"Autonomous AI agents are software entities that can perceive context, make decisions and take actions toward a goal with limited or no human intervention" - Mangesh Mulmule, Vice President and Head of Presales, Google Ecosystem, HCLTech

As autonomous agents scale across the enterprise, the strategic question shifts from what can they do to what should they do, and under whose rules? Without clear governance, organizations invite the rise of shadow AI agents; unsanctioned tools operating outside official visibility - similar to the challenges posed by Shadow IT. The risks include sensitive data leakage, unvetted decisions, malicious executions, compliance breaches and reputational harm.

Industry efforts are centered around practical standards. The OWASP Top 10 for LLM Applications, tailored to threats around LLMs and agentic systems, has established the most critical risks,  such as Intent breaking and goal manipulation, Repudiation & untraceability, Identity spoofing and impersonation and more. Meanwhile, the MITRE’s OCCULT framework proposes a structured way to measure and observe how AI agents perform real offensive actions such as lateral movement, credentials harvesting and network enumeration.

This article outlines how senior leaders can set enterprise guardrails, such as policy, controls and culture, to ensure Agentic AI is ethical, transparent and aligned with business and regulatory expectations, while preventing shadow AI from taking root.

What is Agentic AI governance?

"Agentic AI governance is the policies, controls and oversight that direct autonomous AI agents to act within secure, ethical, legal and business boundaries, while preserving innovation velocity" - Mangesh Mulmule, Vice President and Head of Presales, Google Ecosystem, HCLTech

Agentic systems differ from traditional predictive models. They don’t just recommend; they plan and act collaboratively with other agents, which creates a unique set of challenges.

Well-governed autonomy accelerates outcomes, reduces human bottlenecks and raises the bar for accountability. The shift calls for greater controls, observability and automated oversight, often via what Gartner calls guardian agents that monitor and, when necessary, block other agents.

Gartner expects guardian agents will capture between 10-15% of the Agentic AI market by 2030, underscoring the operational reality that humans alone can’t scale oversight.

Traditional versus agentic governance

• Traditional AI governance centers on data lineage, model risk, validation and pre-deployment testing
• Agentic governance adds runtime rules of engagement, including what an agent can access, when to seek human sign-off, how to record actions and how to contain misbehavior, including decommissioning the agent automatically

The privacy community is responding in kind. IAPP emphasizes the importance of layered guardrails to anticipate and handle emerging risks while enabling operational objectives.

The bottom line: Agentic governance blends traditional AI risk management with operations playbooks from SRE, security and compliance, which are implemented in real time.

Why are guardrails necessary for Agentic AI?

"Guardrails are necessary because autonomous agents can take irreversible actions at machine speed; amplifying small design flaws into large commercial, legal, operational and reputational failures" - Mangesh Mulmule, Vice President and Head of Presales, Google Ecosystem, HCLTech

Risks of shadow AI agents:

• Data exfiltration into third-party models and logs
• Noncompliant processing across compliance standards, such as GDPR and CCPA
• Biased or misleading outputs driving poor or unsafe decisions
• Attack surfaces from prompt injection, insecure plug-ins and “excessive agency”
• Loss of auditability and accountability when actions aren’t logged

Real-world consequences:

1. Air Canada chatbot liability: A tribunal found the airline liable after its site’s chatbot misrepresented bereavement fare rules. The ruling emphasized that companies are responsible for the information delivered by their AI
2. Dutch childcare benefits scandal: Algorithmic scoring led to wrongful fraud accusations and severe harm to families. This is an illustration of opaque systems eroding rights and trust

Beyond these cases, the OWASP LLM Top 10 highlights attack classes that turn poorly governed agents into breach vectors.

Takeaway: Shadow agents and ungoverned autonomy multiply risk. Guardrails prevent headline events, but by turning tacit trust into demonstrable controls - stakeholders, regulators and customers can see, they also enable scale.

Key principles for governing Agentic AI systems

Transparency and Explainability

"Transparency and explainability mean that an agent’s capabilities, constraints, data sources and decision rationale are visible as a measurable metric to stakeholders and are auditable by design" - Mangesh Mulmule, Vice President and Head of Presales, Google Ecosystem, HCLTech

Transparency and explainability enhance trust, regulatory defensibility and faster incident response. When something goes wrong, organizations need to reconstruct what the agent knew, what it decided and why. The need to have measurable indicators around agents' behaviour is paramount, as the errors and deviations can creep in slowly as edge-use case data ingestion.

Tools and techniques

• Decision/event logs capturing prompts, tool invocations, inputs/outputs and approvals
• Provenance and traceability through data lineage and content labeling
• Model and agent cards describing intended use, limitations, datasets and risk controls.
• Measurable KPIs for technical, business and operational parameters of AI agents
• Counterfactual and rational views

IAPP recommends multi-tier guardrails with clear documentation and guardian agents can also generate “explanations of record.”

Ethical alignment

A values-aligned agent respects an organization’s ethics, customer commitments and legal obligations. Start by encoding principles such as fairness, dignity, safety and environmental impact into policy and into technical constraints.

Reference widely used frameworks to ground your approach, such as NIST AI Risk Management Framework, ISO/IEC 42001 and industry codes of practice.

Accountability and oversight

Clear lines of responsibility are also non-negotiable. Organizations need to decide who approves agent capabilities, who signs off on risky actions and who holds the pager when something breaks.

The emerging pattern is human-in-the-loop backed by guardian agents that review, monitor and protect by auto-blocking out-of-policy actions.

Governance in practice: Organizations should set thresholds, require role-based access and mandate incident reporting with root cause analysis for agent failures; just like for any other security-related activity.

10 best practices for setting AI guardrails in enterprises

To build clearly defined and effective guardrails, organizations should treat autonomous AI systems like products with clear ownership, vision and a roadmap.

1) Align on risk tolerance and must-avoid outcomes: Gartner advises general counsel to lead structured conversations that classify use cases by risk and define unacceptable outcomes.

2) Establish approval workflows for agent capabilities: Treat new tools, plug-ins and data sources like change-managed assets that require threat modeling, privacy review and business sign-off before enabling actions like sending emails, moving funds, or touching production data.

3) Implement role-based access control (RBAC) and least privilege: Give permissions based on role and context. Decide who can do the task, what they can access, where the data or system lives, and when it’s allowed. This reduces accidental overreach.

4) Audit AI decision logs: Record everything, including the prompt the agent received, who approved the action and what the result was.

5) Deploy guardian agents for oversight. Start with reviewers for content and accuracy, add monitors for behavioral and policy conformance and move to protectors to auto-block actions for high-impact use cases.

6) Contain and test continuously: Use sandboxes for new actions and continuously test different scenarios.

7) Tackle shadow AI head-on. Publish approved tools, implement data loss controls and offer secure, logged alternatives so employees don’t go rogue. (Shadow AI often stems from unmet needs.)

8) Disclose appropriately: Be clear when content is AI-assisted and when a human is the decision-maker. Gartner highlights the importance of external and internal disclosures as part of policy.

9) Train, simulate, measure KPIs: Run scenarios, measure key metrics around business, technical and operational parameters, practice escalation, customer communication and remediation.

10) Measure and report: Track incidents prevented by guardian agents, the percent of actions requiring human approval and time-to-contain. Report quarterly to a risk committee.

Emerging frameworks for Agentic AI governance

OWASP Top 10 for LLM-Based AI

What it is: A community standard cataloging the most critical security risks in LLM and agentic applications.

Key items to operationalize

• LLM01 Prompt Injection: Validate inputs, isolate context and use allow-listed tools
• LLM02 Insecure Output Handling: Never execute model output blindly and sanitize before use.
• LLM06 Sensitive Information Disclosure: Prevent leakage via prompts/outputs and retrieval
• LLM08 Excessive Agency: Restrict what the agent can do without human approval

MITRE OCCULT Framework

What it is: A structured evaluation approach proposed by MITRE researchers to measure models’ potential to enable offensive cyber operations if agents are exploited or misused.

How it helps

• Standardized test design tied to realistic threat scenarios (not exercises)
• Benchmarking and telemetry that highlight when a model/agent is crossing red-lines.
• Risk signaling to governance bodies that certain capabilities require extra containment

How to build trust in Agentic AI systems

"Trust is earned when organizations can show that Agentic AI decisions are constrained, observable, measurable, correctable and aligned with human intent" - Mangesh Mulmule, Vice President and Head of Presales, Google Ecosystem, HCLTech

Strategies that raise confidence

• Visible accountability: Publish decision rights, escalation paths and how complaints are handled
• Explainable records: Provide customers and auditors with an accessible record of what an agent did and why they did it
• Proactive oversight: Guardian agents that review and block, and humans who approve exceptions
• Safe defaults: Simulate everything before executing
• Policy-led transparency: Consistent AI disclosures and opt-out channels for sensitive content

Actionable tips

• Start with low-impact workflows and run agents that can act, but require human confirmation to finalize
• Implement confidence thresholds. If there's any uncertainty or if data provenance is a weak route to a human.
• Retain what’s necessary for performance but avoid hoarding sensitive information
• Invite independent reviews and publish a summary of findings and fixes
• Track and share trust metrics, including false-positive/negative rates, blocked actions and customer satisfaction after agent interactions

A generational capability shift

Agentic AI is a generational capability shift, but with power comes responsibility. Autonomous agents can unlock scale and speed, but also deliver errors at machine pace. Governance is how organizations can harness capability without courting chaos.

Moving forward, guardrails can be established by defining policy, instrument runtime controls and adding automated oversight. It's important to use community frameworks and ground everything in transparency and accountability.

Proactive leaders don’t wait for regulations or incidents to force their hand. They operationalize guardrails now, starve shadow AI by offering safe, sanctioned alternatives, and measure trust as a first-class outcome.

Crucially, treat agentic governance as a core business capability, because in an agentic world, good governance represents significant competitive advantage.

FAQs 

1) What’s the difference between an autonomous AI agent and a traditional AI model?
Traditional models predict, whereas agents act, plan and complete tasks with limited human input. As a result, they need stringent guardrails, not just pre-deployment testing.

2) What is a shadow AI agent and why is it risky?

Shadow AI agents are an unsanctioned agent or AI tool used without IT oversight. Risks include data leakage, noncompliance and excessive agency without audit trails or approvals.

3) What autonomous AI guardrails should every enterprise implement first?

Role-based access control (RBAC) and least privilege, approval workflows for high-impact actions, immutable decision logs, sanctioned tool catalogs and disclosures for AI-assisted content.

4) How do OWASP and MITRE OCCULT fit into autonomous AI governance?

OWASP’s LLM Top 10 provides concrete security controls and OCCULT offers a way to evaluate adversarial/offensive capabilities.

5) Do organizations really need guardian agents?

Yes, as AI proliferates, human oversight won’t scale. Gartner predicts guardian agents will represent 10–15% of the agentic AI market by 2030, acting as reviewers, monitors and protectors.

6) How can organizations prevent legal exposure from agent mistakes?

Combine clear policies and disclosures with testing, human-in-the-loop for risky actions, and robust logging.

7) What’s a pragmatic first 90-day plan for implementing guardrails for automonous AI?

First audit how AI is used, then approve a minimal safe stack, spell out decision rights, enable logging and role-based access controls (RBAC) and pilot guardian agents.