On February 25, 2026, Gartner released its first Market Guide focusing on Guardian Agents, marking a significant event for this new technology area. A Gartner Market Guide offers a definition of a market and forecasts its near-term capabilities. It focuses on nascent markets, providing insights into representative vendors and their offerings to better understand the market, rather than ranking them.

Gartner defines a Guardian Agent as a system that oversees AI agents, ensuring they act in accordance with established objectives and limitations. Security and identity professionals in enterprises can request a limited distribution copy of Gartner’s Market Guide for Guardian Agents.

Recent news from outlets like the Wall Street Journal, The Financial Times, Forbes, and Bloomberg demonstrates the rise of AI agents. Team8’s 2025 CISO Village Survey showed that:

• Almost 70% of companies already utilize AI agents (systems that both respond and take action) in live environments.
• An additional 23% plan to implement them in 2026.
• Two-thirds of these companies are building them internally. 

However, the market guide highlights that this rapid adoption by businesses is exceeding existing governance procedures. This increases the potential for “operational failures and noncompliance” as AI agents gain greater autonomy and are integrated into crucial business processes.

We concur, recent cloud outages arising from independent AI agent conduct are not surprising. In early implementations, AI agent deployments are generating more identity dark matter than conventional service accounts – this includes unmanaged and opaque identities, local credential authentication, tokens that never expire, and unrestricted access privileges.

Furthermore, AI agents tend to seek shortcuts, looking for the easiest way to achieve satisfactory results, as detailed in our ““Lazy LLMs,”” article. They can exploit unused or inactive accounts or unsecured tokens, generally with plainly-coded credentials and extensive rights. This allows them to finish tasks regardless of whether it should have been permitted, resulting in unanticipated or unlikely events.

Adding to the risk, the 2026 CrowdStrike Global Threat Report stated that malicious actors also actively abuse AI systems, sending harmful prompts to GenAI tools in over 90 organizations and taking advantage of AI development platforms.

Read our prior article in The Hacker News to learn more about how AI agents increase and manipulate what we term “Identity Dark Matter.”

Given the confirmed need for AI agent oversight, how to technically accomplish it becomes the pressing issue. Gartner offers significant value in this area by assessing the market to help determine what is possible and narrowing it down to what is most useful when considering the issue that needs to be solved.

The market guide outlines mandatory features in 3 key areas:

These core areas incorporate nine features that helped shape our beliefs of the five tenets underlying secure (and productive) AI agent use.

Even when vendors are addressing the same needs of Guardian Agents, their strategies and solutions often have varied structural foundations and differ greatly.

Gartner details six emerging delivery and integration strategies which greatly impact adopters beyond initial impression. They affect where control lies, how much real-world visibility users receive, how enforceable the regulation will be, and how much coverage from the agent estate can be attained.

Here is our brief take on each model:

• Standalone Oversight Platforms: Generally, they are the easiest to start with. They collect logs, telemetry, and events into one place and can provide meaningful posture visibility, auditability, and analysis. However, they tend to lean more towards observation than direct intervention. This is useful, but it is not the same as active control. Visibility alone can not prevent poor actions before they happen, which is pivotal to AI risk posture.
• AI/MCP Gateways: A clear, intuitive model: incorporate a central control point, forcing all agent traffic through it. This central layer would then monitor and enforce policy across multiple agents. But this requires constant active traffic through the set layer. Gateways can become constricting and lead to false assurance. If teams bypass them, or interactions happen outside the set governed route, visibility breaks down promptly.
• Embedded or In-Line Run-Time Modules: Positioned closer to execution, whether that be inside the agent platform, an AI management platform, or an LLM proxy. They are often easier to activate and act swiftly, making them appealing. However, the downside is their platform-bound nature. They govern the environment they exist in, limiting broader enterprise coverage. Great local control makes for weak enterprise-wide consistency within agent-spanning multiple stacks.
• Orchestration Layer Extensions: These are appealing when orchestration serves as the operating layer for multi-agent workflows, adding policy, visibility and oversight to the workflow level. However, they assume orchestration acts as the point for impactful control. If the agents are not commonly run through orchestration layers, this is irrelevant. Its power relies on the architecture.
• Hybrid Edge – Cloud Models: Realism is achieved with these models. As Gartner notes, these are becoming more essential as agent ecosystems become more endpoint-focused. This model shares oversight through local execution environments and cloud analysis, which can reduce latency and improve runtime relevance. This avoids centralizing everything in one junction and raises the complexity bar as implementation of distributed governance is difficult.
• Coordination Mechanisms: Standards, APIs, and hooks are connectors between models, rather than an official deployment model. Currently, these connectors are immature. Gartner specifies that integration from AI agent platforms remains difficult due to a lack of standard interfaces. So, adopters should carefully note between simply “supports standards” versus if something “works seamlessly in production”. This coordinated layer is necessary, but should not be taken as solved.

Regardless of the technical approach, Gartner strongly suggests needing more than the governance of each AI agent built into a single cloud provider, identity tool, or AI platform. Specifically, the following is called out;

“A neutral, trusted guardian agent layer with multiple guardian agents performing separate but integrated oversight functions enforces routing across all providers. Thus, the guardian agent acts as the missing universal enforcement mechanism.”

The most significant long-term conclusion we obtained from the Market Guide is that Guardian Agents will not be limited to features embedded in AI platforms. Gartner explicitly states that “enterprises will require independent guardian agent layers that operate across clouds, platforms, identity systems, and data environments.”

Why? AI agents exist in a variety of places.

Agents engage with APIs, applications, data stores, infrastructure components, and each other across multiple environments. Although cloud providers can watch over agents within their ecosystem, once those agents collaborate, give tasks, or operate across different suppliers, there is no single platform that can enforce governance alone.

Therefore, Gartner argues that organizations will increasingly introduce business-owned guardian agent layers. These will control individual platforms and oversee agents throughout the enterprise.

Governance cannot live solely within the platforms that create or host AI agents. It needs its own, elevated space.

In conclusion, agent governance in the future will not be platform-exclusive oversight. It will be business-owned oversight. The organizations that adopt this architecture early will be in better standing to grow their agents safely. Without risking automation, their infrastructure, data, and personal identities are better ensured.

Despite the excitement surrounding AI agents and the major stories projecting these agents replacing jobs, the Guardian Agent market is still growing. According to Gartner, “Today, guardian agent deployments are mainly prototypes or pilots, although advanced organizations are already using early versions of them to supervise AI agents.”

The guardian agent market, including technologies for autonomous AI agents’ oversight, security, and governance, is rapidly accelerating as agentic AI is quickly adopted across industries.

Frankly, a similar statement of the Agentic market overall can be made. AI agents within both Orchid (the company and product) have been implemented. But organizations are only understanding what is possible. Individual employees have their own AI agents. Many vendors offer built-in AI agents, extending from simple chatbots. Corporate standard platform have been implemented to assist or replace certain tasks.

However, AI agent visibility is essential sooner rather than later. Orchid Security recommends that the guardrails and governance necessary for human users are implemented to guide their AI companions as well.

AI agents are rapidly changing how enterprises operate.

The questions are no longer “should we use them”, but “how shall we govern them?”

Safe use of AI agents necessitates using the same core principles from identity practitioners — the least accessible privilege, lifecycle management, and audibility — and apply them within the newest form of non-human protocol.

If identity dark matter is considered invisible and uncontrollable, unmanaged AI agents may become their fastest-growing source. Taking action now to bring them to light will allow organizations to rapidly move forward with AI without risking trust, compliance, or security. Therefore, Orchid Security is building identity infrastructure to eliminate dark matter, and make Agent AI adoption safe to deploy at enterprise scale.