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When an AI agent makes a consequential decision, a useful explanation needs more than the agent’s final answer. It should let an organization reconstruct what information was available, which model and rules shaped the decision, and what happened afterward. Allard Buijze, Axoniq’s founder and CTO, argues that conventional operational systems may not preserve that history; his proposed remedy is a durable, traceable record of decisions and their effects.
What a system of record for AI decisions needs to show
Buijze’s framework separates a decision record into three parts. Together, they help answer not just what an agent decided, but how the decision arose and what it changed.
- Historical inputs: Preserve the information as it was available to the agent at decision time. A reference to a customer profile is not enough if the profile can later change; the record should capture the relevant values or point to an immutable version.
- Decision context: Record the conclusion and the configuration that shaped it, such as the model, prompt, policy, and tools involved. This is Buijze’s architectural recommendation, not a universally agreed definition of explainability.
- Downstream effects: Link the decision to the actions or outcomes that followed, so a later reviewer can trace from a decision to its consequences—and from an effect back to the decision behind it.
A model’s explanation of its conclusion may still leave gaps if the organization cannot recover the original inputs or identify the resulting actions. The aim of the broader record is historical traceability, not simply a persuasive account of why the model produced an answer.
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Buijze’s recommendations can be turned into practical design checks. They are architectural guidance from a vendor-associated opinion article, not independently validated performance findings.
- Test the action-record boundary: Simulate a failure after an action succeeds but before its record is written. If the business action and its evidence can diverge, the organization may end up with conflicting histories. Consider how the action and record can be committed together or reconciled reliably.
- Test historical reconstruction: Change a referenced profile or other mutable record, then check whether the original values used for a past decision can still be recovered. Capture those values or retain an immutable version when needed.
- Test causal tracing: Use correlation and causation identifiers to follow an outcome back to the decision that produced it, and to follow a decision forward to its effects.
- Test the record’s useful life: Set retention to match the period during which questions may arise, and check whether domain experts can understand entries without relying on the original system’s developers.
- Test replay carefully: Determine whether historical events can be replayed through corrected logic to examine a changed interpretation. Replay can support analysis, but it should not silently repeat real-world actions.
Event sourcing or a companion decision log?
Buijze presents event sourcing as one option, not a requirement for every organization. The key distinction is whether the event record is itself authoritative for system state or supplements an existing source of truth.
| Design question | Event sourcing | Companion log |
|---|---|---|
| Where does authoritative state live? | The event sequence is the source from which state is derived. | An existing operational system remains authoritative; the log records decisions alongside it. |
| How are actions and records connected? | Decision-related events form part of the state history, though the implementation still needs to handle reliable recording. | The design must prevent or reconcile cases where an action succeeds but the companion record does not. |
| Can historical inputs be reconstructed? | Only if the relevant historical values or versions are represented in the event history. | Only if the log captures those values or points to an immutable version; mutable references alone may fail. |
| Can effects be followed? | Events can provide a history, with identifiers needed to connect decisions and consequences. | The companion record needs explicit causal links to the operational changes it documents. |
| What is the trade-off? | It makes the event history central to the system’s state model. | It can fit alongside existing systems, but the relationship between each action and its record must be dependable. |
Neither approach is automatically superior. The choice depends on the existing architecture, whether the organization is prepared to make events authoritative, and the level of historical reconstruction and traceability required.
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What Article 12 of the EU AI Act requires—and what it does not
Article 12(1) of the EU AI Act says: “High-risk AI systems shall technically allow for the automatic recording of events (logs) over the lifetime of the system.” Article 12(2) describes relevant logging purposes, including identifying risk situations or substantial modifications, facilitating post-market monitoring, and monitoring operation under Article 26(5). The EUR-Lex consolidated regulation identifies its version as updated 27 July 2026.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThis provision applies to high-risk AI systems; it should not be presented as a logging mandate for every AI system or agent. Nor does the text quoted here require every organization to retain every prompt, complete input snapshot, chain of reasoning, and downstream business effect. Those broader elements are part of Buijze’s proposed architecture, not a blanket statement of Article 12’s requirements. Article 12 also sets additional minimum logging details for the specified remote biometric identification systems in Annex III, point 1(a).
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Where to begin with implementation
Buijze recommends starting with decisions already delegated to agents and prioritizing them by who might later ask about them and when. That is implementation advice from the article’s author, not a regulator-prescribed process.
- Inventory delegated decisions. Identify decisions an agent can make or trigger, then note the actions and systems that may be affected.
- Prioritize the questions that matter. For each decision, ask who may need to review it and how long afterward that question could arise.
- Define the minimum record. Specify the historical inputs, model and configuration context, and outcome links needed to answer those questions.
- Choose the record’s role. Decide whether the event sequence will be authoritative or whether a companion log will sit beside an existing source of truth.
- Exercise failure and recovery. Test gaps between action and recording, changes to referenced data, and the path from decision to outcome. Confirm that the record remains readable and retained for the needed period.
The article is a vendor-associated opinion piece by Buijze, who discusses event sourcing and Axon Framework. Its architectural prescriptions should be read as an argument for durable decision traceability, not as an independent comparison of implementations or products.
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