Key Takeaways
- Without trusted metadata, your AI agents are just confidently guessing.
- Context is what separates a capable agent from a reliable one.
- Trusted data plus metadata context equals accurate, business-ready AI agents.
For most of the last two decades, metadata has been treated like a filing cabinet, one you update after the fact, if at all. The data continued to flow, the dashboards kept refreshing, but the metadata sat idle on a shelf. That arrangement left a ton of value on the table while humans were the ones making decisions, but in the age of AI it’s becoming open self-sabotage.
As AI agents are pushed into production across sales, finance, support, and operations, they’re being asked to make high-stakes decisions from coordinating supply chains and prioritizing workloads to approving refunds.
But roughly 90% of enterprise AI use cases end up stuck in the pilot stage, and close to eight in ten companies using gen AI are seeing no notable effect on revenue. Instead, they face broken data pipelines, integration failures, and poorly informed decision-making. The bottleneck is rarely the model, it’s enterprise readiness, and at the core of that is the context that lets AI agents understand what the data they’re acting on actually means.
“AI agents must operate using trusted data sources and trusted business meaning,” says Chris Kuang, Senior Product Marketing Manager at Boomi.
That’s because every call an agent makes rests on what the metadata tells them, and if that metadata is obsolete or insufficient, the agent resorts to making assumptions and responding with convincing hallucinations.
“Metadata has gone from a static descriptor to something that maps business meaning onto technical assets, layering in security and governance, data usage policies, and endorsement statuses. That ultimately increases agent accuracy and reduces hallucinations.”
The theory is simple to explain, harder to execute. The first step towards producing metadata agentic AI can trust is to switch from a mindset of viewing metadata as a passive description to seeing it as an active part of your infrastructure.
Is Your Metadata Strategy Still a Spreadsheet?
For most organizations, metadata still lives in wikis that nobody refreshes, shared drives that nobody indexes, and Confluence pages whose authors might have left the company years ago. This “documentation era” of metadata management created three persistent problems: the metadata is passive, it goes stale almost immediately, and it lives far apart from the work it is supposed to describe.
The price tag of this approach is not insignificant. Industry analysis puts the annual cost of poor data quality and redundancy at roughly $12.9 million per organization, and a large portion of that traces back to definitions trapped inside people’s heads or buried in documents nobody can find. This siloed knowledge tax is paid in duplicated work, slower delivery, and decisions made on outdated assumptions. It’s also likely to derail your AI initiatives before they even get started.
It’s easy to ask an AI agent to pull a list of “active customers”, but it can actually be quite difficult for the AI to know exactly what it is you’re looking for.
Three different teams might have their own views on what that term means: maybe NA sales applies it to any account generating more than $10K in annual recurring revenue, while for EMEA operations it’s any account with a transaction in the last 90 days, and APAC finance includes only customers with invoices paid in the current quarter. Each definition is correct inside its own team, but they’re also incompatible, and the agent has no way to know which one to go with. AI agents trained on this kind of fragmented context produce misleading answers that look authoritative and slip past review.
“If we don’t provide the agent with the context to understand what the data really means, or, given conflicting sources of truth, which one we want it to pull from, then agents will hallucinate or try to come up with a response built out of three conflicting terms,” explains Kuang.
Other downstream costs include duplicated effort, broken lineage, compliance drift, and longer onboarding cycles, all because rules are applied from different versions of the same definition.
As the race to adopt and harness AI gathers pace, organizations still running on wikis and shared drives will be left retrofitting under pressure. By 2027, 80% of agentic AI use cases will hinge on real-time, contextual, and ubiquitous data access.
What “Metadata as Infrastructure” Actually Means
If documentation is something you write, forget, and leave to become obsolete, infrastructure is something the system runs on, paying for itself every time it’s called on. So, how does that apply to metadata?
Metadata as infrastructure can be split into three working layers:
- The technical layer: The plumbing of schemas, APIs, data types, table relationships, and query logs. Most of this can be captured directly from the systems that produce it.
- The business layer: This is where the meaning lives in glossaries, KPIs, ownership, domain rules, and curated definitions that separate one team’s reading of a term from another’s. To be trustworthy, subject matter experts have to validate it.
- The operational layer: The execution surface that includes access controls, compliance enforcement, real-time lineage, and alerting. This is the ingredient that makes metadata active rather than referential.
Together they enable a system that knows what exists, understands what it means, and acts on that understanding.
While it’s a popular metaphor to explain metadata as the conductor of the data train, Kuang frames it as a division of labor between the data and the context that gives it meaning.
“Agents can run on data products alone, but they can’t run accurately without metadata,” Kuang says.
“Data Hub gives the agent the trusted record — the cleaned, matched data. Metadata is the onboarding and the policy manual on top of it: it tells the agent what ‘at-risk account’ actually means and which rule to apply. A capable agent with no context is just a confident new hire guessing — and that’s where hallucinations come from.”
Where passive metadata sits in a catalog and waits to be browsed, active metadata steps up and fires an alert when a schema changes, reclassifies data when a permission shifts, tags sensitive fields as they land, and updates lineage in real time rather than waiting for an audit to surface the discrepancy.
And when metadata lives where the work happens and can act on the world rather than just describe it, your organization can enjoy measurable results in developer velocity, compliance accuracy, and AI readiness.
Netflix and Spotify both embraced metadata as infrastructure under operational pressure. The global video streaming giant rebuilt its catalog into a governance and cost-reporting engine to handle GDPR and support self-serve data access for thousands of engineers, enhancing efficiency. Meanwhile, Spotify built Backstage and improved developer efficiency, clarity of dependencies, and reduced reliance on informal knowledge sources once metadata started appearing next to the code instead of sitting in a parallel wiki.
“When I think of metadata as infrastructure, I think of it as less of a static informational support layer and more part of the entire process for improving agentic accuracy,” Kuang says.
“Data that has been synchronized from different sources into Data Hub becomes the golden record. Those golden records are then cleaned, matched, and fed with context, which comes from metadata, and all of that golden-record information is then fed into Agentstudio, or into external AI agents. The result is improved agent accuracy and better business outcomes.”
The Context and Metadata Agentic AI Requires
A key reason for why metadata must move from descriptor to infrastructure comes down to one simple fact about AI agents: they don’t ask for help.
A human analyst who hits an ambiguous definition can easily ping the senior engineer on Slack, but an agent operating 24/7 might have no idea who to ping or how. It needs machine-readable, endorsed, current context at the exact moment it’s working, and if that context is missing, it doesn’t wait until it gets answers, it goes ahead and guesses, before confidently handing the answer downstream.
Boomi’s team has a name for what happens when an agent runs out of context: the “reasoning wall”.
Maybe an agent picks the wrong field as authoritative because no semantic definition is enforced at runtime, it joins two datasets that look compatible but have no governed relationship, or it invents transformation logic because the business rule lives in a document somewhere, but not in any metadata the agent can read.
“If agents don’t understand what the data means, or don’t have the most up-to-date instructions, they will give incorrect guidelines or operate incorrectly and autonomously,” Kuang says.
“They can easily break compliance rules or carry out unapproved actions.”
For instance, if the policy rules, lineage, and field-level meanings are not wired in as operational metadata, the agent might lean on a “refund_eligible” flag from a downstream dataset, miss the centralized policy that blocks the transaction, and issue an inappropriate refund.
“Another good example turned up in a side-by-side demo we ran of two churn-risk agents, one with context, one without,” Kuang explains.
“We took one at-risk account and ran both agents on it. The agent without the glossary fell back on the escalation rule baked into its own instructions — which hadn’t been refreshed in a year — and recommended scheduling a VP-level engagement.”
“But that rule was already out of date. Just the previous month we’d added a rule to the glossary: any at-risk account with a contract value over a million dollars goes to the SVP, not a VP. The hard-coded agent had no way of knowing the rule had changed.”
However, the second agent was grounded in Meta Hub. Pulling the same record from Data Hub, it saw the contract value was over a million, looked up the current rule in the glossary, confirmed it was endorsed and in effect, and escalated the account to the Senior VP — as the new policy requires. The only difference was whether the agent reasoned from a central, recently endorsed rule or from instructions hard-coded a year earlier.
“There are a lot of consequences of inaccurate agents, ranging from compliance issues to unsatisfied customers to lost revenue. With metadata, we provide the business context required to set better agent guardrails and prevent these undesirable actions.”
This solution to the “reasoning wall” effect is measurable: across 522 AI-generated SQL queries, adding semantic metadata delivered a 38% comparative increase in accuracy. The biggest gains showed up in medium-complexity questions involving joins and business rules, the workhorse queries analysts run every day, where accuracy more than doubled.
It’s interesting to note that the same study found that more metadata is not better: more verbose, human-readable documentation effectively gums up the works for the AI, leading to around a 14% decline in performance compared with a concise variant and costing 52% more to run.
Counterintuitive results like these help illustrate why metadata for agents has to be expertly designed to be concise and optimized for machine consumption. This is now what the industry calls context engineering.
The Infrastructure for Metadata Agentic AI Depends On
Kevin Petrie, Vice President and Head of Data Management Practice at BARC defines context engineering as:
“The discipline of ensuring that for any given task, GenAI/ML models and agents are provided with the precise, trustworthy, and permissible information they need to perform correctly and securely.”
But context engineering only works when the metadata underneath it is built for the job. Five requirements separate production-ready infrastructure from just another catalog wearing a new name:
- A central system of record : Definitions have to be formally validated by the people who own the domain, and the system needs a lifecycle (pending, endorsed, deprecated) so consumers know which definitions are safe to act on.
- Semantic association: Endorsed business meanings have to be bound directly to the technical assets they describe: data objects, APIs, and agents. Without that binding, the glossary is merely another document.
- Universal lineage: End-to-end visibility into how data moves, who touched it, and what depends on it serves three audiences: engineers diagnosing incidents, compliance teams responding to audits, and AI agents deciding whether a change is safe.
- Native, in-workflow access: If users have to leave the environment to consult the metadata system, they’ll avoid it whenever possible, so metadata must exist where the users work, not in a separate catalog that requires context switching.
- Governance guardrails: To prevent hallucinations and off-policy actions, the agent should not have to take a guess. It should be able to look up the rule the way a human employee would consult a policy manual, except automatically, every time.
Skip any of these, and the system will be forced to revert to improvisation somewhere in the reasoning chain, making its whole operation look questionable.
“Enterprises are getting serious about accuracy as they move into production with agentic AI,” Petrie says.
“Boomi Meta Hub helps achieve this. It minimizes hallucination risk by integrating diverse metadata into governed context for agent decisions and actions. This ensures that AI-driven workflows are grounded in the nuanced business reality of the enterprise.”
Boomi Meta Hub for the Metadata Agentic AI Demands
The metadata layer of the Boomi Enterprise Platform is expertly designed to embed AI agents in the endorsed business context they need to be effective and reliable.
Its key capabilities include:
- Instant Business Glossary Curation: AI Suggest transforms a title and purpose into a structured, document-like glossary, eliminating the manual documentation burden and establishing semantic intelligence across the entire Boomi Enterprise Platform.
- Expert Endorsement and Collaboration: Subject matter experts formally endorse definitions through lifecycle statuses (Endorsed, Pending, Deprecated), building a trusted system of record, not a pile of guesswork.
- Semantic Association: Glossaries link directly to technical assets and agents, ensuring both operate on agreed-upon business meaning rather than hard-coded, static instructions that go stale.
- Universal Lineage: End-to-end mapping of data flows provides the traceability agents and teams need to understand downstream impacts before making changes.
- Native, In-Workflow Access: Meta Hub starts with instant business glossary curation as the AI Suggest feature turns a title and a short purpose statement into a structured glossary entry, removing the manual writing burden that kills most glossary projects before they reach critical mass.
And all of this works seamlessly together as a single system is that it was built as one.
“We are a one-stop shop, we have the whole enterprise platform: agents, Data Hub, integration, and then MetaHub sitting alongside them. That eliminates the friction and latency of third-party data hopping,” Kuang says.
“We compete against iPaaS platforms because they don’t have metadata management natively, and we compete against data governance platforms because they don’t have the rest of our stack.”
Your agents are already making decisions. The question is whether they are making them with endorsed business context, or just guessing.
Sign up for the Boomi Meta Hub Early Access Program and ground your agentic AI in the business reality it needs to deliver trusted results.