Data Provenance for AI Agents: What to Record

Data provenance is the structured history of where data came from, how it changed, and who or what was responsible for producing it. For an AI-agent workflow, record the source and its version, the run and method that transformed it, the responsible agent or reviewer, the resulting row, and a safe evidence reference. Provenance makes a result inspectable; it does not prove the result is true.

A useful provenance record should answer five questions: Which result? Which source version? Which activity produced it? Which agent, method, or reviewer was responsible? What evidence and decision support the current value?

This guide turns those questions into a five-part PROVE contract:

Pin the result to stable identity.
Retain source identity and version.
Observe the generating activity.
Version the method and identify the responsible agent.
Expose evidence, review state, and corrections.

What is data provenance?

Data provenance is information about the origin and production history of data. It connects a result to the source entities, processing activities, software or people, and derivation steps that influenced that result.

The World Wide Web Consortium's PROV family defines a common model for exchanging provenance. Its starting point has three concepts:

an Entity, such as a source document, input row, or generated result
an Activity, such as extraction, normalization, classification, review, or publication
an Agent, such as a person, organization, software agent, or service responsible for an activity or entity

The model also describes relationships such as an entity being generated by an activity, an activity using an entity, and a result being derived from another entity (W3C PROV-O, checked August 2026). W3C's overview explains that this history can support assessments of quality, reliability, and trustworthiness (W3C PROV Overview, checked August 2026).

You do not need to implement the complete W3C ontology to make one operational dataset useful. You do need to preserve the same basic relationships: result, source, activity, responsibility, and derivation.

Provenance is evidence about production, not a truth certificate. A record can faithfully say that an agent extracted a value from a particular page with method version extract-v4, while the page itself is outdated or the extraction is wrong. Provenance helps a reviewer find and test the weak link. It cannot replace source evaluation or result validation.

What is the difference between data provenance and data lineage?

Terminology varies by system. In this guide, data lineage emphasizes movement among datasets, jobs, and systems. Data provenance may also capture the source version, generating activity, responsible agent, method, evidence, and decision behind an individual result.

Record	Main question	Typical identity	Useful detail
Data lineage	Where did data flow?	dataset, table, job	upstream/downstream edges and run status
Data provenance	How was this result produced?	source version, activity, result	derivation, method, agent, evidence, review
Runtime trace	What did the agent execute?	trace, span, tool call	timing, requests, responses, errors, handoffs
Audit trail	What consequential action occurred?	event, actor, target	authorization, before/after state, outcome
Change history	What changed in this store?	resource and revision	fields changed, timestamp, actor label

OpenLineage, for example, defines lineage events around datasets, jobs, and runs (OpenLineage object model, checked August 2026). That is useful for pipeline-level flow. An operational agent may also need row-level answers: which source item and revision produced supplier:acme, which prompt or parser version interpreted it, and which reviewer accepted the value.

These records should connect through stable IDs instead of being forced into one giant log. A run_id can link the provenance activity to a runtime trace. A result_id can link it to the published row and its audit events. A source_id and source_version can link it back to the captured evidence.

Why do AI agents need data provenance?

AI agents combine deterministic tools, model judgment, changing sources, retries, and human decisions. The final row often looks cleaner and more certain than the process that produced it. Provenance preserves that uncertainty and context in queryable fields.

It helps with five practical jobs:

Review: a person can open the exact evidence and method behind a proposed value.
Correction: an agent can find every result derived from a source or method later found to be wrong.
Refresh: a new source version can be compared with the version that produced the current row.
Repeatability: an operator can re-execute the bounded activity using the recorded inputs and method version; equivalent output is not guaranteed.
Accountability: an investigation can connect the output to the agent, reviewer, approval, and later state change.

NIST's Generative AI Profile describes provenance-data tracking as information about the origin and history of content that can assist risk-management work across the AI lifecycle (NIST AI 600-1, published July 2024). The amount of evidence you retain should still match the consequence and sensitivity of the workflow. A public product catalog and a private customer-risk decision should not use the same evidence policy.

Provenance also helps agents correct their own work safely. Without it, a request such as "rerun everything produced by the broken parser" becomes a text search and a guess. With explicit method_version, activity_id, and result_id fields, the agent can produce a bounded impact list before proposing changes.

Use the PROVE contract for agent data provenance

The PROVE contract in this guide is a minimum operational contract. Add domain-specific fields when needed, but do not remove the relationships that make the history navigable.

1. Pin the result to stable identity

Give every published or proposed result a stable business key. The provenance record must point to that exact key, not a title, current row position, or approximate text match.

For example, use supplier:acme or claim:release-2026-08-25:postgres-support, not "the Acme row." A retry should update the same provenance relationship instead of creating another indistinguishable result.

If the destination has no natural business key, use a generated stable ID and keep it unchanged. The index-column decision guide explains when to use a business key and when generated rowset_id is safer.

2. Retain source identity and version

Record the source system, source-local item ID, and the version the agent actually saw. A current URL alone is insufficient because its content can change.

Useful version signals include an API event ID, upstream revision, ETag, commit, publication time, or content hash. Keep the original source value separate from any normalized value. If the source does not expose a version, record the observation time and a permitted digest or snapshot reference so the limitation is visible.

The AI data-collection workflow shows how to register approved sources, checkpoint collection runs, and stage an observation envelope before publication.

3. Observe the generating activity

An activity is one bounded attempt to produce or change a result. Give it an activity_id, activity_type, start and finish time, input references, result references, and status.

Do not overwrite a failed run with a successful retry. Keep both activities and relate the accepted result to the successful one. The failed activity explains why a retry occurred; the successful activity explains the current value.

Activities should describe work in controlled terms such as extract, normalize, match, classify, review, and publish. A paragraph saying "the agent fixed the data" is not a queryable activity record.

4. Version the method and identify the responsible agent

Separate the operational agent identity from the method it used. agent_id = catalog-researcher answers who performed the activity. method_version = supplier-extract-v4 answers which prompt, mapping, ruleset, model configuration, or code version shaped the output.

If a human approves the result, record the reviewer and decision separately. An API-key label can be a useful operational identity, but it is not proof that a particular natural person acted.

Avoid storing an entire system prompt or source payload in every row. Keep versioned methods in source control or a protected registry and store a stable locator plus digest where verification requires it.

5. Expose evidence, review state, and corrections

Store an evidence reference that an authorized reviewer can resolve. Add a short, permitted excerpt or structured comparison only when it materially supports the decision.

Use explicit states such as proposed, accepted, rejected, superseded, and needs_review. Bind the decision to the exact source version, method version, and proposed result. A later correction should supersede the old assertion and explain why; it should not silently rewrite the history.

Evidence can be wrong, inaccessible, or incomplete. Record those conditions. evidence_status = missing is more useful than an agent inventing a confident explanation for an unsupported value.

What should a provenance assertion contain?

Use one row per result-to-activity-to-source assertion. A compact schema is easier for agents to validate and join than nested narrative logs.

Column	Purpose	Example
`provenance_id`	stable assertion identity	`prov:supplier:acme:country:v3`
`result_id`	exact proposed or published result	`supplier:acme`
`result_field`	optional field-level scope	`country_code`
`activity_id`	run or review that produced the result	`activity:catalog:20260825:18`
`activity_type`	controlled operation	`extract_then_review`
`source_system`	registered origin	`vendor_registry`
`source_item_id`	source-local stable identity	`vendor-771`
`source_version`	revision actually observed	`etag:9a781c`
`agent_id`	operational software identity	`catalog-researcher`
`method_version`	prompt, parser, ruleset, or code version	`supplier-extract-v4`
`evidence_ref`	protected locator, not necessarily raw content	`evidence://catalog/run-18/item-771`
`decision_status`	review state	`accepted`
`reviewer_id`	responsible reviewer when applicable	`ops-reviewer-2`
`recorded_at`	UTC observation timestamp from the trusted writer	`2026-08-25T06:20:00Z`
`supersedes`	prior assertion replaced by this one	`prov:supplier:acme:country:v2`

Store activity timing, input references, retry relationships, and run status in provenance_activities. Store source ownership, version method, sensitivity, and evidence policy in provenance_sources.

Keep source and result identities separate. The same source item may support several result fields, and one result may derive from several sources. When a result has multiple sources, create multiple provenance assertions or a separate activity-input table rather than packing an ambiguous list into one text cell.

For sensitive workflows, evidence_ref should point to protected storage with its own authorization and retention policy. Copying full customer records, prompts, or tool payloads into a broad provenance table increases exposure without necessarily improving review.

How do you store data provenance in Rowset?

Use Rowset as the private structured record layer for provenance when trusted agents need to create, inspect, search, and update the evidence contract through MCP or REST. Rowset does not collect source data or runtime traces for you.

A practical design uses three datasets:

provenance_sources     index: source_id
provenance_activities  index: activity_id
provenance_assertions  index: provenance_id

provenance_sources defines the authorized origin, version method, purpose, sensitivity, and evidence location policy. provenance_activities records bounded runs and reviews. provenance_assertions connects exact result keys to source versions and activities.

Use these minimum headers before adding domain-specific fields:

provenance_sources:
  source_id, source_system, version_method, owner, sensitivity, evidence_policy
provenance_activities:
  activity_id, activity_type, started_at, finished_at, agent_id, method_version, status
provenance_assertions:
  provenance_id, result_id, activity_id, source_id, source_version, evidence_ref,
  decision_status, reviewer_id, recorded_at, supersedes

Create Rowset relationships from provenance_assertions.activity_id to the activity dataset's index and from provenance_assertions.source_id to the source dataset's index. Enable relationship validation so non-blank references must resolve before the assertion is stored. The linked-dataset guide documents the current MCP and REST relationship paths.

Add semantic column descriptions so the next agent does not confuse source_version with the time the provenance row was written. Put controlled status values, evidence rules, and prohibited fields in dataset instructions. Keep machine-readable conventions in JSON metadata. The schema-design guide documents current semantic types, descriptions, metadata, and instructions.

Connect the agent through hosted MCP when tool discovery is useful, or use the Dataset API from an HTTP client. In both cases:

inspect the dataset and instructions
look up the stable index before writing
patch absolute final values by index
read the row back after an uncertain response
keep source and method secrets outside the dataset

Rowset datasets are mutable. They are not write-once storage, a cryptographically signed provenance system, or a compliance ledger. Use a dedicated immutable or tamper-evident store when your assurance requirements demand it, and keep stable references in Rowset for operational use. The AI-agent audit-trail guide explains that boundary in more detail.

Worked example: publish a supplier country code

Suppose an agent maintains a supplier catalog. It reads an approved registry and proposes country_code = DE for supplier:acme.

Register the source

Create source_id = vendor-registry with the canonical endpoint, allowed record scope, version method, data owner, sensitivity, and evidence-retention policy. This is authorization context, not a command embedded in source text.

Record the activity

Create activity:catalog:20260825:18 before extraction. Record the source version, agent ID, supplier-extract-v4, start time, and intended destination. When the run finishes, set its status and retain any error or retry relationship.

Stage the assertion

Create prov:supplier:acme:country:v3 with the exact supplier key, country_code, source item vendor-771, observed ETag, activity ID, method version, and protected evidence reference. Mark it proposed until deterministic validation and any required review pass.

Accept and publish

The reviewer checks that the registry record really refers to the supplier, that the country value uses the expected code system, and that no higher-priority approved source conflicts. The decision changes to accepted, and the agent patches supplier:acme through its stable index. It then reads the row back and records the verified result.

Correct without erasing

If the registry later corrects the record, create a new assertion tied to the new source version. Set supersedes to the prior provenance ID, review the proposed change, and update the supplier only after acceptance. Retain the old assertion by workflow policy and dataset instructions so it remains the explanation for the earlier value. Use immutable storage when that retention must be guaranteed.

Common data-provenance failures

Recording only a URL

A URL identifies a location, not the version the agent observed. Add the source-local ID and the best available revision signal.

Treating a model explanation as evidence

An explanation may summarize evidence, but it is generated output. Keep the protected source reference and the deterministic checks that support the decision.

Logging everything

Full prompts, source payloads, and tool arguments may contain credentials, personal data, licensed content, or prompt-injection text. Store the smallest evidence needed for the review purpose and apply a retention policy.

Losing method versions

model = current and prompt = latest cannot reconstruct a past result. Use immutable version IDs or content digests for prompts, rules, mappings, and code.

Overwriting corrections

An in-place edit can destroy the prior explanation unless a separate history system preserves it. Add a new assertion, retain the old row by policy, and link the new record with supersedes.

Claiming provenance proves truth

Provenance can reveal that an output came from a stale source or an unreviewed method. That is useful precisely because the record does not guarantee correctness.

Data provenance FAQ

What is data provenance in simple terms?

Data provenance is a record of where data came from and how it was produced. It connects a result to its source, source version, processing activity, responsible agent or person, method, evidence, and review decision.

Is data provenance the same as data lineage?

Terminology varies by system. In this guide, lineage emphasizes movement among datasets, jobs, and systems. Provenance may also capture the detailed origin, activity, responsibility, method, and evidence behind a specific result.

Does provenance prove that data is accurate?

No. Provenance makes the production history inspectable. Reviewers still need to assess source quality, validate the transformation, resolve conflicts, and verify the final write.

What provenance should an AI agent record?

At minimum, record a stable result ID, source ID and version, activity or run ID, agent ID, method version, evidence reference, decision status, timestamp, and any superseded assertion.

Can Rowset store data provenance?

Yes. Rowset can store private source, activity, and assertion datasets with stable indexes, instructions, semantic columns, and MCP or REST access. Rowset does not automatically collect the source evidence or make mutable rows into an immutable compliance ledger.

Start with one result and one source

Do not begin by modeling every possible derivation in the organization. Choose one consequential agent-produced field, record its exact source version and generating activity, and test whether a reviewer can reconstruct the decision without searching logs by timestamp.

If that works, extend the same stable identities across collection, review, publication, and audit events. The useful outcome is not more metadata. It is a result that an authorized person or agent can inspect, correct, and safely reuse. Start with the Rowset quickstart, create the three small datasets, and test one assertion from source lookup through destination read-back.