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Configuring model and data sources

To ensure that your agent delivers accurate results, it is crucial to select the right model and connect relevant data sources. These data sources form the agent's knowledge base.

The platform lets you configure different models and data source types for each agent. You can upload files, connect to cloud storage, or link to website URLs that can be indexed. The agent uses this information to quickly find and return relevant answers to your queries. Your agent can scan through all this information in no time to find solutions to your queries.

Select a language model (LLM)​

When you create a new agent or edit an existing one, click Model to open the catalog. What you can choose depends on your organization’s plan, enabled providers, and region. Models differ in speed, cost, context size, and quality—use the labels in the product as the source of truth for what is available to you right now.

Azure OpenAI (enterprise GPT routes)​

Many GPT-family models are offered through Azure OpenAI for organizations that use that deployment. Those routes are the ones that carry enterprise-grade privacy commitments where your agreement with Microsoft applies. For models that expose reasoning controls, Devs.ai sends only combinations the Azure endpoint accepts, so chats are less likely to fail because of invalid reasoning settings.

Kimi (Moonshot)​

When Kimi 2.6 and related Kimi models appear for your organization, the catalog lists a 256K context window so expectations line up with provider limits. If web search is turned on for your environment and the model supports it, the agent can answer using web search in the chat alongside its other tools—the same org and agent settings that govern search for other models still apply.

Other providers​

Depending on what your administrators enable, the picker may include models from OpenAI (direct), Anthropic, Google, Perplexity, xAI, Cohere, Meta, Mistral, and others. Each card in the catalog summarizes capabilities such as vision, tools, reasoning, and approximate context—use those details when comparing options.

After you select a model, you can adjust parameters such as temperature and token limits where the model supports them.

Keep in mind that LLMs still struggle with precise math unless you give them structured tools or verification steps. If an agent fails or times out repeatedly, try a different model or simplify the task; for Azure-hosted GPT models, rare failures tied to reasoning configuration should be less common than in earlier releases.

Data sources​

After selecting a model, connect different data sources to build a knowledge base for your agent to refer to.

  • Previous Data - reuse data sources added for other agents.
  • Upload Files - upload files directly from your computer, such as documents, spreadsheets, or PDFs. These files are indexed and used by the agent when responding to queries.
  • Website URLs - enter URLs of websites or domains you want to index. The agent can scan and reference content from these pages.
  • Google Drive - connect your Google account to import data from your Google Drive .
  • Microsoft OneDrive - connect your Microsoft account to import data from your Microsoft OneDrive.
  • Confluence and JIRA - connect your Atlassian account to configure Confluence and JIRA to allow the agent to use issues, tickets, and documentation.
  • Github - connect GitHub repositories so the agent can reference code, issues, and related documentation.

When using cloud storage or an online data source, you can set a frequency for data refresh. This controls how often data is indexed or pages are refreshed.

Large or complex data sources may affect response time. It is important to test your agent after making changes. Clear and focused data sources produce better results than large, unstructured collections.

After configuring datasources, you can set up additional actions and link tools to your agent.

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