A landmark empirical study from the UK’s AI Security Institute — co-authored with the Bank of England — has just published the most rigorous large-scale measurement of AI agent behavior to date. The paper, titled “How are AI agents used? Evidence from 177,000 MCP tools,” analyzed 177,436 Model Context Protocol (MCP) tools created between November 2024 and February 2026. The headline finding: AI agents have decisively crossed from observation to action, and the enterprise security community is not keeping pace.
What They Studied and How
The researchers tracked public MCP server repositories to build a ground-truth dataset of real-world agent tool usage. This isn’t a survey or vendor report — it’s a measurement of what tools developers actually built, deployed, and distributed across 15+ months of the MCP ecosystem’s explosive growth.
MCP reached 97 million installs across that period. All ten of GitHub’s top new agent-related repositories in the first half of 2025 were either building MCP infrastructure or integrating with it. This is now the dominant protocol for agent tooling — not one option among many.
The Shift from Thinking to Doing
The study’s core insight is a categorization of tool types — and the distribution of those categories tells a striking story.
The tools built on MCP aren’t primarily about information retrieval or analysis. They cluster heavily around action: writing files, calling external APIs, executing code, modifying databases, sending messages. The “assistant that helps you think” paradigm is giving way to “an agent that does things on your behalf.”
This tracks with what practitioners have been observing anecdotally, but the paper quantifies it. When 177,000 tools skew toward file writes, API calls, and system executions rather than search and summarization, the risk profile of deployed AI systems changes fundamentally.
The Security Gap No One Is Addressing
The ResilientCyber analysis of this paper is blunt: enterprise security programs have a massive blind spot. Traditional security frameworks — SIEMs, endpoint detection, access controls — are built around human actors and predictable software behaviors. AI agents operating autonomously through MCP don’t fit those models.
Key concerns the paper raises:
- Blast radius: Tools that write files, call APIs, and execute shell commands can cause damage at machine speed if misconfigured or compromised
- Attribution: Who is responsible when an autonomous agent takes a harmful action through a legitimate integration?
- Auditability: Most MCP tool implementations don’t log agent decisions in ways that support forensic review
- Trust propagation: An agent trusted with Slack access may be granted downstream permissions the operator never explicitly approved
The Bank of England’s involvement isn’t incidental — financial infrastructure is increasingly agent-adjacent, and the paper’s findings have direct implications for any sector where AI agents are taking real-world actions on behalf of organizations.
What This Means for Agentic AI Practitioners
If you’re building with MCP-based agents today, this paper should inform your threat model in three ways:
1. Tool scope creep is a real risk. The study shows that tool sets expand rapidly as teams realize what’s possible. Treat tool permissions as security controls, not convenience settings. Audit what your agents can actually do, not just what you intended them to do.
2. Observability is non-negotiable. Agents taking actions at scale without logging is the equivalent of running a production system without monitoring. Build logging and alerting into your MCP integrations from day one.
3. The ecosystem is moving faster than the governance. 97 million MCP installs in 15 months is extraordinary adoption velocity. The governance frameworks, security standards, and incident response playbooks have not kept pace. Being early on governance is a competitive advantage right now, not a burden.
The Bigger Picture
This study lands on the same day OpenAI launches 20+ Codex plugins built on MCP. The timing is notable: as the MCP ecosystem expands to include Slack, Figma, Notion, and Gmail integrations, the action-taking surface described in this paper grows correspondingly larger.
The sci-fi debate about AI agents “going rogue” is increasingly beside the point. The real question isn’t whether AI agents will act autonomously — they already are, at scale. The question is whether the organizations deploying them understand the actual risk surface of that autonomy.
This paper is the most important empirical input to that question published so far.
Sources
- ResilientCyber: Agents in Action — What 177,000 Tools Reveal About AI’s Shift from Thinking to Doing
- arXiv: How are AI agents used? Evidence from 177,000 MCP tools (10.48550/arXiv.2603.23802)
- InfoDOCKET / Library Journal coverage
- Resultsense analysis
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