and communication tools, for example, Slack and Microsoft Teams, ensuring seamless human-machine interaction.

To view the currently available tools, see the Agent tools library.

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