
The ICO's agentic AI call for evidence: What you need to know
The Information Commission's Office (ICO) has opened a six-week call for evidence on agentic AI, closing on 20 November 2026. It focuses specifically on how data protection law applies to the distinctive capabilities and risks of agentic AI systems. Given that many organisations are already deploying agentic AI tools, this development deserves close attention.
This comes at the same time as the ICO publishing the results of its supervision of the largest foundation model providers, following which 10 of the world's largest AI model developers committed to making data protection improvements.
What is agentic AI, and why does it raise data protection questions?
We covered these key questions in the Agentic AI and Data – Five Big Questions article, written by Emma Erskine-Fox, previously published on our website.
Agentic AI consists of AI systems capable of making decisions and taking actions with minimal human oversight and a degree of autonomy. This typically goes beyond the prompt-response interactions of traditional generative AI, as an agent can plan, execute and adapt over time. This means that agentic AI systems tend to process personal information on a larger scale, and in a more complex way, than traditional generative AI.
The ICO flags that an agentic system could use more personal information than necessary to complete a task, or it could disclose special category personal data to an unauthorised third party. Orchestration activities could allow inaccurate information or bias to spread through the chain of processing, affecting later decisions or actions.
This combination raises data protection concerns, which the ICO is aware of and which is a factor in this agentic AI call for evidence.
Why the ICO is acting now
The ICO is seeking to support responsible innovation. It has previously contributed to an agentic AI deep dive by the Digital Regulation Cooperation Forum (DRCF), as well as identifying the need to provide greater regulatory certainty to AI developers and deployers on agentic AI.
As many of us are, it is also aware of recent agentic AI incidents concerning foundation model providers, such as the widely covered incident where OpenAI agents escaped a testing sandbox to infiltrate Hugging Face. The ICO has already made enquiries with OpenAI, Anthropic, Meta and the UK's AI Security Institute around recent agentic AI testing.
The feedback provided in respect of this call for evidence will also inform the ICO's forthcoming statutory code of practice on AI and automated decision-making (ADM).
The six themes
The initial analysis conducted by the ICO of agentic AI systems identified six key areas where organisations face challenges in implementing a 'data protection by design and by default' approach.
The six themes identified provide a helpful framework for stress-testing existing and proposed agentic AI deployments against the ICO's current thinking. For example, do you know what data your agents can access? Can a clear lawful basis be identified for all stages of the workflow? Has meaningful transparency been provided to individuals?
The themes are as follows:
Agentic AI systems may retrieve information from external systems and update other systems without approval being required at each step. This increases the risk that security failures, or malicious instructions, can spread through connectors and data sources.
Organisations must:
- Assess security risks arising from the system's capabilities, connections and data processing activities
- Implement technical and organisational measures that provide an appropriate level of security.
The call for evidence seeks to understand how effective common measures are in practice, and how stakeholders evaluate security risks in general.
Agentic AI systems can collect personal information from a wide range of sources while working on a task. This means that organisations deploying them need to document and communicate what agents can access, what they can do with that information, and who is responsible at each stage of processing.
Organisations must:
- Design transparency into agentic AI systems
- Ensure people receive relevant, timely information about how the organisation will process personal information
- Provide clear and comprehensive information about the purposes of storing information on a person's device (where appliable under the PECR)
- Put in place appropriate technical and organisational measures throughout the supply chain
The call for evidence seeks to understand how organisations provide meaningful transparency to individuals, including in multi-agent systems where this challenge is likely to be greater.
Agentic AI systems can involve multiple organisations, models, agents and tools. This makes it challenging to identify who determines the scope and purpose of every processing activity, and who is a controller under the UK GDPR, given that an organisation's level of influence over processing activities can change depending on the workflow.
The call for evidence seeks to understand how organisations allocate accountability in practice, how agentic actions are tracked, and how the system's operation is made clear to deployers.
Agentic AI systems may be used as part of decision-making processes that have significant effects on people in a manner that falls in scope of Article 22 of the UK GDPR. This includes circumstances where a decision results from a mix of automated and human decisions.
Organisations must assess whether the ADM provisions within Article 22 of the UK GDPR apply across the agentic AI system's operation.
The call for evidence seeks to understand how ADM provisions are applied, and how safeguards are implemented in the context of agentic AI.
An agentic AI system may utilise its memory gained during one task to support another task, but this does not mean that the use for an additional purpose is compatible with the original purpose for collecting that information.
Organisations must:
- Assess whether use of personal information is reasonably expected in specific contexts
- Identify potential adverse effects of data processing by agentic AI systems on people
The call for evidence seeks to understand measures in place that support compliance with the fairness and purpose limitation principles.
Agentic AI systems involve multiple data processing activities, which can make it difficult to identify appropriate lawful bases across different stages of an agentic workflow. In particular, there are complexities in relying on contract or legitimate interests as a lawful basis, which may apply but should not be assumed by default.
Organisations must:
- Identify an appropriate lawful basis for processing personal data
- Ensure any consent sought meets the required threshold
The call for evidence seeks to understand commonly used lawful bases for agentic AI development and deployment, and how these are selected and evaluated.
What happens next?
The dynamic nature of agentic AI raises important questions about how to comply with data protection requirements, which organisations across the UK are working to understand. Organisations are already working hard to implement governance and technical solutions to comply with legal requirements when developing and deploying agentic AI systems.
The evidence gathered will be used to develop guidance that helps organisations in the UK understand their regulatory obligations in respect of agentic AI, as well as informing the statutory code of practice on AI and ADM. The call for evidence provides a helpful opportunity for organisations to demonstrate the value of agentic AI systems, as well as to share compliance challenges directly with the ICO, to inform what guidance and support is provided by the regulator.
Responses can be submitted until the end of 20 November 2026. If you would like to discuss whether and how to respond to the call for evidence, or to review your existing agentic AI arrangements, please do get in touch.
This publication is intended for general guidance and represents our understanding of the relevant law and practice as at October 2026. For more information see our terms & conditions.
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