The Real Bottleneck in Enterprise AI Isn’t the Model, It’s Context

Everyone’s racing to ship AI agents for data work. They want them to write SQL, debug pipelines, generate tests, auto-document assets and surface insights on demand. It almost feels as if the promise of self-serve analytics that data engineers have been waiting for has finally arrived. Unfortunately, these deployments are failing simply because the agents don’t understand how the data platform actually works. They don’t know which tables to trust, whether pipelines are flaky or who owns what. They can’t trace how a schema change in one domain corrupts dashboards, models and metrics elsewhere. So they hallucinate. They query stale or deprecated assets, optimize for the wrong sources and give executives well-written, yet systematically wrong answers. This is what I call the context wall: the hard boundary between what AI can generate and what it can reliably ground in production reality. The context wall is forcing a shift in focus from shiny interfaces to the infrastructure layer underneath, because that’s where the real intelligence already lives. Why Today’s AI Agents Are Flying Blind Most enterprise AI strategies still treat context as an afterthought. A large language model (LLM) gets dropped on top of a warehouse. Maybe there’s a catalog or maybe internal docs are indexed and wired into retrieval-augmented generation (RAG). On paper, the agent has schemas and descriptions. In practice, it has almost no sense of operational truth. The agent doesn’t know whether last night’s job failed, if table names are being decommissioned or if Finance trusts a specific curated table for close. It can’t tell if a missed service-level agreement (SLA) upstream should invalidate five downstream dashboards. Without live, operational context, AI agents become fancy UIs over incomplete metadata. They’re fine for demos, but dangerous for decisions tied to revenue, risk or regulation. If we want agents that can be embedded into critical workflows, they can’t be blind copilots. They need to see how data is produced, validated, moved and consumed — continuously, not just at design time. Orchestration: The Missing Context Layer Every time a pipeline runs, fails, retries, passes a test or breaches an SLA, the orchestration system records it. Over time, this becomes a full-fidelity operational record containing lineage, health, ownership and usage across lakes, warehouses, streams and apps — not just one system. That makes orchestration metadata a de facto “flight recorder” for the entire data platform, which provides: A live view of lineage and dependency chains A view of what’s healthy versus chronically broken Clear ownership and responsiveness signals Evidence of which assets are actually business-critical That broad picture is exactly what most AI agents miss today. In more complex and heavily regulated environments, this becomes a major gap. Financial services, healthcare, critical infrastructure, public sector and air-gapped or remote deployments all need provable lineage, strong controls and explainability. In those settings, orchestration is the source of truth that makes trustworthy AI even possible. What AI Native Looks Like with Orchestration Intelligence An AI native data platform doesn’t start with a chatbot. It starts by turning orchestration into a context engine for both humans and agents. Let’s compare two agents. Agent A is wired only to the warehouse and catalog. It sees schemas, names and stale docs, but can’t distinguish gold from garbage. It will happily generate SQL on top of broken pipelines and tell a great story about it. Agent B is grounded in orchestration.Before recommending or querying a table, it checks run history, test results, SLAs, lineage and downstream importance. It defaults to assets that are healthy, governed and owned, and can explain its choices. If a key job fails, it knows which metrics, dashboards and AI workflows to flag or pause. Once orchestration intelligence is the substrate, new capabilities fall out naturally: Reliability-aware SQL and insights: Agents choose sources based on health and certification, not guesswork. Instant impact analysis: A schema or pipeline change triggers automated blast-radius detection. Out-of-the-box observability: Because open ecosystems like Apache Airflow already integrate across the stack, lineage and metadata are captured as pipelines run. Human plus agent usability: The same context layer is searchable and explorable by engineers, operators and AI agents. That’s what “AI native” actually means here. It’s AI that is born inside the internal operations of the platform, not bolted onto it. Where We Go From Here The real bottleneck in enterprise AI is no longer the model. It’s the absence of grounded context. Treating orchestration telemetry as strategic, and exposing its view of lineage, health, ownership and usage as a shared context layer, is how AI becomes reliable. As more work is handed to agents, the systems that embed this context from day one will be the ones that stay accurate, explainable and safe in production. Getting your AI to understand how the data platform truly runs can take it from demo state to being part of the core stack. The post The Real Bottleneck in Enterprise AI Isn’t the Model, It’s Context appeared first on The New Stack.