AI Agent Compliance

An AI chatbot answers a question. The DPIA writes itself. The data flow is one direction: user → server → LLM → server → user. You can document it on a single page.

An AI agent is different. The agent reads an email, decides whether to schedule a meeting, calls your calendar API, drafts a reply, and updates your CRM. It uses tools. It chains decisions. It accesses systems your DPIA never considered.

The compliance questions multiply:

• Who is the data controller when an autonomous agent makes a decision?
• How do you DPIA a system that adapts its behaviour based on past interactions?
• What does Article 22 (automated decision-making) require when the agent escalates to a human after 3 failed attempts?
• How do you audit a tool-using LLM when each tool has its own data flow?
• What's the lawful basis when an agent reads private data to decide whether to take an action?
• How do you handle a data subject access request when the agent's memory is stored across vector databases, conversation logs, and tool-call histories?

The current DPIA template doesn't cover any of this. The current privacy notice says nothing useful. The vendor due diligence checklist doesn't ask the right questions about tool-use boundaries.

That's the work I'm focused on. New patterns for new systems.

Singapore's IMDA published the world's first national framework for agentic AI in January 2026; it remains open for public feedback. The work covered here aligns directly with three of its four governance dimensions: risk bounding, human accountability checkpoints, and technical controls throughout the agent lifecycle. Organisations working through the IMDA framework consultation can use this practice as the operational counterpart. (IMDA framework.)