The Missing Context Layer in AI Agent Infrastructure
AI agents are getting smarter. But without context infrastructure, they're still flying blind.
"AI models are rapidly becoming commoditised," Microsoft CEO Satya Nadella said this week. "The real competitive edge will lie in context engineering — the ability to ground AI systems in enterprise data, workflows, and human relationships so that agents can reason, plan, and act with relevance."
He's right. And it points to a gap in most AI architectures today.
When a human pulls data from Salesforce or queries an ERP system, they're not operating in a vacuum. The user brings strategic understanding to the interaction. They know why they're asking. They know what matters. They can interpret results against competitive dynamics, market conditions, organizational priorities.
That context isn't stored in your systems of record.
Systems of record solve for access and governance. They answer: What data exists? Who can touch it? What's the audit trail?
They don't answer:
- Why does this data matter strategically?
- How should it be interpreted relative to market dynamics?
- What questions should we even be asking?
Context Before Execution
This is the architectural gap in most agent frameworks today. We've built sophisticated systems for storing data and governing access. We've built increasingly capable models for reasoning and execution. But we've underinvested in the layer that connects them — the strategic context that tells agents why before they figure out how.
Most AI stacks start with reasoning or agents and assume context will magically appear. That's the failure mode.
The architecture needs to be: Context → Systems of Record → Execution.
Get the context layer wrong, and even perfect data governance can't save you. It's like giving someone flawless turn-by-turn directions to the wrong address.
What Context Infrastructure Looks Like
This is the problem we're solving at Strata. We sit before agent action begins.
We turn the external world — companies, markets, competitors — into Context Shells: portable, machine-readable units of strategic intelligence that explain what's true, what just changed, and why it matters right now.
Before an agent queries your CRM, before it pulls from your data warehouse, before it executes any workflow, it consumes a Context Shell. Who is this company? What's their competitive position? What market dynamics are shaping their decisions? What should we actually be trying to understand?
We call this Layer 0: the context layer that sits beneath agent orchestration and above raw data. It's not memory (what happened). It's not retrieval (what's stored). It's understanding - the strategic intelligence that humans carry around by default and agents desperately need.
The result: humans and agents reason and act from the same situated understanding.
Systems of record will absolutely grow in value as agents scale. But they'll grow in value because a context layer makes them usable, not instead of one.
We're building the context infrastructure layer the rest of the AI stack assumes exists...but doesn't.
If you're building agents and finding yourself patching over context gaps with prompt engineering and retrieval hacks, you're not alone. We built Strata because we hit the same wall. Come see what we're working on.