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10-15% of market by 2030
webA Gartner market prediction relevant to practitioners tracking how AI safety concepts (agent oversight, monitoring) are being commercialized; useful for understanding industry deployment trends around agentic AI governance.
Metadata
Importance: 42/100press releasenews
Summary
Gartner forecasts that 'guardian agents' — AI systems designed to monitor, audit, and constrain other agentic AI systems — will represent 10-15% of the agentic AI market by 2030. This signals growing industry recognition that autonomous AI agents require oversight mechanisms built into deployment architectures. The prediction highlights a nascent but commercially significant category of AI safety infrastructure.
Key Points
- •Gartner predicts guardian agents will constitute 10-15% of the agentic AI market by 2030, representing a significant commercial safety layer.
- •Guardian agents are AI systems tasked with monitoring, auditing, and constraining the behavior of other autonomous AI agents.
- •The forecast reflects enterprise demand for oversight mechanisms as agentic AI deployments scale across industries.
- •This trend suggests AI safety concepts like monitoring and containment are being operationalized into commercial products.
- •The emergence of guardian agents as a market category may influence governance frameworks and deployment standards for agentic systems.
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| Page | Type | Quality |
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| Agentic AI | Capability | 68.0 |
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[Newsroom](https://www.gartner.com/en/newsroom)/[Information Technology](https://www.gartner.com/en/newsroom/topics/Information%20Technology)/Press Release
# Gartner Predicts that Guardian Agents will Capture 10-15% of the Agentic AI Market by 2030
NATIONAL HARBOR, MD, June 11, 2025
## Guardian Agents Will Ensure AI Processes Stay Reliable and Secure as AI Risk Surface Expands
By 2030, guardian agent technologies will account for at least 10 to 15% of agentic AI markets, according to Gartner, Inc.
Guardian agents are [AI-based technologies](https://www.gartner.com/en/information-technology/topics/ai-strategy-for-business) designed to support trustworthy and secure interactions with AI. They function as both AI assistants, supporting users with tasks like content review, monitoring and analysis, and as evolving semi-autonomous or fully autonomous agents, capable of formulating and executing action plans as well as redirecting or blocking actions to align with predefined agent goals.
**Guardrails Are Needed as Agentic AI Usage Continues to Grow**
According to a Gartner May 19, 2025 webinar poll of 147 CIOs and IT function leaders, 24% of respondents had already deployed a few AI agents (less than a dozen) and another 4% had deployed over a dozen.
The same poll question found that 50% of respondents said they were researching and experimenting with the technology, while another 17% of respondents said that they had not done so, but planned to deploy the technology by the end of 2026 at the latest. Automated trust, risk and security controls are needed to keep these agents aligned and safe, accelerating the need for and rise of Guardian Agents.
“Agentic AI will lead to unwanted outcomes if it is not controlled with the right guardrails,” said [Avivah Litan](https://www.gartner.com/en/experts/avivah-litan), VP Distinguished Analyst at Gartner. “Guardian agents leverage a broad spectrum of agentic AI capabilities and AI-based, deterministic evaluations to oversee and manage the full range of agent capabilities, balancing runtime decision making with risk management.”
**Risks Increase as Agent Power Increases and Spreads**
Fifty-two percent of 125 respondents from the same webinar poll identified that their AI agents are or will primarily focus on use cases related to internal administration functions such as IT, HR, and accounting, while 23% are focused on external customer facing functions.
As use cases for AI agents continue to grow, there are several [threat categories](https://www.gartner.com/en/cybersecurity/topics/cybersecurity-and-ai?utm_medium=press-release&utm_campaign=RM_GB_YOY_ITSEC_NPP_PR1_PILCYBERAI) impacting them, including input manipulation and data poisoning, where agents rely on manipulated or misinterpreted data. Examples include:
- Credential hijacking and abuse leading to unauthorized control and data theft.
- Agents interacting with fake or criminal websites and sources that can result in poisoned actions.
- Agent
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