The Hidden Race to Build the Highways of the Agent Era

Kamiwaza’s James Urquhart offers commentary on the hidden race to build the highways of the AI agent age. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

For much of the past decade, progress in artificial intelligence has been measured in size and speed. Each new model promised more parameters, larger context windows, and faster inference times—a race defined by horsepower. This “bigger is better” mindset has become so pervasive that researchers note it is “increasingly seen as common sense.

But as the industry chases marginal gains from ever-larger models, a quieter transformation is underway. The next frontier isn’t faster engines, but better roads. As organizations increasingly train and deploy smaller, private models optimized for specific domains and data, the center of gravity is shifting from scale to coordination. The rise of AI agents amplifies that change, moving value from model performance to the infrastructure that allows intelligence to operate securely, persistently, and at scale. Like the highway networks that made modern travel possible, the agent era will be built on connective foundations: open protocols, persistent memory, and orchestration backbones that unify diverse systems.

Why Agents Need New Infrastructure

Large language models can now sustain remarkably long conversations. With million-token context windows and short-term memory features, they simulate continuity better than ever before. Yet that continuity remains temporary. It lives within the session or application layer, not within the model itself. Once the session ends, so does the context.

Enterprise-grade agents operate on a different plane. They need to reason across time, coordinate with other systems, and preserve verifiable records of what they’ve done. That requires structured, durable memory to maintain continuity, and secure communication channels to ensure that context can be exchanged safely between agents. It also demands governance: clear rules for which agents can access which data, under what conditions, and for how long.

Without this foundation, even the most advanced agents operate in isolation. They can complete tasks independently, but are unable to coordinate safely or predictably with others. The result is a system without shared signals or guardrails: data remains siloed, permissions vary across tools, and no shared protocol defines how agents communicate or exchange memory.

It’s a familiar pattern in computing. Early cloud adopters faced the same kind of fragmentation before orchestration standards like Kubernetes brought order to distributed systems. The agent ecosystem now needs an equivalent layer of coordination—a set of shared rules and connective infrastructure that transforms isolated intelligence into an organized network.

Emerging standards are beginning to fill that gap: protocols to define how agents talk to each other, persistent memory systems to preserve continuity and context, and orchestration backbones to govern how intelligence flows securely across environments. Together, these elements mark the inflection point where AI shifts from individual models to a distributed system of cooperating agents.

Enabling Standards for Orchestrated Intelligence

A new layer of standards is beginning to define how agents connect and retain what they learn. These aren’t product features—they’re the scaffolding of a distributed intelligence ecosystem.

The Model Context Protocol (MCP), introduced by Anthropic to standardize how AI models interact with external tools and data sources, has quickly gained traction among enterprise and open-source developers. It serves as connective tissue among models, tools, and applications, giving agents a shared language for context exchange. Through consistent interfaces, they can retrieve data, invoke tools, and hand off tasks without custom integrations. MCP effectively turns interoperability into infrastructure.

The Agent-to-Agent (A2A) protocol, originally developed by Google and now governed by the Linux Foundation, addresses the coordination layer of distributed intelligence. It defines how agents communicate, share state, and delegate tasks through standardized message formats and trust boundaries. By enabling collaboration across frameworks and vendors, A2A converts isolated systems into components of a shared network, advancing the shift toward interoperable, orchestrated AI.

Advances in memory-driven architecture are also redefining what it means for intelligence to persist. Recent research like MemOS, a “memory operating system” for large language models, treats memory as a managed resource: durable, queryable, and shared across sessions. Google’s Vertex AI Memory Bank and Microsoft’s work on agent memory management move in the same direction, giving AI systems continuity that extends beyond a single interaction. The underlying idea is simple but profound: intelligence gains stability when it can remember. Persistent memory transforms inference from a transient calculation into an ongoing process of accumulation and refinement—an essential step toward agents that learn, coordinate, and improve within governed enterprise systems.

Together, these developments push AI toward orchestrated intelligence: agents that act with continuity, context, and shared governance.

What This Means for Enterprises

For enterprise leaders, these developments mark a shift in where value is created. The competitive edge now lies in coordination: how effectively organizations can connect models, data, and agents into a coherent system.

To build a durable foundation for agentic AI, organizations will need to:

• Adopt and unify emerging standards. Building on open protocols like MCP and A2A reduces integration friction and keeps options open as the ecosystem evolves.
• Reimagine compliance for persistent memory. Durable agent memory changes how privacy, retention, and audit ability must be managed. Governance frameworks will need to account for data that endures across sessions and systems, balancing innovation with accountability.
• Treat interoperability as a strategic investment. Systems that exchange context across environments deliver compounding returns in flexibility, insight, and resilience.

Recent moves such as Snowflake’s acquisition of Crunchy Data and Databricks’ purchase of Neon illustrate the consolidation underway across the data and AI stack. Platform leaders are racing to control the connective layers—databases, pipelines, and orchestration systems—that manage how intelligence flows through distributed environments. As agent ecosystems mature, these foundations will define both the performance and the resilience of enterprise AI.

The Road Ahead

Every technological era reaches a point where innovation depends on connection. AI is now at that point. The progress of the next few years will be defined less by new model break throughs than by the reliability of the systems that surround them: protocols, memory, and orchestration. These are the highways that let intelligence move securely and at scale.

These foundations are still forming, yet their direction is clear. As open standards mature, they’ll turn today’s isolated capabilities into coordinated systems. The organizations that invest early in this connective infrastructure will define how far and how safely the agent era travels.