OpenAI Frontier Needs Shared Business Context Most Companies Don't Have

AI agents fail in production because they lack shared business context. Without connectivity (where data lives), semantics (what terms mean), and experience (how work gets done), agents generate confident mistakes instead of trustworthy outputs. OpenAI Frontier requires this infrastructure, but most enterprises don’t have it. Here’s the readiness gap blocking deployment and what you need to build first.

OpenAI’s announcement of Frontier articulated what you’ve been wrestling with: AI agents need shared business context to operate reliably at scale. This infrastructure gap (the space between AI prototypes that work in controlled environments and production systems that deliver consistent value) is what’s called the AI Value Chasm. OpenAI’s explicit focus on shared business context validates the challenge you face deploying agent platforms.

Shared business context means your agents can find data across fragmented systems (connectivity), understand what business terms mean (semantics), and know how work actually gets done (experience). Without this infrastructure, agents operate blind, unable to locate relevant data, misinterpreting business terminology, or following procedures that don’t match reality. The context layer provides the foundation agents need to generate trustworthy outputs rather than confident mistakes.

Here’s what Frontier readiness requires at a glance:

Aspect	Details
Primary Requirement	Unified infrastructure addressing connectivity, semantic, and experience gaps
Three Core Gaps	Connectivity (data across systems), Semantics (business terminology), Experience (workflow knowledge)
Infrastructure Scope	Cross-system integration spanning warehouses, lakes, SaaS applications, BI tools, and operational systems
Ideal For	Organizations deploying AI agents with decision-making capabilities or broad data access
Critical Dependencies	Unified metadata layer, machine-readable definitions, captured institutional knowledge

What OpenAI Means by Shared Business Context

Shared business context means your agents can find data across systems (connectivity), understand terminology (semantics), and know workflows (experience). OpenAI’s internal data agent uses six context layers spanning 600 petabytes, yet employees still struggle with table disambiguation. This reveals the infrastructure depth production agents require beyond basic access.

OpenAI describes Frontier’s foundation this way: “Frontier connects siloed data warehouses, CRM systems, ticketing tools, and internal applications to give AI coworkers that same shared business context. They understand how information flows, where decisions happen, and what outcomes matter. It becomes a semantic layer for the enterprise that all AI coworkers can reference to operate and communicate effectively.”

Break down what this actually requires:

“Connects siloed data warehouses, CRM systems, ticketing tools, and internal applications” — This is the connectivity problem. Your enterprise data lives fragmented across dozens of systems using incompatible formats. Your agents need unified access to data wherever it lives, not system-by-system integration work for each use case.

“Understand how information flows, where decisions happen” — This is the experience problem. How work actually gets done lives in SOPs, Slack threads, email chains, meeting decisions, and people’s institutional knowledge. Your agents need access to workflow understanding and business logic humans carry implicitly.

“What outcomes matter” — This is the semantic problem. Business terminology like “customer,” “revenue,” “active,” “qualified” means different things across your departments and contexts. Your agents need machine-readable definitions showing what terms mean specifically in your organization’s context.

OpenAI built internal infrastructure demonstrating this architecture. Their data agent operates across 600 petabytes and 70,000 datasets, using six context layers: table usage patterns, human annotations, AI-generated enrichment, institutional knowledge, conversational memory, and runtime context. What this reveals is that agents need multiple overlapping context sources, not just schema information but usage patterns showing which data employees actually trust, annotations capturing business logic, and enrichment providing semantic understanding.

Why Semantic Layers Aren’t Enough

Semantic layers define consistent business metrics and calculation logic, solving part of the semantic gap. But they don’t address connectivity (how agents find and access data across fragmented systems) or experience (how agents understand workflows and institutional knowledge).

You’ve likely implemented semantic layer tools and structured your metrics definitions, answering “how should we calculate revenue?” Context layers answer the broader question: “what data exists measuring revenue across our systems, what does revenue mean in different contexts, how do we actually use this data, and is it reliable right now?”

Frontier agents operating with semantic layers alone still face the connectivity gap (can’t access data they need) and experience gap (don’t understand workflow context). Complete agent infrastructure requires addressing all three gaps together.

Gap Type	What It Solves	What Agents Can’t Do Without It	Infrastructure Required
Connectivity	Find and access data across systems	Locate relevant data, query fragmented platforms	Unified metadata layer, cross-system discovery
Semantic	Understand business terminology	Interpret terms correctly, disambiguate definitions	Machine-readable glossary, usage context
Experience	Know how work gets done	Follow actual procedures, apply judgment	Captured workflows, institutional knowledge

Why Don’t Most Enterprises Have a Shared Business Context Yet?

Your enterprise context exists today. It’s just fragmented, undocumented, and inaccessible to agents.

The connectivity gap exists because your metadata lives in silos. Unified metadata layers cataloging data across warehouses, SaaS applications, lakes, and BI tools remain rare. You’ve built system-specific catalogs (the warehouse knows about its tables, the BI tool knows about its dashboards, SaaS applications maintain their own schemas) but no unified view spans all systems. Your agents can’t query fragmented metadata scattered across disconnected platforms.

The semantic gap exists because your definitions live in human-readable documents. Your product teams document “active user” in Confluence. Finance maintains “revenue recognition rules” in spreadsheets. Marketing defines “qualified lead” in email threads. Governance committees discuss “PII classification” in meeting notes. This institutional knowledge provides human context but remains invisible to agents needing structured, machine-readable metadata they can programmatically query.

The experience gap exists because workflow knowledge lives in people’s heads. How work actually gets done (the exceptions, edge cases, judgment calls, timing dependencies, escalation procedures) exists as tribal knowledge transmitted through meetings and Slack messages. You’ve documented official procedures, but agents need the real procedures including all the informal understanding experienced employees apply instinctively.

Even organizations with strong foundations struggle with context infrastructure. OpenAI’s internal data agent serves 600 petabytes across 70,000 datasets, yet employees still report spending “tons of time trying to figure out how similar tables differ and which to use.” If unified platforms with consistent infrastructure face disambiguation challenges, your federated enterprise environment with heterogeneous systems encounters even greater complexity.

The gap isn’t tooling availability, it’s integration architecture. You have catalogs, glossaries, documentation systems, and monitoring tools. What’s missing is the unified infrastructure making this context queryable, comprehensive, and accessible to agents operating at machine speed across business terminology rather than technical schemas.

How Do You Audit Your Infrastructure Readiness for AI Agents?

You face three interconnected gaps when building agent infrastructure. Each requires specific capabilities before agents can operate reliably in production environments.

The Connectivity Gap

Your agents need to operate on data fragmented across SaaS tools, data platforms, and dashboards. The connectivity problem isn’t just cataloging systems, it’s making data discoverable and accessible when it lives in dozens of disconnected places.

Foundation: Can your agents discover what data exists across all systems? This requires unified metadata infrastructure cataloging assets across data warehouses, business intelligence tools, and SaaS applications. Active metadata management goes beyond static catalogs, capturing runtime information showing which assets employees actually use and trust.

Control: Can your agents access the data they discover? Beyond discovery, agents need programmatic access respecting existing security controls. This means propagating authentication and authorization across heterogeneous systems so agents operate within the same access boundaries governing human users.

Reliability: Do your agents know if the data they’re using is trustworthy? Connectivity without quality assessment means agents confidently use stale, incomplete, or incorrect data. You need data quality monitoring, lineage tracking showing data transformations, and freshness indicators revealing when datasets last updated.

The Semantic Gap

Discovery and access solve the technical connectivity problem. But your agents still need to understand what your business terms actually mean.

Foundation: Are your business definitions machine-readable? Your agents can’t parse Confluence pages explaining “active user” or spreadsheets documenting revenue recognition rules. You need business glossaries integrated into metadata platforms where agents can programmatically query term definitions.

Control: Can your agents disambiguate terms that mean different things in different contexts? “Customer” means something different to sales (signed contract), support (active account), and finance (revenue-generating entity). You need contextual definitions showing how terminology varies across organizational boundaries.

Reliability: Do your semantic definitions reflect actual usage versus documented standards? Just like workflow documentation, business definitions often describe ideal states rather than reality. You need usage analytics showing which definitions employees actually apply when making decisions.

The Experience Gap

Even with connectivity and semantics, your agents lack the workflow knowledge and institutional understanding humans apply instinctively.

Foundation: Is your workflow knowledge captured and queryable? This includes standard operating procedures, escalation paths, exception handling logic, and timing dependencies. You need documentation systems where workflow knowledge exists in structured formats agents can process rather than narrative descriptions humans read.

Control: Can your agents distinguish between documented procedures and actual practice? You document ideal workflows, but work happens through informal coordination, judgment calls, and contextual adaptation. You need capturing of institutional knowledge showing how experienced employees handle edge cases.

Reliability: Do your agents understand when to escalate versus when to act autonomously? Not every decision requires human approval, but agents need clear boundaries. You need governance frameworks defining agent decision authority and establishing monitoring for actions requiring human review.

AI readiness implementation typically addresses these sequentially. Start with connectivity (agents need access to data before they can interpret it). Build semantics second (agents with access but no understanding generate unreliable outputs). Add experience last (agents can provide value without complete workflow knowledge through careful scope limitation and monitoring).

This isn’t tool procurement, it’s infrastructure work. You must determine where context lives today, how to capture it systematically, where it should centralize versus remaining distributed, and how agents access it reliably.

Tool Type	What It Provides	What It’s Missing for Agents	When to Use
Data Catalog	Discovery, metadata	Semantic definitions, experience	Part of connectivity solution
Semantic Layer	Metric definitions, calculation logic	Connectivity, workflows	Part of semantic solution
Business Glossary	Human-readable definitions	Machine-readable format, API access	Input for semantic infrastructure
Context Layer	Unified metadata + semantics + experience	N/A — comprehensive solution	Foundation for all agent platforms

How Atlan Approaches AI Agent Readiness

The challenge with agent infrastructure isn’t theoretical understanding, it’s practical implementation across systems that weren’t designed for unified context access.

You discover this when agents fail unpredictably. You deploy an agent for financial analysis. It queries the wrong revenue table because your semantic layer doesn’t capture that “revenue” means different things in forecasting versus actuals. Or it can’t find the customer churn data because that lives in a SaaS tool your metadata catalog doesn’t index. Or it follows documented approval procedures that everyone knows changed six months ago but the documentation never updated.

Atlan’s approach recognizes that context infrastructure must span technical and human understanding. The platform provides unified metadata management across data warehouses, lakes, BI tools, and SaaS applications, solving the connectivity foundation. But it goes beyond technical cataloging to capture business context through embedded glossaries, usage analytics showing which assets employees trust, and workflow documentation integrated with data assets themselves.

Organizations implementing Atlan for agent readiness report that the hardest part isn’t technology procurement, it’s the organizational work capturing semantic definitions and workflow knowledge that exists as tribal understanding. You’ll spend weeks documenting what “active customer” actually means across contexts, mapping approval workflows that never got formally written down, and identifying which datasets employees consider authoritative versus supplementary.

Frequently Asked Questions

Does OpenAI Frontier include the shared business context infrastructure organizations need?

No. Frontier provides the agent platform but you must supply the context layer. Your agents need unified metadata spanning fragmented systems, machine-readable business definitions, and captured workflow knowledge. Without this infrastructure, you can’t deploy Frontier reliably regardless of platform capabilities. Build the context layer before implementing agents.

Can we build the context layer incrementally while deploying Frontier agents?

Limited initial deployments work with partial context infrastructure. Start with foundational capabilities and deploy agents for narrow use cases where context gaps don’t affect reliability. Expanding beyond controlled testing demands addressing connectivity, semantic, and experience gaps systematically. Attempting full deployment without comprehensive context infrastructure creates unpredictable failures.

How does the context layer differ from our existing data catalog?

Your data catalog provides discovery infrastructure, solving part of the connectivity gap. Catalogs answer “what data exists and where is it” but don’t provide semantic definitions agents need to understand terminology, workflow knowledge showing how work gets done, or governance and quality signals required for reliable operation. The context layer integrates discovery with semantic understanding and experience capture.

What happens if agents operate without complete context infrastructure?

Your agents encounter predictable failures with incomplete context. Connectivity gaps mean agents can’t find relevant data or access systems they need. Semantic ambiguity causes misinterpretation of business terms leading to incorrect analysis. Missing experience context results in agents following documented procedures that don’t reflect actual practice. These failures make reliable operation impossible.

Can semantic layers replace the full context layer?

Semantic layers address part of the semantic gap through metric definitions and calculation logic but don’t solve connectivity (finding and accessing data across systems) or experience (understanding workflows and institutional knowledge). You’ve structured your metrics but agents still need unified access to fragmented data and captured workflow understanding. Complete agent infrastructure requires semantic layers integrated with broader context capabilities.

How long does building a production-ready context layer take?

You’ll need several months implementing foundation, control, and reliability capabilities if starting from fragmented metadata. Timeline assumes existing data infrastructure (warehouse, orchestration, monitoring) and focuses on metadata integration work. Organizations building from scratch requiring tool procurement and basic data platform capabilities need significantly longer establishing context infrastructure before agent deployment.

Is the context layer specific to OpenAI Frontier or required for all agent platforms?

All agent platforms require shared business context regardless of provider. Your enterprise agent implementations from major cloud providers and AI companies face identical requirements. Agents need unified metadata infrastructure providing discovery, semantic understanding, access control, lineage mapping, and quality assessment. The context layer is a prerequisite infrastructure for agent platforms, not a vendor-specific requirement.

Who typically owns building the context layer within organizations?

Context layer development spans your data platform teams, governance organizations, and business stakeholders. Data platform teams implement discovery infrastructure and lineage instrumentation. Data governance teams establish semantic definitions and access policies. Quality engineering teams build reliability monitoring. Business stakeholders define terms and metrics. Successful implementations establish cross-functional working groups coordinating development rather than assigning ownership to single teams.

Can we use our existing business glossary and governance committee for the context layer?

Your existing glossaries and governance committees provide critical input but don’t constitute the technical infrastructure agents require. Agents need machine-readable metadata accessed through APIs, not document-based definitions humans read. Your business glossaries must migrate from Confluence pages to metadata management platforms. Governance policies must transform from committee decisions to enforced technical controls. Institutional knowledge must convert to queryable infrastructure.

What metrics indicate the context layer is working correctly?

Context layer effectiveness measures through agent reliability metrics. Track your agent task success rates, time-to-completion for common requests, access violation frequency, data quality incident rates, and user trust scores. Organizations with mature context infrastructure see high agent task success rates, fast completion times for standard requests, minimal unauthorized access violations, and sustained user confidence in outputs. Low success rates or frequent quality incidents indicate gaps requiring attention.

What Infrastructure Investment Determines Agent Success?

OpenAI Frontier makes the implicit requirement explicit: agents need shared business context to operate reliably at scale. Closing this context gap represents the infrastructure investment determining whether agent platforms deliver production value or remain confined to controlled demonstrations.

You operate with fragmented context across three dimensions.

Connectivity: data scattered across warehouses, SaaS tools, and applications with inconsistent access patterns.
Semantics: business definitions living in documents, Slack threads, and tribal knowledge rather than machine-readable formats.
Experience: workflow understanding and institutional knowledge carried in people’s heads, not captured in systems agents can query.

Agents attempting to operate across this landscape encounter the same failures early LLM adopters experienced: prototype success that doesn’t scale because underlying infrastructure can’t support reliable operation.

Connectivity, semantics, and experience interact and compound. You’ll discover which gaps matter most through implementation, not assessment. What’s consistent: agents need baseline capability across all three. Strong connectivity without semantics means unreliable analysis. Perfect semantics without connectivity means agents can’t act. Both without experience means operationally inappropriate decisions.

OpenAI identified the right requirement. Agent success depends less on the platform itself than on the foundation making business context (across systems, terminology, and workflows) queryable and reliable enough for trustworthy outputs at scale.

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Atlan is the next-generation platform for data and AI governance. It is a control plane that stitches together a business's disparate data infrastructure, cataloging and enriching data with business context and security.

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