For many organisations, the first phase of AI governance was relatively simple to understand. The business needed to decide whether staff could use tools such as ChatGPT, what information they could put into them, whether customer data was allowed, and what level of review was needed before AI-assisted outputs were used externally.
That phase is not over, but it is no longer enough.
The UK AI governance conversation is now moving quickly toward agentic AI: systems that can plan, act, select tools, interact with users, make recommendations, access connected systems, or trigger downstream business processes. These are not just chatbots producing text. They are AI-enabled systems that can do things.
That difference changes the entire control model.
When an AI tool simply drafts an email, the main questions are about accuracy, confidentiality, bias, intellectual property, and review. When an AI agent can access a CRM, prioritise leads, issue customer responses, approve refunds, triage complaints, escalate cases, update records, or recommend decisions about people, the questions become more operational and more serious.
Who authorised the agent? What systems can it access? What data can it use? What actions can it take? When must a human intervene? How are decisions logged? How are harms detected? How can a decision be challenged, paused, corrected, or reversed?
This is why agentic AI governance is becoming one of the strongest AI GRC topics in the UK.
Why the UK regulatory signal matters
On 31 March 2026, the Digital Regulation Cooperation Forum published its paper, The Future of Agentic AI. The DRCF describes the paper as a forward-looking exploration of agentic AI and how UK regulatory frameworks can help realise the opportunities of the technology in a responsible and safe way. It also emphasises regulation as an enabler of innovation, while protecting consumers and their rights.
The DRCF matters because it brings together four major UK regulators to create a more coherent approach to digital regulation. Its own description states that the DRCF brings together four UK regulators to deliver a coherent approach for the benefit of people and businesses online.
For business leaders, this is an important signal. Agentic AI is not being viewed only as a technology issue. It is being framed as a cross-regulatory issue involving governance, data protection, cybersecurity, consumer rights, competition, market behaviour, accountability, and harm prevention.
That means agentic AI governance cannot sit only inside IT. It requires a joined-up model across operations, compliance, legal, risk, data protection, cybersecurity, procurement, customer experience, HR, and executive leadership.
The issue is not whether AI agents are useful
AI agents can create real business value.
They can reduce manual workload, speed up customer service, improve knowledge retrieval, automate routine workflows, support sales teams, improve internal reporting, and reduce friction across fragmented systems. For SMEs, the potential is especially attractive because AI agents may allow smaller teams to operate with the coordination and responsiveness of much larger organisations.
But the same qualities that make agents valuable also make them risky.
An AI agent is valuable because it can act with some degree of autonomy. It can interpret an objective, break it into steps, use tools, retrieve information, and move a task forward. But autonomy creates a governance problem. The more an AI system can do, the more the organisation must define what it is allowed to do.
A business should not wait until an AI agent makes a poor recommendation, discloses sensitive data, misclassifies a customer, creates unfair outcomes, triggers an unauthorised workflow, or causes commercial damage before deciding what the control framework should have been.
Agentic AI needs governance before deployment, not after incident response.
The move from acceptable use policies to control frameworks
Many organisations still treat AI governance as a policy exercise. They create an acceptable use policy, tell staff not to upload confidential information, and ask managers to review AI-generated work.
That is useful, but it is not sufficient for agentic AI.
Policies tell people what they should do. Control frameworks define what systems are technically and operationally allowed to do.
Agentic AI needs the second category.
A mature AI control framework should define the lifecycle of an AI agent from proposal to approval, deployment, monitoring, incident management, and retirement. It should make clear who owns the use case, who approves it, what business process it affects, what systems it touches, what data it consumes, what decisions it influences, and what evidence is retained.
The key shift is this:
AI governance can no longer be a document that sits outside the workflow. It has to become part of the workflow.
What should an agentic AI control framework include?
A practical agentic AI governance framework should include several core controls.
1. Use-case approval before deployment
Every AI agent should have a named business owner, a defined purpose, and a documented approval route. The organisation should be able to answer a basic question: who approved this agent to operate in this business process?
This matters because agentic AI can blur ownership. A team may configure the agent, a vendor may provide the platform, IT may connect the systems, and users may rely on the outputs. Without clear accountability, everyone can assume someone else is responsible.
A use-case approval process should capture purpose, expected value, affected stakeholders, risk level, data categories, system access, human oversight requirements, and success measures.
2. System access controls
An AI agent should not have broad access simply because it is technically convenient.
Access should be limited by role, purpose, and risk. If the agent only needs to read product documentation, it should not be able to access customer financial records. If it only needs to draft a response, it should not be able to send that response without approval. If it only needs to summarise a case, it should not be able to update the case outcome.
This is basic access governance applied to a new type of actor: the AI agent.
3. Data classification and minimisation
Agentic AI governance must include clear rules on what data the agent can access and use. Public information, internal business information, confidential commercial information, personal data, special category data, financial data, employment data, and customer records should not be treated as if they carry the same risk.
The Data Use and Access Act 2025 has also changed the UK data protection context. The ICO says the Act amends, but does not replace, the UK GDPR, the Data Protection Act 2018 and PECR. It also states that automated decision-making changes open up the range of lawful bases organisations can rely on when using personal information to make significant automated decisions, provided appropriate safeguards continue to apply, with special category data remaining more protected.
For agentic AI, this means businesses need more than a generic statement that “AI may use data”. They need to classify the data, understand the legal basis, apply minimisation, and define safeguards.
4. Human-in-the-loop thresholds
Not every AI action needs human approval, but some clearly should.
The control framework should define thresholds for human review. These may be based on risk, customer impact, financial value, regulatory exposure, vulnerability, uncertainty, or decision significance.
For example, an AI agent may be allowed to summarise a customer complaint without approval, but not close the complaint. It may recommend a refund, but not issue one above a defined value. It may prioritise candidate applications for review, but not reject candidates without meaningful human involvement. It may propose a credit decision, but not make a significant decision about a person without the correct legal and procedural safeguards.
The point is not to block automation. The point is to decide where automation is acceptable and where human judgement remains necessary.
5. Audit logging and explainability
Agentic AI systems need audit trails.
A business should be able to see what the agent was asked to do, what information it accessed, what tools it used, what output it generated, what action it took, whether a human approved it, and what happened next.
This is not only a compliance requirement. It is an operational necessity. Without logs, the organisation cannot investigate incidents, improve the system, evidence control, resolve disputes, or understand whether the agent is creating value.
For regulated sectors, this becomes even more important. If an AI agent influences a customer outcome, financial recommendation, hiring decision, complaint response, vulnerability assessment, or access to a service, the organisation needs evidence.
6. Vendor due diligence
Many AI agents will be built on third-party platforms. That creates vendor risk.
Before deployment, organisations should understand how the vendor processes data, whether customer data is used for model training, where data is stored, what security controls apply, what logs are available, what contractual protections exist, how sub-processors are managed, and what happens if the service fails.
Procurement should not treat AI agents as ordinary software subscriptions. If the system can act inside a business process, procurement needs to understand the operational, legal, and reputational risk attached to that capability.
7. Incident response and rollback
Agentic AI introduces new incident scenarios.
An agent may send incorrect information to customers, act on outdated data, escalate the wrong cases, expose sensitive information, create biased outcomes, overstep its authority, or trigger workflows that create downstream consequences.
The organisation needs a clear response model. Who can pause the agent? Who investigates? How are affected records identified? How are customers notified where needed? How are decisions reversed? How is the root cause documented? How are controls updated before the agent is re-enabled?
A kill switch is not a strategy, but every agentic AI deployment should have a defined stop mechanism.
Automated decision-making must be screened early
Agentic AI governance also overlaps with automated decision-making.
The ICO opened a consultation on draft guidance about automated decision-making, including profiling, on 31 March 2026, with a closing date of 29 May 2026. The ICO states that the updates follow the introduction of the Data Use and Access Act 2025 and that the guidance is aimed at data protection officers, compliance professionals, and technical leads overseeing the use or procurement of ADM systems.
This is directly relevant to AI agents.
An organisation deploying an AI agent should screen whether the system is making, contributing to, or materially influencing decisions about people. It should also assess whether those decisions are significant, whether they involve personal data, whether special category data is involved, whether meaningful human involvement exists, and whether safeguards such as transparency, challenge, review, and redress are in place.
For SMEs, this does not need to be bureaucratic. But it does need to be explicit.
A simple ADM screening step at the start of every AI agent use-case review can prevent serious problems later.
Board-level accountability is becoming unavoidable
Agentic AI governance is not only a technical control issue. It is an accountability issue.
If an AI agent affects customers, employees, suppliers, pricing, financial decisions, complaints, risk assessments, or regulated processes, senior leadership needs visibility. Boards and directors do not need to understand every technical detail, but they do need to know where AI agents are being used, what risks have been accepted, what controls exist, and whether the systems are delivering measurable value.
This is where AI GRC becomes commercially important.
Good governance should not be positioned as a brake on innovation. It should be positioned as the mechanism that allows the business to adopt AI safely, confidently, and at scale.
A controlled AI agent can be improved, audited, defended, and expanded. An uncontrolled AI agent becomes a future incident waiting for a trigger.
What SMEs should do now
SMEs should not wait for perfect regulation before acting. The direction of travel is already clear.
Start by creating an AI agent register. Record every agentic system in use or under consideration. Include purpose, owner, vendor, connected systems, data used, actions permitted, human review points, and risk rating.
Then introduce an approval workflow. No AI agent should be connected to business systems without review. The review should include data protection, cybersecurity, operational risk, customer impact, contractual risk, and value case.
Next, define access and action boundaries. Decide what the agent can read, write, recommend, send, approve, update, or trigger. Separate low-risk assistance from high-impact action.
Then implement logging. If the business cannot reconstruct what the agent did, the agent is not ready for operational deployment.
Finally, monitor value as well as risk. Agentic AI should have measurable business objectives. Time saved, cost reduced, error rates, customer satisfaction, response speed, revenue impact, and risk reduction should be tracked. AI governance should not only ask “is this safe?” It should also ask “is this worth it?”
Strategic AI Guidance position
Agentic AI is turning AI governance from policy management into operational control.
The businesses that benefit most from AI agents will not be the ones that deploy them fastest. They will be the ones that deploy them with clear purpose, strong controls, measurable value, and defensible accountability.
Strategic AI Guidance helps organisations move from informal AI experimentation to controlled AI adoption, with governance frameworks that make AI useful, auditable, and defensible.
For SMEs, that means practical AI control frameworks, use-case approval processes, data and risk classification, human-in-the-loop design, vendor review, automated decision-making screening, logging requirements, and board-level visibility.
AI agents are coming into business workflows quickly. The question is not whether they will create opportunities. They will.
The real question is whether your organisation can control them before they start making decisions, taking actions, or creating risks you cannot explain.