You walk into a favorite restaurant. The host checks your reservation, shows you to a table, and presents the menus. Someone comes to take your order, and a server brings out the dishes after the kitchen prepares them. Following a delicious meal, you pay the bill and leave quite pleased.
Now, imagine you’re going to an eatery where AI agents do all the work. Let’s call it The Bot Bistro. You arrive at the appointed time after making the reservation with an online agent. Another agent directs you to a table. You place your order with a waitstaff agent, and a team of cooking agents prepares the meals in the kitchen. The server agent delivers them. Later, a bill processing agent takes payment. Oh, and maybe another agent will automatically open the door for you on the way out.
Agents can’t do all of those tasks – yet. However, this example illustrates how agents can and will operate within our businesses, performing individual tasks and collaborating on more complex workflows. Like human restaurant workers, these agents require something to do their jobs: Tools.
Agents can’t do anything without tools, and how they use them highlights the growing importance of application programming interfaces (APIs). All those tasks we described agents doing at The Bot Bistro couldn’t happen without APIs providing access to the needed tools.
The Role of APIs
What makes agents so revolutionary is that they can “do stuff” independently. Digital tools enable them to accomplish their work.
Tools include things like web browsers (databases, file systems, knowledge bases), business applications (your CRM systems, ERPs, supply chain platforms), communication interfaces (web browsers, APIs), and processing capabilities (search functions, calculators, data transformation services). Tools transform agents from conversation partners into context-aware and actionable business solutions by allowing them to retrieve data, manipulate it, make sense of it, and interact with the world beyond their internal programming to create desired outcomes.
We consider critical business operations, such as customer relationship management (CRM) and enterprise resource planning (ERP) systems, as well as financial ledgers, to be the “what” of our business. But agents view them differently. From an agent’s perspective, these systems are tools of executable capabilities. They are the “how” for agents — agents accomplish assigned tasks by accessing and acting on the information these systems contain.
How do agents use tools? Hello APIs!
APIs are the unsung connectors of modern digital ecosystems. They standardize protocols that enable different software programs to communicate and exchange data snippets. When an agent needs information from a business system, it makes what’s known as an API call – a structured request for specific data. The API retrieves the information and returns it to the agent. Think of APIs as the communication channels that give agents the ability to act on your behalf.
Helping Agents Work Their Magic
Let’s return to The Bot Bistro restaurant example and everything needed for that delightful meal. AI agents perform their work using APIs as the conduit to access various information systems, such as a reservation service, a menu database, numerous applications in the kitchen for meal preparation, and a point-of-sale application to process the payment. (Agents could very well be doing some of that real-world work right now as assistants to humans, such as handling reservations and calibrating cooking temperatures.)
The agent taking your order might not know how to cook your ribeye medium-rare. However, using an API, it communicates the order to the “chef” agent, which then prepares it, and informs the “waiter” agent of the preparation time via the API. That chef agent then might communicate with other cooking agents to prepare salads, appetizers, main courses, and desserts. All of these digital interactions are near-instantaneous. The agents are the task-doers, each with a distinct set of responsibilities.
But good agents can’t do their work without great APIs. That’s because APIs enable these coordinated workflows by exchanging information and instructions and providing the necessary tools – in this case, to prepare a meal.
This highlights a crucial point. APIs are more than just connectors in your business.
Since APIs interact with the most critical systems – customer data, financial records, operational controls – API management has become essential in the age of agentic AI.
APIs require scrupulous monitoring, transparency, explainability, and thorough governance of compliance and security. Without that, an agent designed to help with expense reports might inadvertently access payroll systems it shouldn’t or make unauthorized changes to production systems if permissions aren’t set correctly. AI agents need guardrails to prevent them from taking improper actions with their API-supplied access to tools.
Also, APIs are not without flaws. They can be brittle – meaning they’re fragile and prone to breaking when systems change. This creates maintenance overhead and integration complexity. It’s why there’s the growing trend toward something you’ve probably heard of, but might not yet understand: Model Context Protocol (MCP). While still a young technology (even by AI standards), MCP is gaining rapid adoption across major AI platforms. It has emerged as an industry standard interface for agents to find and access tools.
MCP is sometimes compared to a USB-C universal connector. Imagine if your thousands of business systems each required a different, proprietary plug. It would be a nightmare. MCP provides a single, standardized port for an agent’s use.
Instead of agents needing to understand the unique specifications of each system it accesses, MCP creates a consistent way to discover available tools, requested data, and perform actions. It simplifies the underlying API calls by abstracting away the backend technical complexities, making it easy for agents to interact with countless different tools in a single environment. MCP unifies how these diverse APIs are accessed, allowing agents to quickly obtain information, respond to prompts, and perform complex actions.
All of that is good. But it also presents its own problems. From a security standpoint, providing easy access to an agent creates inherent security risks. Additional management layers need to be placed around authentication and authorization to ensure that agents do not gain access to data files they shouldn’t. (For instance, you probably wouldn’t want an AI agent to have access to sensitive private data for everyone in the organization or all of your customers.)
Then there’s the “paradox of choice.” Giving AI agents access to all tools when they only need some causes bloat. This can lead to confusion and inefficiency due to an overload of options. Agents may not know what to do and consequently behave incorrectly, respond with inaccurate information, or perhaps not work at all.
The Secret to Agent Trust
Everything related to AI, from the agents themselves to the supporting technology, needs to be properly monitored and secured. This involves implementing access controls so agents can only use relevant tools, maintaining audit logs of agent actions, setting rate limits to prevent system overload, and regularly reviewing what they can access.
Without that essential element of governance, there will be no trust in agents.
Or, back at The Bot Bistro, agents might try cooking the meal in a refrigerator instead of on a stove or processing the credit card with knives.
Learn more about Model Context Protocol and how Boomi makes MCP enterprise-ready.