Enterprise agent deployments nearly doubled in four months—from 7.2% in August 2025 to 13.2% by December. That acceleration exposes where production systems hit architectural limits.

The gap: 58% of organizations can monitor their AI agents, but only 37% can actually stop them when something goes wrong.

We see this threshold daily in web agent infrastructure. A pricing agent pulls data across hundreds of hotel sites simultaneously. Monitoring catches incorrect rates from a regional variant. Stopping it means coordinating revocation across different authentication implementations—OAuth here, session cookies there, proprietary tokens elsewhere. Each has different timeout behaviors. Without unified control planes, bad data flows into pricing systems before manual coordination completes.

What Breaks at Scale

Organizations deploying agents at scale hit governance limits that expose fundamental coordination challenges:

Governance Capability	Organizations Unable to Execute
Enforce purpose limitations on agents	63%
Quickly terminate misbehaving agents	60%
Stop agents when monitoring detects issues	63% (inverse of 37% who can)

When OpenAI's plugin ecosystem suffered a supply chain attack, compromised credentials accessed customer data across 47 enterprise deployments for six months. Six months. The exposure window reveals the coordination challenge: revoking access across plugin integration points requires coordinating across different authentication mechanisms and session management implementations. Distributed systems struggle to achieve this at the speed cascading access demands.

For web agents, the coordination challenge is architectural. An agent mid-transaction on a booking site maintains authentication state, cookies, and sessions that persist across multiple surfaces. Containment means tracking which sessions are active, which are mid-transaction, which data pipelines are consuming which outputs. You can't revoke access you're not tracking. You can't terminate sessions whose state you don't know.

Research shows a single compromised agent can poison 87% of downstream decision-making within four hours. Traditional incident response operates on longer time scales. Multi-agent systems propagate failures faster than manual coordination can contain them.

Why Audit Trails Predict Containment

The clearest predictor of containment capability: organizations without evidence-quality audit trails lag 20-32 percentage points behind on every governance metric.

The architecture explains this. Building audit trails across distributed web agents means tracking state as sessions move between sites. Which authentication tokens are active. Which sessions are mid-transaction. Which data pipelines are consuming which outputs. That same state-tracking infrastructure enables coordinated containment. Organizations without comprehensive audit trails lack the state visibility that containment requires.

Gartner predicts over 40% of agentic AI projects will fail by 2027. Legacy systems can't support coordination requirements at production scale.

Building for Coordination

The deployment acceleration shows organizations crossing an architectural threshold. Infrastructure built for monitoring can't handle the coordination requirements of production agent deployments.

For web agents, infrastructure decisions are crystallizing now. Control planes designed for coordination from the start. The ability to track authentication state across fragmented surfaces. The infrastructure to revoke access across hundreds of different site implementations simultaneously. The observability to know which sessions are mid-transaction and which can be safely terminated.

Organizations building this infrastructure now position themselves for a market where containment speed determines operational viability. Those treating coordination as an afterthought will discover the gap when production incidents expose it. By then, the architectural debt is expensive to unwind.

The 20-point gap closes through architectural decisions that treat coordination as fundamentally different from observation and build infrastructure accordingly.