The Tool Desk
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Why an AI agent can work in the demo but fail in production
A demo usually runs against a deliberately chosen set of sources and a limited workflow. Production brings a larger, messier environment: documents may be duplicated or out of date, authoritative answers may be split across systems, and users may have different rights to view or change information. If the agent cannot reach reliable sources—or cannot distinguish which version is authoritative—it may produce a confident but misleading answer.
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Data is not the only possible cause of a production problem. Tool integrations, permissions, workflow design, monitoring and human oversight also matter. The practical question is not simply whether the model answered well once, but whether the full system can find appropriate information and behave safely across the conditions it will encounter.
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Is your data ready for AI agents?
Start by mapping the answers and actions the agent is expected to handle to the data and systems it needs. For every kind of answer, name the system of record and the person or team accountable for it. Then decide how the agent will retrieve that information, how quickly updates must appear, and what permission and retention rules apply. Microsoft’s guidance emphasizes source quality and accessibility as readiness considerations. Microsoft Agent Readiness Framework
- Authority: Identify which system or document is definitive when sources conflict, and assign an owner who can resolve the conflict.
- Freshness: Set an acceptable update delay for each source and a way to detect stale or failed updates.
- Preparation: Look for duplicates, conflicting versions, sensitive fields, and documents that lack useful classification.
- Retrieval: Choose an integration or retrieval pattern that fits the source’s permissions, freshness, sensitivity and retention requirements. Prefer maintained official APIs or connectors where available, and verify the boundaries they enforce.
These checks are specific to the workflow. A source that is suitable for answering general policy questions may not be suitable for exposing individual records or triggering an external action.
How do you keep an AI agent from accessing data it should not see?
Limit the agent to the smallest data scope required for its task. If it acts on behalf of a user, pass that user’s identity securely and preserve the user’s permissions instead of giving the agent a broad service account that can see everything. Microsoft’s guidance discusses permission-aware access and identity in agent scenarios. Microsoft Agent Readiness Framework
Test boundaries with representative accounts, not just an administrator account. AWS recommends checking row-level security with at least two user accounts and removing sensitive columns from datasets rather than merely hiding them in an interface. AWS QuickSight embedded analytics best practices
- Verify that users with different access rights receive only the records they are allowed to see.
- Remove unnecessary sensitive columns from the data available to the agent; a hidden field may still be exposed through another route.
- Set document view, query and upload permissions separately, and classify documents so those permissions can be applied consistently.
- Re-run access tests when source data, connectors, identity handling or agent workflows change.
What controls does an agent need before it can act?
Reading information and taking action carry different risks. An agent that drafts a response is not equivalent to one that sends it, shares a file or changes an external system. For actions that transmit information or alter systems outside the agent’s immediate workspace, place a human approval checkpoint before execution. Keep audit records that show what information was accessed, what action was proposed or taken, and who approved it.
The Australian Government’s agentic AI addendum on data states: “Data readiness and exfiltration must be treated as a mandatory prerequisite for agentic AI systems, consistent with the AI technical standard.” Australian Government agentic AI addendum on data
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical production-readiness review
- Define the task. List the questions the agent must answer and the actions it may take. Separate read-only work from actions that send information or change another system.
- Assign sources and owners. For each answer type, name the authoritative source and the owner responsible for its accuracy and updates.
- Set retrieval requirements. Document freshness expectations, data quality needs, permissions, sensitivity and retention rules; select integrations that meet them.
- Minimize access. Grant only the data scope the task needs. Preserve user permissions when the agent acts for a user, and remove unnecessary sensitive fields from accessible datasets.
- Test realistic identities and content. Check access with multiple representative accounts, including cases that should be denied. Test for stale, duplicate and conflicting source material as well as expected answers.
- Control consequential actions. Require human approval before outbound or external changes, and keep logs suitable for review.
- Retest after changes. Recheck workflows when data, permissions, connectors or agent behavior is updated, and define who monitors problems and owns remediation.
Vendor checklists can help teams identify questions to ask, but they are not a certification that a particular deployment has been tested or is safe. Test your own data and workflows and include them in your organization’s security assessment.
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