The VantEdge Manifesto
Thesis
We believe AI infrastructure will evolve toward separation of concerns: context systems that retrieve and prepare data, distinct from agent systems that reason and act.
This isn't about building better APIs. It's about recognizing that context retrieval and agentic reasoning are fundamentally different problems requiring different architectural approaches — especially in enterprise environments where data is distributed, security is non-negotiable, and accuracy determines trust.
Context Systems vs. Agent Systems
The field is separating two distinct capabilities:
Agent Systems decide what to do, how to decompose tasks, when to use tools, how to respond to users.
Context Systems understand where data lives, how to access it securely, what it means in business terms, how to present it accurately.
These are complementary, not competing. But conflating them means neither works well and changes to one break the other.
Just as databases became separate from application code and authentication became separate from business logic, context retrieval is becoming a distinct infrastructure layer with its own requirements and architectural patterns.
What Makes a Context System
Core principles are emerging:
Business semantics at infrastructure layer - Context systems must encode organizational knowledge — the meanings, relationships, and rules that exist in institutional memory but not in schemas. This isn't metadata management; it's making implicit business logic explicit and executable at the infrastructure level.
Unified security across heterogeneous sources - Security policies defined once and enforced universally, regardless of where data lives or how it's accessed. The context system becomes the single enforcement boundary for what agents can and cannot see.
Observable data flow - Every retrieval traced end-to-end. When results are wrong, you can reconstruct exactly what was queried, where it came from, how it was transformed, what was filtered. Context systems must be designed for forensics, not just execution.
Separation from reasoning - The context system doesn't decide what information an agent needs — the agent does. But the context system owns how to retrieve it, what it means, whether access is permitted, and how to present it accurately. Clear boundaries enable independent optimization.
Where the Field Is Moving
From integrated to separated - AI infrastructure will evolve from agents handling their own data access to specialized context systems that do it better.
From general to specialized - Generic API gateways won't solve enterprise context problems. Purpose-built systems optimized for how agents consume data.
From implicit to explicit - Business knowledge in tribal lore or agent prompts will be encoded in infrastructure. The context system becomes the source of truth.
From monolithic to federated - Enterprise context systems must work across cloud boundaries, support air-gapped deployments, respect data residency. This requires new federation patterns.
The Opportunity
The separation is happening. The question is whether to build context infrastructure in-house, embed it in every agent, or adopt specialized systems.
The companies that recognize context as distinct infrastructure — and build or adopt it accordingly — will deploy AI systems that others can't.
If you're building production agents and hitting data access problems, we'd love to compare notes.
—B & R & E
Built at the University of Toronto