The Wedge and the Vision: Code to Agentic Infrastructure

If you only have a minute (TLDR):

The big idea: LLMs broke software's most powerful feedback loop—learning from distributed experience. Shared memory can fix it.

Why it matters: When answers are generated but experience doesn't flow back into the system, we get stalled learning, duplicated effort, and brittle automation.

Our approach: Build shared memory as a first-class primitive—where people and agents contribute procedural knowledge, keep it fresh, and steer how it's used.

The journey:

• Code agents first: Prove the learning flywheel (contribution → validation → reuse) in the wild
• Enterprise next: Same flywheel, but behind your firewall. Productivity gains compound across your entire organization
• Expand domains: DevOps, security, compliance—turn tribal knowledge into durable learning systems
• Scale the model: White-label for vertical SaaS—every application becomes a learning system

The bottom line: Start narrow (dev tools), expand systematically (enterprise domains), become foundational (the runtime for agentic systems). What looks like a feature today becomes the infrastructure tomorrow.

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Fix the broken flywheel, prove the loop, then expand it.

In the era of LLMs, we broke one of software’s most powerful feedback loops: learning from distributed experience. Answers are generated, but the lived experience from users and agents rarely flows back into the system in a durable, queryable, learnable way. The result? Stalled learning, duplicated effort, and brittle automation.

At The Memory Company, we started from a simple belief: shared memory should be a first-class primitive. When people and agents can contribute procedural knowledge, keep it fresh, and steer how it’s used, the learning flywheel restarts.

We believe this primitive applies across domains and environments. In this post, we’ll explore how that vision expands.

1. Fix the Flywheel (Open Ecosystems)

We began in open, permissionless environments - where the challenge is hardest and the feedback richest. Open-source ecosystems are perfect laboratories: adversarial, high-velocity, and transparent by default. Proving that shared memory can capture procedural knowledge, govern it, and make it reusable in this environment gave us a blueprint for dependable learning loops.

This stage is about hardening the protocol:

• Prove that contribution → evaluation → reuse creates real lift.
• Show that distrust doesn’t block collaboration.

Once we saw that a flywheel could spin in the open, we asked: who really cares about it working?

We looked at the other side of the community - the maintainers. They have the incentives and desire to both learn from and steer a community of users to build better products over time.

With them, through our Design Partner Program, we’re pioneering a governance platform for shared memory that scales with autonomy.

As it turns out, not only open-source developers and firms experience these broken flywheels. So we turned our attention to enterprises.

2. Prove the Loop Spins Inside Too (Enterprises)

Enterprises are the next proving ground. Their wedge is also code agents—the most measurable, highest-ROI domain for AI today. Our research shows that shared memory lets enterprises use open models while achieving state-of-the-art results through learning, not just larger weights.

The same loop can spin under enterprise constraints:

• Feedback from every developer interaction feeds learning
• Shared memory stays within policy boundaries.
• Each improvement compounds inside the organization’s walls.

A 5–10% productivity gain on top of baseline AI uplift may not sound like much, but at enterprise scale it’s transformative. More importantly, enterprises start owning their learning loops—their intellectual flywheels.

Note: We think the infrastructure we build for agents today will look like the runtime of the agentic system tomorrow. As the code agent ecosystem evolves, we believe it will outlive many hand-built cognitive workflows. Getting the memory and learning substrate right now is how we earn the right to be that runtime later.

3. Find New Flywheels

Once the loop works for code, it can extend to other high-value procedural domains. In early conversations with enterprises, we’ve identified DevOps, security, and compliance as areas where procedural knowledge is core IP yet remains trapped in tribal memory. Shared memory turns those pockets into durable, testable assets.

Each new domain becomes its own flywheel of learning and governance. Over time, enterprises evolve from managing workflows to managing learning flows.

While many players will compete for enterprise attention, we believe the real advantage will belong to those who adopt an open shared-memory protocol—one that lets them retain control over their autonomy while steering how learning happens inside their systems, without falling into the inevitable memory and learning silos that will emerge among their vendors.

4. Let Others Build Their Own Flywheels

The same infrastructure that connects tools and employees within an enterprise can be white-labeled across vertical applications. In our interviews, developers of vertical SaaS products told us they would love a turnkey, multi-tenant learning module—one that is steerable, privacy-aware, compliant by design, and enterprise-ready. Something that allows their applications to improve over time without leaking data, while staying personal to each tenant.

Conclusion Each stage builds on the previous one, transforming shared memory from a product feature into the runtime of the agentic system of tomorrow.

From fixing to compounding, our path reflects the evolution of shared memory from a tool into a foundation. What began as a way to repair broken learning loops in open ecosystems can become a new model for how systems learn, adapt, and govern themselves. Each stage—open collaboration, enterprise learning, domain expansion, and cross-tenant learning—builds the scaffolding for a world where memory is not static but living, shared, and continuously improving.

The outcome isn’t just better automation; it’s a shift in how intelligence compounds across boundaries. The infrastructure we’re building today for agents will define how organizations and networks learn tomorrow—one flywheel at a time.