AI Agents Get Hands With Tool Calling

The era of passive AI chatbots is rapidly giving way to an age of active AI agents. The key to this transformation? Tool calling. This capability allows artificial intelligence models to go beyond generating text and actually interact with the outside world. Think of it as giving AI hands, enabling it to fetch live data, execute code, or trigger actions in external systems. As detailed in a recent Databricks blog post, tool calling is the mechanism that bridges the gap between generative AI, which produces content, and agentic AI, which performs tasks.

This shift is not just theoretical. Gartner predicts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025. This explosive growth hinges on reliable tool calling. Without it, an AI agent might understand a request but be unable to fulfill it, limiting its value in production environments. Instead of building brittle, rule-based integrations for every possible workflow, organizations can deploy AI agents that dynamically select and use the right tools as needed. This flexibility reduces engineering overhead and makes systems far more adaptable.

How AI Agents Use Tools

The process of tool calling follows a structured loop. First, the AI model must recognize when a request cannot be answered from its internal knowledge alone. For instance, asking about the weather requires external data, while asking for the capital of France does not. This recognition is trained into the model.

Once a need for an external tool is identified, the AI selects the most appropriate one from a list of available tools. Each tool is described to the model with its function and required parameters. The model then generates a structured request, typically in JSON format, specifying the tool and its arguments. Crucially, the AI model itself does not execute the tool; an application layer handles this. The result from the tool is then fed back to the model, which synthesizes it into a natural language response or confirms an action taken.

This process isn't always a single step. Complex tasks, like summarizing quarterly performance and emailing it to executives, might involve multiple tool calls in sequence: querying a database, generating a chart via code execution, and finally sending an email through an API. This iterative capability is what defines an agent.

Types of Tools Agents Can Call

The utility of tool calling spans several categories. Information retrieval is perhaps the most common, allowing agents to query databases, search documents, or fetch real-time data like stock prices. Retrieval-Augmented Generation (RAG) is a specific application where models use this to ground responses in verified data.

Code execution tools empower agents to perform calculations, data transformations, or generate visualizations. This is invaluable for data analysis tasks. For example, an analyst could ask an AI agent to compare regional revenue, and the agent could query a lakehouse, calculate growth rates, and generate a chart, all within a single conversational exchange.

Process automation tools allow agents to interact with business systems directly. This includes sending emails, updating CRM records, or initiating workflows. These tools transform AI from passive observers into active participants in business processes.

Finally, in operational contexts, tool calling can connect AI to smart devices and IoT systems, enabling monitoring and control of physical equipment.

Tool Calling vs. Function Calling

While often used interchangeably, a distinction exists. Function calling was an earlier concept, focused on generating arguments for predefined functions. Tool calling is a broader term, encompassing APIs, databases, code execution, and any callable external resource. It represents the evolution of the concept as AI agents became more sophisticated and capable of orchestrating multi-step workflows across diverse systems. Databricks supports both terms, reflecting the industry's convergence on 'tool calling' for agentic AI.

Enterprises Embrace Agentic AI

The business imperative for tool-calling AI agents is clear. Databricks, a major player in the enterprise data and AI space with StartupHub.ai score 82/100 and verified financials showing $7 billion raised at a $134 billion valuation, is actively developing this capability. Their Agent Bricks platform provides a governed environment for building these agents, integrating native support for protocols like MCP and ensuring governance through Unity Catalog. This focus on governance is critical for enterprises adopting AI agents, as it ensures auditability and security.

Competitors like Palantir Technologies (StartupHub.ai score 85/100) and Alphabet (NASDAQ:GOOGL) (StartupHub.ai score 80/100) are also heavily invested in agentic AI. Palantir's Artificial Intelligence Platform (AIP), for example, focuses on enabling agents to interact with enterprise data securely. Alphabet, through its vast cloud infrastructure and AI research, is also a key player in this space, offering tools and services that facilitate agent development.

Real-world examples abound. Customer support agents use tool calling to check order statuses or update records. IT operations teams deploy agents to monitor infrastructure, diagnose issues, and trigger remediation actions. Enterprise search tools can now pull information from disparate systems, providing employees with synthesized answers rather than just links.

The ability for AI to reliably and securely interact with external systems is no longer a futuristic concept. It is a present-day necessity for enterprises looking to automate complex workflows, improve operational efficiency, and unlock new forms of value from their data. As AI agents become more capable, tool calling will be the foundational capability that allows them to move from simply understanding the world to actively shaping it.