AI Agents: Why Governance, Oversight And Guardrails Matter More Than Ever

Recent headlines about autonomous AI systems behaving unpredictably are a reminder that as AI becomes more autonomous, the risks become more complex.

While the specifics of any individual incident will differ, they highlight a broader challenge facing businesses: how to introduce increasingly autonomous AI systems without losing visibility, oversight and control.

AI agents can access systems, make decisions and carry out tasks with limited human intervention. The efficiencies are obvious. So too are the legal, reputational, operational and cyber security risks if appropriate guardrails are not in place.

Our recent Great Expectations report, conducted with The Financial Times, surveyed 300 senior executives from organisations with more than £500 million in annual revenue across seven international markets. It found that while 94% of executives see AI as strategically important, only 7% report strong adoption across their entire organisation, and 55% say scaling AI has been harder than expected.

The challenge is rarely the technology itself. More often, it is the governance, accountability and risk management needed to deploy AI with confidence. As organisations increasingly explore AI agents, those challenges will only become more important.

Why AI agents create new risks for your business

Generative AI tools typically respond to prompts provided by a user. AI agents are different. They can initiate actions, access systems, use tools, interact with third-party applications and complete multi-step tasks with limited human input.

1. Loss of visibility and control

One of the biggest challenges with AI agents is understanding exactly how decisions are being made. An agent may pull information from multiple sources, apply reasoning and take actions that are difficult for users to fully predict and track.

This can lead to situations where organisations struggle to explain:

• Why a decision was made
• Which data was used
• Whether appropriate policies were followed
• Who is responsible when something goes wrong

As AI systems become more autonomous, explainability becomes increasingly important for regulatory compliance, stakeholder confidence and internal accountability.

2. Data protection, cyber security and resilience risks

AI agents often require access to internal systems, documents, business data and third-party applications to operate effectively. While this can unlock significant efficiencies, it can also increase an organisation's attack surface and create new resilience challenges.

Without appropriate controls, organisations may face risks such as:

• Unauthorised access to sensitive information
• Inadvertent disclosure of confidential data
• Excessive permissions being granted to AI systems
• Data transfers that breach contractual or regulatory requirements
• Infringement of third-party IP rights
• Increased exposure if a connected system or third-party provider is compromised
• Operational disruption if agents fail or produce inaccurate outputs

As organisations become more reliant on AI agents, resilience becomes just as important as compliance. An AI system that meets regulatory requirements but cannot operate securely, accurately or reliably is unlikely to deliver lasting business value.

3. Accountability gaps

Many organisations are still experimenting with AI in isolated business functions and this siloed approach is one of the major barriers to successful adoption. AI initiatives often emerge within individual teams without a clearly defined enterprise-wide governance structure.

The result is that AI agents can end up operating in an environment where responsibilities are unclear:

• Who owns the system?
• Who approves deployment?
• Who monitors ongoing risks?
• Who decides whether a use case is acceptable?

Without clear answers, organisations may find themselves exposed when problems arise.

4. Regulatory and compliance exposure

AI regulation is evolving rapidly across multiple jurisdictions. Organisations already face complex requirements relating to privacy, cyber security, consumer protection and sector-specific regulation.

AI agents add further complexity because they may:

• Make recommendations affecting customers
• Process personal information
• Execute transactions
• Generate content or communications on behalf of the business

Businesses must understand whether existing governance frameworks adequately address these new use cases.

5. Operational errors at scale

The attraction of AI agents is their ability to act quickly and efficiently. The challenge is that errors can also happen quickly and at scale.

A mistaken instruction, incorrect piece of training data or poorly designed workflow could potentially be repeated hundreds or thousands of times before a problem is identified.

When human review is removed from a process, organisations need alternative safeguards to detect and prevent mistakes.

These risks are interconnected. In many cases, they stem not from the technology itself, but from unclear ownership, insufficient oversight and weak governance structures.

The businesses succeeding with AI are focusing on governance first

Organisations with stronger AI adoption are significantly more likely to have active board engagement in AI oversight and governance. They are also far more likely to involve the General Counsel at the outset of AI initiatives.

This reflects an important shift in how organisations should think about AI agents. The conversation is no longer purely about technology.

It is about:

• Risk appetite
• Decision-making authority
• Regulatory readiness
• Mitigation measures
• Governance structures
• Human oversight

Businesses that treat AI as an enterprise issue rather than a technology project are generally better positioned to scale safely and effectively.

Six practical steps to mitigate AI agent risks

1. Establish clear ownership

Every AI agent should have a named business owner responsible for:

• Oversight
• Risk management
• Performance monitoring
• Approval of significant changes

Ownership should not sit solely with IT or technology teams.

2. Define acceptable use and risk appetite

Many organisations are deploying AI faster than they are defining the rules surrounding it.

Businesses should establish clear policies covering:

• High-risk use cases
• Prohibited activities
• Escalation procedures
• Human review requirements
• Decision-making thresholds

Employees need clarity about why and when AI agents can and cannot be used.

3. Implement human oversight

Not every decision should be delegated to an AI agent. Similarly, overlaying AI on existing workflows without proper consideration risks embedding and exacerbating existing flaws or inefficiencies that may be inherent in those processes.

Businesses should identify:

• Which decisions require human approval?
• Which activities can be automated?
• When intervention is required?
• How exceptions are handled?

The greater the potential impact of a decision, the stronger the case for human oversight.

4. Review access permissions carefully

AI agents should operate on the principle of 'least privilege'.

This means ensuring they only have access to:

• The systems they genuinely need
• The data required to perform tasks
• The tools necessary for authorised activities

Regular audits can help identify unnecessary permissions before they create problems.

5. Build governance into deployment from day one

One of the most common mistakes organisations make is treating governance as something that can be added later.

This is precisely what prevents many organisations from moving beyond isolated pilots. Strong governance does not slow innovation; it enables organisations to scale with confidence.

The most successful adopters are embedding legal, compliance, risk and business stakeholders into AI programmes from the outset rather than attempting to retrofit controls once systems are already operational. Crucially, governance frameworks must evolve alongside AI adoption – and ideally stay one step ahead of it.

6. Test AI agents like any other critical system

Before deployment, organisations should subject AI agents to the same scrutiny as any business-critical technology.

This includes:

• Security testing
• Access reviews
• Incident response planning
• Business continuity exercises
• Ongoing monitoring of outputs and performance

Testing helps identify vulnerabilities, validate controls and ensure organisations can respond quickly if something goes wrong. Testing needs to continue during the lifetime of the operation of the agent too to identify and rectify when agents start to act outside of their authority.

AI agents are an opportunity – but only with the right guardrails

The emergence of AI agents represents the next phase of AI adoption. The potential benefits are considerable, but so are the risks.

The organisations that succeed will not necessarily be those that deploy AI agents first. They will be those that deploy them responsibly, in line with their business strategy, with clear governance, defined accountability and appropriate oversight.

As our Great Expectations research shows, the real challenge is not ambition. It is execution. Governance, accountability and oversight are what enable organisations to move AI from experimentation to enterprise-wide value.

Frequently asked questions

Q: Are AI agents riskier than generative AI tools?

A: Potentially, yes. Because they can take actions rather than simply generate outputs, AI agents often create additional governance, security, compliance and accountability risks.

Q: Do organisations need a separate governance framework for AI agents?

A: Not necessarily, but existing AI governance frameworks should be reviewed to ensure they adequately address autonomy, decision-making authority, oversight and accountability.

Q: Who should oversee AI agent deployments?

A: Responsibility should be shared across business, legal, risk, compliance and technology teams, with clear executive ownership and board-level visibility for significant deployments.

Q: What is the biggest mistake organisations make?

A: Treating governance as an afterthought. Many organisations focus on deployment first and only consider risk and oversight once problems emerge.

Q: How can businesses get started?

A: Begin by assessing your current AI governance framework, mapping AI use cases across the organisation, identifying high-risk activities and establishing clear ownership and oversight structures.

How prepared is your organisation?

Whether you are exploring AI agents for the first time or looking to scale existing deployments, now is the time to ensure the right governance structures, controls and decision-making processes are in place.

Our AI team advises boards, general counsel and executive leadership teams on AI governance, regulatory readiness, risk allocation, procurement, deployment and enterprise-wide adoption strategies. If you would like to discuss how to deploy AI agents safely, responsibly and at scale, please get in touch with our team.

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