How do I stop AI from using outdated company information?

Summary

• AI agents can produce confidently wrong answers when they rely on stale definitions, outdated metrics, or old business rules that no longer apply.
• Genie Ontology uses freshness signals alongside certification, usage, and expertise to rank which business definitions and data sources are most current and trustworthy.
• Automatically refreshed context ensures agents reflect your company's current state rather than consulting archived documents or historical queries.
• The ontology continuously learns from your queries, dashboards, and notebooks, so as your business changes, AI stays aligned with it.

How do I stop AI from using outdated company information?
AI agents are confident and convincing even when wrong. When an agent reaches for a stale definition of 'active customer', an old revenue calculation, or a metric that changed meaning three quarters ago, the answer sounds authoritative but steers decisions the wrong way. The deeper problem: general-purpose agents search every available data source and document equally, treating a 2022 spreadsheet the same as today's certified metric.
Genie Ontology addresses this by building a continuously refreshed layer of trusted, current business meaning. Rather than letting agents grope through all available data, it ranks potential sources using freshness alongside other authority signals like certification, usage patterns, and domain expertise. As your organization's definitions, metrics, and operating practices evolve, the ontology learns and updates automatically, so AI always draws from your current business understanding.

What is the data freshness problem for AI?

Three related issues compound this:
Stale semantic definitions. A metric called 'active_users' in a 2022 dashboard might count logged-in users, while a newer version counts paid subscribers. An agent that finds both will guess incorrectly and contaminate decisions.
Abandoned context. Old notebooks, archived analyses, and historical queries pile up in workspaces and remain searchable. An agent might excavate a query written three years ago that used outdated business logic or a deprecated data source.
Manual refresh cycles. Without automated learning, business teams must manually update semantic models and documentation to keep pace with real changes. Documentation drift is inevitable.

Key capabilities to evaluate

When choosing how to keep AI grounded in current information, look for these capabilities:

Capability	Why it matters
Freshness-aware ranking	Prioritizes recent, actively used definitions over older ones. Newer business logic should outrank archived calculations.
Continuous automatic learning	Learns from current queries, dashboards, and notebooks in real time rather than relying on manual updates. Keeps pace with how the business actually works.
Certification and ownership	Marks which definitions are official (e.g., Finance-certified revenue) versus ad-hoc. Prevents agents from confusing experimental queries with trusted sources.
Permission-aware context	Respects who authored and owns each definition. Ensures agents only suggest context appropriate to a user's role and access.
Live data computation	Agents query current data and recompute results rather than retrieving pre-summarized or cached answers. Reflects today's state.
Versioning and lineage	Tracks which definitions and metrics are related, when they changed, and why. Makes it clear when a source has been superseded.

How Databricks approaches this

Genie Ontology is a shared business understanding layer that combines explicit governance from Unity Catalog with knowledge automatically learned from your organization's work. It makes the current, authoritative context available to every AI agent.
Freshness as a ranking signal. Genie Ontology evaluates signals including certification status, how recently a metric has been used, expertise signals from who created it, and relationships to other trusted assets. Freshness is one of these authority signals. When an agent asks a question, the ontology retrieves and ranks relevant context, placing actively maintained, current definitions ahead of older or experimental ones.
Automatic, continuous learning. Rather than waiting for manual semantic updates, Genie Ontology learns continuously from queries, dashboards, SQL notebooks, and Lakeflow pipelines. It identifies recurring definitions, calculations, relationships, and business rules embedded in your daily work. When a metric changes meaning or a team shifts to a new data source, the ontology detects and incorporates that change.
Governed semantics from Unity Catalog. You can explicitly model your current business metrics using Metric Views, which define reusable calculations and dimensions. Certification, domains, and glossary terms mark which definitions are official. The ontology combines this explicit governance with learned context, giving you control over what's definitive while still capturing the long tail of business knowledge.
Live data reasoning. Genie Agents and connected AI tools reason over current data through SQL and Lakeflow Pipelines. Rather than summarizing historical snapshots, agents compute fresh answers from today's tables, reflecting your current business state.
Permission-aware retrieval. The ontology respects the underlying access policies in Unity Catalog. Each user sees context appropriate to their role, preventing accidental exposure of confidential definitions or data while ensuring agents can still ground answers in trustworthy sources.

Use cases

Accurate financial reporting. When revenue has multiple definitions (gross, net, recognized, accrued), agents need to know which applies to each question. Marketing asks total bookings; Finance asks recognized revenue. Genie Ontology ensures each agent uses the current, appropriate definition.
Operational alerting and dashboards. Metrics drift. What counted as 'customer retention' in 2023 may have changed. When a Genie Agent is summarizing performance trends, stale retention logic would misrepresent this quarter's story. Continuous learning keeps agent narratives aligned with current measurement.
High-stakes decisions. Board-level or pricing decisions shouldn't rely on outdated assumptions about how you calculate profitability or segment customers. Ontology-grounded agents provide decision-makers with answers that reflect today's business model and metrics.

FAQs

### How does Genie Ontology know what's outdated?
It tracks signals like whether a metric is actively used in dashboards and queries, certification status, who created it and when, and relationships to other definitions. Recently used, actively maintained definitions rank higher than unused or experimental ones.
### Can I explicitly mark a metric as outdated or deprecated?
Yes. Through Unity Catalog, you can certify metrics and mark them with ownership, descriptions, and deprecation notices. Genie Ontology integrates those signals. Agents prioritize certified, non-deprecated definitions.
### What if my data changes frequently but the semantic definition doesn't?
Genie Ontology computes fresh answers over live data. If you're tracking daily metrics, agents query today's rows, not cached results. The underlying semantic stays current through Metric Views, while the data itself is always up to date.
### How often does Genie Ontology refresh what it learns?
The ontology learns continuously as queries, dashboards, and notebooks run. Changes to metrics and definitions propagate to agents within the system's normal processing cycles, typically within minutes to hours depending on your refresh cadence.