From Modules to Mind: How MCP Sparks Collective AI Cognition

Introduction

Financial services increasingly rely on AI to support decisions and manage complex workflows. Yet, as Vahe Andonians, Founder and CEO of Cognaize, emphasized in the recent webinar From Modules to Mind: How MCP Sparks Collective AI Cognition, the industry still struggles with a critical gap: building systems that scale, reason, and adapt. The missing piece? Memory.

Why Context Matters

In current agent-based systems, three foundational goals often remain out of reach: maintainability, debuggability, and modularity. While traditional software engineering builds for these attributes, today's AI systems frequently grow in an unstructured, probabilistic fashion, driven by disconnected prompts and loosely assembled tools. The real challenge isn't building agents themselves, but giving them structured, usable memory.

To function over time, agents must understand context—what information matters, in what format, and for which task. Without this, model outputs become brittle and hard to trace. Cognaize's Founder and CEO Vahe Andonians argued that creating systems with shared context is key to making AI maintainable, scalable, and ultimately useful.

From Agents to Hypergraphs

Cognaize’s approach moves beyond isolated agents. Instead of chaining outputs through basic prompts, the team is designing hypergraphs—interconnected structures where models share and request context dynamically. Each agent works with specific layers of context: document-level, user-level, task-specific, and system-wide.

In this architecture, agents declare what context they need, which allows the system to route and reuse relevant data. It also enables systems to grow organically, improving performance without sacrificing clarity or control. This shift—from hardcoded exchanges to context-aware collaboration—lays the groundwork for the next generation of financial AI.