How one big city is letting AI agents in

Boston’s pioneering experiment with MCP is laying the groundwork for an ‘agentic middleware’ for local government, expanding access to public services.

[Images: Adobe Stock]

BY Alberto Rodriguez Alvarez

The next wave of AI will be defined by agentic systems that can take actions: query databases, navigate portals, retrieve records, and increasingly interact with public digital infrastructure at scale.

That shift is already showing up as traffic hitting government sites and services is becoming machine traffic. Some of it is benign (search and discovery). Some of it is ambiguous (scraping and automated browsing). And some of it could become actively harmful if agents can reserve scarce services, submit fraudulent requests, or generate volume that overwhelms public systems. 

The problem is that the government’s current interfaces were not designed for agent-to-government interactions, and the default state of the world has become improvisation: agents “figure it out” by scraping pages and guessing based on previous learning..

This is where Boston’s work becomes instructive. Rather than treating agents as something to block wholesale, or something to embrace without guardrails, Boston is experimenting with a middle path: build a governed, secure, and reliable layer that mediates how AI agent systems interact with government resources. 

In a recent interview, Boston’s CIO, Santi Garces, described why the city is investing in the Model Context Protocol (MCP) as that layer; why they’re starting with open data as a low-risk proving ground; how they’re improving reliability by pushing computation into the data portal itself; and what it would take for MCP-like infrastructure to become replicable digital public infrastructure that other cities can deploy.

Can you explain MCP, and why city governments should care?

MCP stands for Model Context Protocol, and it’s relatively recent. Anthropic, the company behind Claude, launched MCP servers about a year ago. Why it matters is that it provides a way for large language models to interface with the kinds of resources we have in government. Concretely, it’s a way to connect LLMs to APIs and other programmatic systems, for example, allowing an AI assistant to retrieve transit updates or submit a service request through official city systems. We think it will be a new layer that serves as an intermediary between the government’s digital infrastructure and these models.

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