Quick Answer
AI agents use tools to take actions and data sources to access information. When you build an agent, you configure which tools it can use (like web search, CRM access, or analytics connections) and which knowledge bases it can reference (like brand guidelines or product documentation). During execution, the agent draws on those configured resources to complete the task. It might search the web for current competitor information, pull from your positioning documents for context, then format findings based on its instructions. This is what separates agents from basic prompting: they don't just generate text from training data, they gather real information and take real actions using the resources you've enabled.
The power of an AI agent comes from its ability to reach beyond the conversation. A standalone language model can only work with what's in its training data or what you paste into the prompt. An agent can access external tools and data sources, which means it operates on current, specific, and proprietary information rather than general knowledge.
Tools are capabilities that let the agent take actions. When you configure an agent, you enable specific tools based on what it needs to do. A competitive research agent might have web search enabled to find current information. A sales enablement agent might connect to your CRM to pull account details. A content QA agent might access your analytics platform to check how similar content performed. Tools can also write data, not just read it: updating a CRM record, adding a row to a spreadsheet, or saving a file to SharePoint. The agent decides when to use each tool based on the task and its instructions.
Data sources provide reference material the agent can draw from. Knowledge bases are the primary example: you upload documents like brand guidelines, product specs, or past research, and the agent retrieves relevant content when it needs context. Unlike tools, which are about taking actions, data sources are about grounding the agent in your specific information. An agent answering questions about your product line pulls from your documentation rather than guessing based on general training data.
In practice, tools and data sources work together. An agent preparing a competitive brief uses web search (a tool) to gather current competitor information. It references your positioning documents (a data source) to compare against your strategy. It pulls win/loss data from your CRM (another tool) for deal context. The output is grounded in real, current, proprietary data rather than a generic summary.
Configuration is straightforward. When building an agent, you select which tools it can access and which knowledge bases it should reference. The agent's instructions guide when and how it uses them. The result is an agent that doesn't just think, but knows and acts.
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