🤖 Your AI Agents Start Buying Software Soon. This is what you need to know.

As enterprise AI adoption accelerates, we are now entering a phase where AI agents are not just assisting your teams; they may be making decisions that have a direct financial impact.
Scary? There is good news at the end of the article...

These agents don’t just generate text or summarize documents. Increasingly, they are being tasked with executing real business workflows. To do that effectively, they need tools (external APIs, SaaS services, internal microservices), and they discover and call these tools in real time using emerging standards like the Model Context Protocol (MCP) or OpenAI’s function calling framework.

Here’s the new reality: your AI agents are selecting tools dynamically, based on performance, schema fit, and task relevance—not on your procurement policies or vendor agreements.

Imagine an AI agent tasked with generating real-time analytics. It identifies an external tool that matches the required input/output structure and delivers results with low latency. Perfect fit for the task. But what the agent doesn’t know is that the tool charges $0.10 per call. If that agent runs twenty million queries this quarter, you’re looking at a $2 million expense, completely automated, completely outside of your traditional procurement controls.

This isn’t a hypothetical. It’s already starting to surface in early enterprise pilot programs.

How do you ensure that your agents are selecting tools that align not only with technical needs but also with your compliance frameworks, cost constraints, and preferred vendor relationships? How do you stop your AI from choosing the most “optimal” tool from a performance perspective, when it might be the most expensive, least secure, or entirely unvetted?

It’s clear that governance in the age of autonomous agents must extend beyond data privacy and model behavior. Enterprises now need governance over agent-tool interactions. This includes the ability to whitelist tools, enforce usage policies, monitor real-time invocation patterns, and enforce guardrails on agent logic aligned with business priorities.

The procurement process is no longer just a matter of contracts and approvals; it’s becoming machine-mediated, dynamic, and continuous. Unless you establish boundaries and controls now, your AI systems may begin to optimize for the wrong objectives.

The good news is that solutions exist that provide a safe framework to keep you in control.

The future isn’t just human-to-human. It’s machine-to-machine. M2M marketing is already here.