Gartner Predicts Guardian Agents Will Ensure Trustworthy AI by 2030
Guardian agents will ensure AI processes remain reliable and secure as AI risks grow, with Gartner forecasting they will account for 10-15% of the agentic AI market by 2030.
By 2030, guardian agent technologies will account for 10-15% of the agentic AI market, according to Gartner. These AI-based technologies are designed to support trustworthy and secure interactions with AI, functioning as both assistants and semi-autonomous or fully autonomous agents.
Guardrails Needed as Agentic AI Use Grows
A May 2025 Gartner webinar poll of 147 CIOs and IT leaders revealed:
- 24% had deployed fewer than a dozen AI agents.
- 4% had deployed over a dozen.
- 50% were researching or experimenting with the technology.
- 17% planned to deploy AI agents by the end of 2026.
Avivah Litan, VP Distinguished Analyst at Gartner, emphasized: "Agentic AI will lead to unwanted outcomes if not controlled with the right guardrails. Guardian agents balance runtime decision-making with risk management."
Risks Increase as AI Agent Power Spreads
52% of respondents focus AI agents on internal administrative functions (IT, HR, accounting), while 23% target customer-facing applications. Key threats include:
- Input manipulation and data poisoning (e.g., credential hijacking, fake websites).
- Agent deviation and unintended behavior, leading to reputational damage.
Three Primary Guardian Agent Use Types
Gartner recommends organizations adopt guardian agents for:
- Reviewers: Identify and review AI-generated content for accuracy.
- Monitors: Track AI actions for human or AI follow-up.
- Protectors: Adjust or block AI actions in real-time.
Litan warned: "As enterprises adopt multi-agent systems, humans can’t keep up with errors or malicious activities. Guardian agents provide automated oversight."
By 2028, 70% of AI apps will use multi-agent systems, making guardian agents a critical pillar of AI integration.
Image credit: iStock.com/MF3d
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About the Author

Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.