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Preparing data for AI agents means making the right information discoverable, understandable, current enough for the task, safe for each user to access, and testable in the complete agent workflow. Chunking and embeddings may help search documents, but they do not establish whether the sources are authoritative, the data is accurate, permissions are respected, or an answer is still current.
Start with the agent’s job and the authoritative sources
Define the questions the agent should answer and the actions it may take before choosing an ingestion or retrieval design. A support agent looking up policy, for example, has different data needs from an agent that checks live order status or updates a record.
For each data domain, establish:
- Purpose: the questions or actions the agent is expected to handle.
- Authority: which system is the source of truth for each fact.
- Ownership: who is responsible for definitions, quality, and access decisions.
- Audience: which users or roles may see or act on the information.
- Change pattern: how often the information changes and how quickly a change must reach the agent.
Microsoft Learn’s guidance on data architecture for AI agents recommends documenting the retrieval method for each domain, such as search, APIs, or both. That makes a useful design artifact: it connects the agent’s purpose to the source, access rules, and retrieval path instead of treating every company file as a single index.
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Profile, clean, and explain the data
Inventory the actual content and inspect it before making it retrievable. Check file and record formats, coverage, duplicates, missing values, inconsistent units or labels, and whether dates and other key fields mean the same thing across systems. Apply deterministic validation or normalization where the rules are clear; send ambiguous cases to a data owner rather than silently guessing.
Raw values often need business context. A table name or field name may be obvious to its creator but opaque to an agent. Add descriptions of what tables and columns represent, how they are used, relevant business terminology, and any important caveats. Useful metadata can include source system, record or document date, owner, business unit, classification, and update cadence. Include lineage—the source and transformations behind the content—so a result can be traced back and corrected.
OpenAI’s account of its in-house data agent describes combining table usage, human-written descriptions, code-derived context, institutional knowledge, and runtime inspection. It also describes daily enrichment and indexing alongside live data access when stored context is missing or stale. This is an implementation example, not a rule that every organization needs the same layers or schedule.
Choose retrieval to fit each data domain
There is no single retrieval route that suits all company data. The key choice is whether the agent can use a periodically updated index, needs to read a live system, or benefits from both.
| Data need | Possible route | Main consideration |
|---|---|---|
| Policies, manuals, and other reference documents | Search or retrieval-augmented generation (RAG) over an indexed collection | Parsing, metadata, chunk boundaries, and index refresh determine what content can be found and how current it is. |
| Frequently changing operational records | Authenticated live API or warehouse query | Use a live route when an index refresh interval could leave the agent with outdated facts; enforce access and validate the returned data. |
| Questions needing both background and current facts | Hybrid retrieval: indexed context plus a live query | Make clear which source supplies each part of the answer and what to do if the live system is unavailable. |
| Actions that change business records | Authenticated API or other controlled action interface | Retrieval alone is not authorization to act; validate inputs and apply the system’s action permissions and safeguards. |
Google Cloud’s RAG reference architecture describes a common document path: ingest source files, create metadata, parse and chunk content, generate embeddings, and maintain an index. At answer time, the query is embedded, relevant indexed material is retrieved, and that context is passed to the model. This can support searchable reference collections, but it does not make the underlying content authoritative or resolve freshness and permission requirements by itself.
For structured or fast-changing information, a live warehouse query or API may be more suitable than waiting for an index refresh. Microsoft recommends documenting the route by domain, and OpenAI’s example combines indexed context with live warehouse access. Evaluate a managed retrieval service against a custom pipeline based on control, operational capacity, connectors, compliance needs, and regional availability. Amazon Bedrock documentation describes both managed and customer-managed knowledge-base approaches; the features and regions described by service documentation can change.
Apply permissions and safety controls before content is indexed
Decide which data is suitable for the agent before ingestion. Apply classification and handling rules, exclude content that should not be available, and preserve least-privilege access through retrieval. If access varies by user, the retrieval layer must enforce those distinctions; a broad index followed by a prompt asking the model to hide restricted material is not an access-control system.
Plan for malicious or misleading content as well as ordinary data quality issues. AWS Prescriptive Guidance identifies data exfiltration and indirect prompt injection through RAG sources as risks, and recommends input validation or content filtering before ingestion. Its guidance also describes encryption, provenance tracking, and metadata filtering. Metadata filters are only effective when the application supplies correct filter values to retrieval calls.
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Microsoft says Microsoft 365 agents retrieve content while enforcing existing permissions, sensitivity labels, and tenant policies. That behavior is specific to the documented Microsoft 365 agent context; verify the guarantees of whichever platform and connectors you use. The Australian Government Digital Transformation Agency’s agentic AI data guidance calls for assessing readiness, quality, governance, and security in light of the system’s autonomy, and for authenticated, encrypted, auditable data flows. Organizations should also apply the policies and legal requirements that govern their own jurisdiction and data.
Set freshness rules and preserve provenance
For each source, define how often it is refreshed and what the agent should do when information may be stale. That could mean using a live query, stating the last-updated date, declining to give a current-status answer, or escalating to a person. The right behavior depends on the consequence of an outdated answer; a static policy reference and a current account balance do not have the same freshness needs.
Retain source ownership, transformation history, and last-updated information where they can be inspected. AWS guidance identifies lineage and provenance as useful for compliance, troubleshooting, security investigations, data-quality work, and impact analysis. These records also help diagnose whether a wrong answer came from the source, a transformation, retrieval, or the agent’s use of retrieved context.
Evaluate the complete agent workflow
Test more than whether a document can be retrieved or an answer sounds plausible. Build representative questions from the agent’s intended work and record expected answers, sources, or action outcomes. Include common cases, ambiguous questions, missing information, stale records, permission boundaries, and cases where the correct response is to abstain or ask for clarification.
OpenAI’s in-house data-agent example uses curated question-and-answer pairs and manually authored “golden” SQL. It compares generated SQL and returned data rather than relying on text-string matching alone. For a data agent, that distinction matters: two queries can be written differently yet return the same correct result, while a fluent explanation can conceal incorrect data.
- Check whether the agent found the right source and whether the retrieved material supports its answer.
- Verify factual values or query results against the authoritative system.
- Test that users cannot retrieve content outside their permissions.
- For actions, verify both the action’s authorization and its resulting state.
- Record regressions over time as sources, retrieval settings, or agent behavior change.
The example is a first-party implementation, not a universally validated evaluation standard. Adapt evaluation criteria to the domain, user risk, and whether the agent answers questions or changes data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use requirements—not vendor defaults—to make the architecture choice
Compare the design against the agent’s actual needs. The reviewed guidance from Microsoft, AWS, and Google describes implementation options, not an independent benchmark establishing one universally superior architecture. Microsoft recommends built-in retrieval when it meets accuracy and compliance requirements; that recommendation should be tested against the organization’s own requirements.
- Freshness: Can periodic refresh meet the need, or must the agent query live data?
- Governance: Are identity passthrough, document-level permissions, classification, auditability, and data-residency requirements covered?
- Data shape: Does the content include structured records, documents, scanned files, images, or other modalities that need different handling?
- Operations: Does a managed ingestion and indexing service meet the need, or is the control of a custom pipeline worth operating its components?
- Query and action pattern: Does the task call for direct lookup, semantic search, multi-step retrieval, or an authenticated action API?
- Evaluation: Can the team inspect citations, retrieval traces, data correctness, and regressions?
Choose separately for each domain when necessary. A company may reasonably index durable reference material while querying a live system for changing operational facts, provided the agent can distinguish those sources and the security model remains consistent.
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A readiness checklist before launch
- The agent’s supported questions and actions are defined.
- Every data domain has an authoritative source, accountable owner, audience, and freshness expectation.
- Data quality issues and business meanings are documented or corrected.
- The retrieval route is selected per domain and fits the data’s shape and update rate.
- Classification, permissions, ingestion screening, and audit requirements are implemented.
- Provenance and last-updated details are available for troubleshooting and user trust.
- Representative tests cover answer correctness, retrieval, access boundaries, stale data, and action outcomes.
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