As AI moves from generating content to taking action, organizations need to decide what evidence those actions leave behind.  

For the past few years, much of the conversation around artificial intelligence has focused on what AI can create. A chatbot writes an email. A model summarizes a report. A system extracts information from thousands of documents. 

AI agents change the question. Instead of waiting for a person to act on an output, an agent can be given a goal, connect to tools and systems, and complete a series of tasks on a user’s behalf. The NIST AI Agent Standards Initiative describes agents as capable of autonomous actions and is examining areas including identity, authorization, security and interoperability. 

For information managers, that shift creates a practical challenge. If an AI agent takes an action, what evidence should the organization retain so that it can understand and explain what happened later? 

“The final output may show what happened. It may not show why it happened, what information was used or who authorized it.” 

From producing content to changing systems

An AI-generated summary is relatively easy to recognize as an output. An agentic workflow can be more complicated. An agent might retrieve a contract, compare it with a request, update a case-management system, route a document for approval and send a response. Each step may depend on different information and permissions. 

This is already moving from theory into operational use. NIST has described work on an AI agent enrichment workflow for the National Vulnerability Database, intended to support the enrichment of vulnerability information. That example is specialized, but the underlying pattern is relevant across sectors: agents can gather information, use tools and contribute to live business processes. 

The resulting business record may be more than the document or system entry at the end. An organization may also need enough context to establish which agent acted, what task it received, which sources it used, what tools it called, what changes it made and whether a person approved or overrode the result. 

What might need to become part of the record?

The answer will vary by process, risk and jurisdiction. A low-impact administrative task will not require the same evidence as an action affecting a payment, contract, employee or customer. Still, several categories are worth considering: 

• The instruction, goal or workflow rule assigned to the agent. 

• The source documents, records or data used to support the action. 

• The agent identity, version and permissions in place at the time. 

• Tool calls, system updates and other actions completed during the workflow. 

• Human approvals, interventions, exceptions or overrides. 

• The final output, transaction or record created. 

• Relevant activity logs needed to reconstruct the sequence later. 

This does not mean every prompt, intermediate step and technical log should be retained forever. Keeping everything can increase cost, make retrieval harder and leave the organization holding information with no clear business value. 

The better approach is familiar records-management work: define what constitutes evidence, establish the authoritative record, apply an appropriate retention period, control access and make sure the information can be found when needed. 

“Good AI recordkeeping is not about keeping everything. It is about preserving enough reliable evidence to reconstruct a significant action.” 

Think about the questions that arrive later

Information-management gaps often become visible only when someone needs an answer. An auditor asks how an approval was reached. A customer challenges an outcome. A process owner wants to understand why an exception was handled differently. A legal or compliance team needs the record supporting a system change. 

At that point, the final output may not be enough. A complete file might need to connect the result with its supporting sources, approval history and relevant activity. If those elements sit in separate applications, use inconsistent identifiers or disappear under different retention rules, reconstructing the event can become difficult. 

Organizations should therefore involve records, information, risk and process owners before agentic workflows scale. They can decide which actions are significant, what evidence is required, where it will be stored and who should be able to retrieve it. This is not a reason to slow adoption. It is a way to make adoption more dependable. 

Start with the workflow, not the technology

A useful starting point is to map one agent-enabled process from beginning to end. Identify the systems the agent can access, the information it reads, the actions it can take and the points where human judgment remains necessary. 

Then ask a simple set of questions. If this action were reviewed six months from now, what would we need to show? Which item is the authoritative record? Can the supporting evidence be linked to it? Are access and retention rules consistent across the systems involved? Can a person retrieve the full sequence without relying on the AI tool itself? 

These questions help separate information with lasting business value from short-lived technical data. They also expose practical gaps, such as an approval stored in one platform while the source material and activity history are held elsewhere. 

HOW WE CAN HELP 

Build an information layer around AI-enabled work 

• Capture: the records and supporting evidence a workflow needs to preserve.
• Classify: outputs, approvals and activity records consistently across systems.
• Retain: information according to business, regulatory and operational requirements.
• Control access: so sensitive records are available to the right people.
• Retrieve: the evidence needed to understand or reconstruct an action.
• Connect workflows: through digital information management and managed document processes.

Make agent activity part of the information estate

AI-generated and agent-related records should not become another unmanaged pool of data. They need to sit within the same information strategy as email, contracts, case files, operational systems, scanned documents and physical records. 

That means treating agent-enabled work as part of the information lifecycle from the outset. Decide what must be captured, how it will be classified, how long it should remain available and how it will eventually be disposed of. Where the same process spans physical and digital information, the links between those records also need to remain clear. 

AI agents may change how decisions and routine actions are carried out. They do not remove the need for reliable evidence. In many cases, they make that need more important. 

The organizations best placed to use agentic AI will not simply be those with the most advanced tools. They will be the ones that can understand what their agents did, locate the supporting information and explain the result when it matters. 

Talk to us about making the information around AI-enabled work organized, controlled and usable. We’ve been managing physical and digital information for decades, so we sit on top of this layer, not AI design itself.