On July 16, 2026, a San Francisco startup called Alterion launched a product called Draco with a simple pitch: control your agents the moment they act. Two weeks earlier, Forrester had done something more telling than any single launch. The analyst firm added a formal evaluation category for the AI agent control plane, the clearest signal yet that governing autonomous agents has become its own enterprise software market rather than a feature bolted onto something else.

That matters because most companies did not choose to need this. They deployed AI agents fast, watched them spread across clouds, SaaS tools, and endpoints, and only later realized nobody could see what those agents were actually doing in production. An AI agent control plane is the answer taking shape in 2026: a dedicated layer that discovers every agent running inside a business, watches its behavior in real time, and enforces policy before a risky action goes through. This article breaks down what changed, who is building it, and how to think about adopting one.

Why AI Agent Runtime Security Became Unavoidable

For most of 2024 and 2025, businesses managed AI agents the way they managed any new software: with access controls set at deployment and reviewed periodically. That model assumed agents behaved predictably between reviews. Agentic AI broke that assumption. An agent can chain new tool calls, request new data access, or take an unplanned action mid-task, all without a human touching a configuration screen.

AI agent runtime security exists to close that gap. Instead of checking permissions once at setup, a runtime layer inspects every prompt, tool call, and response as it happens, comparing it against a baseline of normal behavior and blocking anything that crosses a policy line. Draco’s own framing captures the shift well: coverage of the full OWASP Top 10 for Agentic Applications, the industry-standard risk framework published by OWASP’s GenAI Security Project, applied continuously rather than at a single audit checkpoint.

This is also where shadow AI agents fit in. Runtime tools do not just watch sanctioned deployments; the better ones discover agents nobody in security ever approved, because those unsanctioned agents are frequently where the real exposure sits.

Inside the 2026 Control Plane Land Rush

Draco is not entering an empty market, it is entering a crowded one that formed almost overnight. Microsoft shipped Agent 365 to general availability in May 2026 as its own first-party control plane. Weeks later Cisco acquired Astrix Security, a non-human identity security specialist, in a deal reported at roughly 400 million dollars specifically to build out agent control capabilities. Independent challengers raised money on the same thesis: WitnessAI closed 58 million dollars to extend into agentic control, and Noma Security closed a 100 million dollar Series B.

What separates Draco’s approach is the no-code claim: no agent code changes and no SDK integrations, with visibility across an enterprise promised in under a week instead of a multi-month rollout. The platform organizes its work into three functions, exploring all agent traffic whether sanctioned or shadow, protecting with real-time semantic guardrails, and helping teams comply with frameworks including SOC 2, ISO 42001, and NIST’s AI Risk Management Framework by generating audit-ready evidence on demand.

The company behind it was founded by a former McKinsey partner and a former Google engineering vice president, a pairing that signals the target buyer clearly: enterprise risk and security leaders, not individual developers experimenting with agent frameworks.

What Is an AI Agent Control Plane and How to Evaluate One

Strip away the branding and an AI agent control plane does four things: it discovers every agent touching your environment, it profiles what normal behavior looks like for each one, it enforces guardrails on risky actions in real time, and it produces an audit trail regulators and auditors can actually use. Any product claiming the category should be measured against those four jobs, not against marketing language.

For a business evaluating options, three questions cut through most of the noise. First, does it require code changes or SDKs, since integration friction is the single biggest reason governance projects stall. Second, how does it treat AI agent identity, since an agent acting under a shared service credential is much harder to audit than one with its own verifiable identity. Third, does its compliance mapping cover the frameworks your industry actually enforces, rather than a generic checklist.

Smaller companies without a dedicated security team should not assume this category is out of reach. Several vendors, including Draco, are explicitly pricing and packaging for fast deployment rather than the year-long procurement cycles typical of legacy security software.

The Governance Theater Risk Ahead

Forrester formalizing this category will likely accelerate two things at once: real security investment, and a wave of vendors relabeling existing products as control planes without the runtime enforcement to back it up. That is a familiar pattern in enterprise security, and it means buyers need to test claims rather than take a product page at face value.

The more interesting long-term question is whether control planes converge with the AI agent governance platforms offered directly by the major clouds. Microsoft, Google, and AWS all now bundle identity and policy tools into their own agent platforms, which raises the possibility that independent control plane vendors either get acquired, as Astrix was, or specialize deeply in the multi-cloud, multi-vendor visibility that a single cloud provider structurally cannot offer.

Key Takeaways

An AI agent control plane has emerged as its own enterprise category in 2026, driven by Forrester’s formal recognition and a wave of launches and acquisitions from Alterion, Microsoft, and Cisco. The core job is consistent across vendors: discover every agent, watch its behavior at runtime, and enforce policy before risky actions complete rather than after. Buyers should evaluate integration friction, identity handling, and compliance mapping before choosing a platform, since the category is moving fast enough that marketing claims are outrunning proven deployments.

Explore more breakdowns of agentic AI tools, platforms, and enterprise deployment strategy at BigAIAgent. Is your organization already running more AI agents than your security team can actually see?