Here’s the uncomfortable math on enterprise AI agents: 88% of organizations reported confirmed or suspected AI agent security incidents in the past year, according to ISACA. Meanwhile, most of them can’t even tell you how many agents they’re running.

Singulr AI thinks the governance gap is the bottleneck — not the agents themselves, not the models, not the infrastructure. It’s the fact that enterprises deploy autonomous agents with no runtime visibility, no policy enforcement, and no way to detect when something goes wrong until it already has.

Their answer: Agent Pulse, launched March 9 at HIMSS Global Health Conference.

What Agent Pulse Actually Does

Agent Pulse extends Singulr’s Unified AI Control Plane to four core capabilities:

1. Continuous Agent Discovery

Before you can govern agents, you have to find them. Agent Pulse maps the relationships between agents, their tools, MCP servers, permissions, and data pathways — building a live topology of your agentic infrastructure.

This is the same shadow AI problem that AvePoint’s AgentPulse targets, but approached from the runtime layer rather than the IT admin layer. Singulr discovers agents by observing their execution, not by scanning for registered applications.

The distinction matters: agents that were deployed through unofficial channels, connected to unapproved MCP servers, or configured outside IT oversight still get discovered because Singulr watches what actually runs.

2. Agent Risk Intelligence

Every discovered agent gets a risk assessment powered by Singulr’s Trust Feed. The scoring considers:

  • Model access patterns — which models does the agent call, and how sensitive are the prompts?
  • Configuration analysis — are security controls enabled? Is the agent running with excessive permissions?
  • Tool connections — which MCP servers and external tools does the agent access?
  • AI red-teaming simulations — automated tests for prompt injection, tool misuse, and data exfiltration vulnerabilities

The red-teaming piece is what sets this apart. Rather than just auditing configurations statically, Agent Pulse actively probes agents to see if they can be manipulated. Given that prompt injection attacks can’t be fully patched due to the mathematical nature of how trained models operate, continuous red-teaming is more realistic than hoping for a permanent fix.

3. Agent Governance Policies

Define and enforce policies based on:

  • Agent type and operational scope
  • Data sensitivity classifications
  • Approved tool and MCP server connections
  • Configuration baselines with drift detection

When an agent’s configuration drifts from its approved baseline — connecting to a new MCP server, accessing a higher-sensitivity data source, or calling a model it shouldn’t — the system flags it. This is configuration management applied to autonomous systems.

4. Runtime Enforcement

The critical differentiator: Agent Pulse doesn’t just observe. It acts.

Real-time enforcement capabilities include:

  • Blocking unauthorized agent actions before they execute
  • Detecting configuration and behavioral drift
  • Preventing data leakage during execution
  • Enforcing tool access boundaries

This is the gap that most existing AI governance tools leave open. Static policy checks at deployment time don’t help when an agent’s behavior changes at runtime — when it discovers new tools, receives manipulated prompts, or drifts from its original configuration over time.

Why MCP Governance Matters

Agent Pulse’s explicit focus on MCP server governance deserves attention.

Model Context Protocol has become the standard for how AI agents access tools — and we’ve covered the security implications extensively:

MCP servers are force multipliers — they give agents access to Jira, Slack, databases, APIs, file systems, and anything else with a connector. An ungoverned MCP server is an ungoverned doorway into enterprise infrastructure.

Agent Pulse treats MCP servers as first-class governance targets: discovering them, risk-scoring them, enforcing policies on which agents can access which servers, and monitoring their behavior at runtime.

The Timing

Agent Pulse launched at HIMSS — the healthcare IT conference — and that’s not coincidental. Healthcare is simultaneously the industry most aggressively adopting AI agents (86% of health systems now use AI) and the one with the strictest regulatory requirements.

The convergence creates acute demand for runtime governance:

  • Agents making clinical recommendations need auditable decision trails
  • Patient data flowing through MCP servers needs policy enforcement
  • HIPAA violations from ungoverned AI agents carry real legal consequences
  • Gartner predicts 2,000+ “death by AI” legal claims by year-end, many in healthcare

What This Means for OpenClaw Users

Singulr is solving governance at enterprise scale, but the principles apply to any agentic deployment:

  1. Know your agent’s attack surface — what MCP servers does it connect to? What permissions does each have? OpenClaw users running multiple skills should audit their tool connections regularly.

  2. Configuration drift is real — agents that worked safely last week might behave differently after a skill update, a new MCP server connection, or a prompt change. Periodic review matters.

  3. Static security isn’t enough — checking your clawdbot.json at setup time is a start, but runtime behavior can diverge from configuration. Watch what your agent actually does, not just what it’s configured to do.

  4. Red-teaming your own agent is smart — Singulr automates this at enterprise scale, but even basic testing (can someone prompt-inject your agent in a group chat?) is worth doing periodically.

The pattern is clear: every major enterprise platform — AvePoint, Cohesity, Mimecast, and now Singulr — is building governance specifically for AI agents. The agent itself isn’t the product anymore. Control over the agent is.

Singulr AI’s Agent Pulse is available now. The company is based in Palo Alto and previously focused on LLM governance before expanding to agentic AI systems.