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Prepare data for AI agents by making it authoritative, understandable, appropriately fresh, permission-aware, and testable for the work the agent will do. Chunking documents and generating embeddings may help an agent find information, but those steps alone do not establish whether the information is accurate, current, safe to expose, or useful for a real task.
A practical preparation process starts with the agent’s questions and actions, maps each data domain to an authoritative source, improves the data and its context, selects a retrieval method for each domain, enforces access controls, and evaluates the complete workflow. The right design depends on the data, freshness needs, security obligations, and operational capacity—not on a single universal architecture.
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What does “ready for an AI agent” mean?
Data is ready when an agent can find and interpret the right information, use it within the requester’s permissions, recognize its source and freshness, and return a result that can be checked. Readiness is therefore a lifecycle spanning source selection, data quality, business meaning, retrieval, security, maintenance, and evaluation.
This matters because an agent may retrieve a plausible but outdated policy, misread a field whose meaning is undocumented, or expose a record to a user who should not see it. In an agent that can take actions, errors can also affect downstream systems. The Australian Government Digital Transformation Agency’s Agentic AI Addendum statements: Data calls data readiness and exfiltration a mandatory prerequisite for agentic AI systems. Its guidance also emphasizes authenticated, encrypted, auditable data flows, with requirements applied according to the relevant jurisdiction and system.
How should you prepare data for an AI agent?
1. Define the agent’s job and identify authoritative sources
Start with the questions the agent must answer and the actions it may take. Then inventory the data domains those tasks require. For each domain, record the authoritative system, accountable owner, permitted users, update cadence, and whether the agent needs reference material, current operational facts, or both.
Do not assume that the easiest source to connect is the source of truth. If multiple systems contain the same fact, decide which one governs and how conflicts should be handled. Microsoft Learn’s guidance on data architecture for AI agents recommends documenting retrieval choices—such as search, APIs, or both—by domain, as part of a governed organization-wide data platform.
2. Profile, clean, and add context
Inspect each source for its format, coverage, duplicates, missing or inconsistent values, and update behavior. Use deterministic validation or normalization where rules are clear. Preserve meaningful distinctions rather than flattening them away: for example, a status code should not be standardized without confirming whether its values mean the same thing across systems.
Add the context an agent needs to interpret and filter information. Useful metadata can include source, owner, dates, business unit, classification, and lineage. For structured data, document table and field meanings, relationships, units, and known limitations. For documents, retain useful identifiers and dates and ensure that extracted text remains connected to its original source.
OpenAI’s account of its in-house data agent illustrates why enrichment can extend beyond indexing raw content: its context combines table usage, human descriptions, code-derived information, institutional knowledge, and runtime inspection. That is an implementation example, not a required recipe for every organization.
3. Choose retrieval for each domain
Use the retrieval method that fits the data’s shape, freshness requirement, and task. An indexed search collection can be effective for reference material; a live API or warehouse query can be more suitable for rapidly changing facts or transactions. A hybrid design can use indexed context to explain a domain while retrieving current values from the system that owns them.
| Data or task need | Possible retrieval route | Key trade-off |
|---|---|---|
| Policies, guides, and other searchable reference content | Ingest and index content for retrieval-augmented generation (RAG). | Search can return relevant passages, but results depend on source quality, parsing, metadata, indexing, and refresh timing. |
| Fast-changing operational or transactional facts | Query a live API, warehouse, or other authoritative system. | Can provide current records, but requires a secure, reliable integration and suitable query controls. |
| Tasks that need both explanations and current facts | Combine indexed reference content with live retrieval. | Offers different routes for different information, while adding integration and evaluation work. |
Google Cloud’s RAG reference architecture describes a common indexed-content flow: ingest source files, produce metadata, parse and chunk content, generate embeddings, and maintain an index. At serving time, the system embeds a query, searches the indexed data, supplies retrieved context, and generates a response. These are implementation steps, not proof that every agent needs embeddings or that an index is the right source for current operational facts.
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4. Apply permissions and security before and during retrieval
Classify data before it is ingested, and establish handling rules for sensitive, regulated, or restricted content. Preserve least privilege throughout the flow: a document or record available to one user must not become available to every user merely because it was indexed. Retrieval must apply the requester’s permissions, not just return the best semantic match.
Plan for malicious or misleading content as well as accidental exposure. AWS Prescriptive Guidance warns that RAG workloads face risks including data exfiltration and indirect prompt injection through malicious documents. It recommends input validation or content filtering before ingestion and discusses encryption, provenance tracking, and metadata filtering. The application or agent must supply correct filter metadata to API calls; having filters available does not by itself guarantee that the right restrictions are applied.
Microsoft Learn says Microsoft 365 agents retrieve content while enforcing existing permissions, sensitivity labels, and tenant policies. That describes the Microsoft 365 context; organizations using other systems need to verify how their own identity, document-level permissions, classifications, audit records, and data residency requirements are enforced.
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Record where information came from, who owns it, when it was last updated, and what transformations were applied. Set a refresh cadence that reflects the source’s change rate and define what the agent should do when data is stale or unavailable. For time-sensitive answers, the agent may need to check the live source or state that it cannot verify the latest value rather than present an old index result as current.
AWS guidance identifies lineage and provenance as useful for compliance, troubleshooting, security investigations, data quality, and impact analysis. OpenAI’s data-agent example pairs daily enrichment and indexing with live table inspection when prior context is missing or stale. Together, these examples show why refresh schedules and retrieval-time checks should be designed around actual use rather than assumed from the presence of an index.
6. Evaluate the complete agent workflow
Build a representative set of questions and expected answers or outcomes before relying on the agent. Include routine requests, ambiguous cases, stale or conflicting information, permission boundaries, and requests that should not be answered or acted on. Check not only the final wording but also whether the agent retrieved the right source, used correct data, respected access rules, and took the intended action.
OpenAI describes using curated question-and-answer pairs and manually authored “golden” SQL to assess its data agent. Its comparison considers generated SQL and returned data rather than relying on string matching alone. This is one first-party evaluation example, not a universally validated standard. The general lesson is to measure the result that matters for the task and inspect retrieval and execution traces when something fails.
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How do you choose an architecture?
Compare options against the requirements of each data domain rather than assuming that one vendor’s recommended pattern is an industry-wide rule. Microsoft recommends built-in retrieval as a default when it meets accuracy and compliance needs; AWS and Google document specific service architectures and alternatives. These are useful implementation references, not independent comparative benchmarks.
- Freshness: Decide whether a periodic index refresh is adequate or whether the task needs direct, live retrieval.
- Governance: Verify identity passthrough, document-level permissions, classification, auditability, and any data residency requirements.
- Data shape: Account for structured records, documents, scanned files, images, and other modalities in scope.
- Query and action needs: Distinguish direct lookup and semantic search from multi-step retrieval or an action through an authenticated API.
- Control and operations: Weigh managed ingestion and indexing against the control—and operational responsibility—of a custom pipeline and self-managed vector store.
- Evaluation: Check whether teams can inspect source citations, retrieval traces, data correctness, and regressions over time.
The cited guidance was accessed October 7, 2026. It provides vendor architecture and product guidance plus Australian Government policy guidance, not a head-to-head performance study. No directly comparable statistic establishing how much data preparation improves agent accuracy is established by these sources, so a universal accuracy percentage would be misleading.
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