There's a leadership posture forming around AI that looks less like "drive adoption" and more like prepare the ground.
Not every organization is ready to ship agents into production. Not every use case makes sense. And yes, there's hype. But leaders who believe an autonomous future is coming are already asking a more concrete question:
What needs to be true for AI results to compound inside an enterprise, rather than fragment into disconnected experiments?
This post is our current answer. We'll cover the production gap enterprises are running into, the two failure modes we keep seeing, and the architectural layers we think an AI-native enterprise needs - starting with context infrastructure, because it changes everything above it.
2025 made the contrast hard to miss: AI can look great in controlled settings, but the gains don't automatically carry into mature systems - large codebases, messy workflows, and regulated environments.
The evidence points in the same direction. In a controlled greenfield task, developers using Copilot completed the work ~55.8% faster. In a randomized trial with experienced developers working in their own familiar repos, allowing AI assistance made them ~19% slower.
That delta is the production gap: the hard part isn't getting a model to do something once. It's making the behavior repeatable, auditable, secure, and cost-predictable as both the tool surface and the organization grow.
And once you see that clearly, the enterprise question changes from "Which model?" to "What infrastructure turns AI into an operational capability?"
The loud story of 2025 was new models, agents, frameworks, and promises. The quieter story happened inside companies: teams trying to make AI part of daily operations and repeatedly hitting the same constraints around access control, debugging, and cost.
That's why the year produced both impressive demos and underwhelming production outcomes. A lot of "AI transformation" stayed stuck as budget-driven experimentation, without a credible path to reliability.
When teams do push forward, three problems tend to show up together:
These pressures usually push orgs into one of two failure modes:
Centralizing early can be the right instinct, but it often becomes a long infrastructure program. Teams wait. The business keeps operating on "manual + meetings" while the platform backlog grows.
Every team has its own agent framework, copilots, MCP servers, and credentials. Progress is fast until it isn't: access sprawl, unclear audit trails, no cost attribution, inconsistent behavior, and no supported path to production.
In both paths, you get activity, but the improvements don't compound. Compounding requires a setup where teams can move fast without bypassing governance, and platform teams can enforce policies without becoming the bottleneck.
So what does that setup look like?
We think AI compounding in enterprises requires:
Connect and govern access to tools/data (where an MCP Mesh fits).
Teams can package capabilities with consistent schemas and permissions (where MCP Studio fits).
Humans and agents can run work safely and distribute what works (apps/modules, and eventually store-like distribution).
Different companies will draw the boundaries differently. But if you're missing the first two, scaling agentic software tends to either stall (too much centralization) or sprawl (too much fragmentation).
This matters now because the ecosystem is converging on a shared substrate for layer (1).
In 2024, MCP looked like "a protocol some people use to connect tools." By late 2025, it's increasingly a shared substrate that major platforms are converging on.
Three signals made the direction hard to ignore:
The implication: interoperability is becoming table stakes, and the hard work shifts upward: how you govern and operate tool access at runtime.
As agents move from "suggest" to "do," governance stops being a policy doc and becomes runtime infrastructure.
This lines up with Gartner's AI TRiSM framing: governance, monitoring, and operational controls as part of running AI systems. And Gartner has also been explicit about the risk side, predicting that by 2030, over 40% of enterprises will experience security or compliance incidents linked to unauthorized "shadow AI."
So the enterprise question becomes practical: Where do we enforce permissions, audit trails, and cost controls—if not inside every single app? That's what context infrastructure is for.
At scale, "connect the agent to tools" turns into a systems problem:
This is what we mean by foundation: centralize cross-cutting concerns you should not rebuild inside every app - SSO and identity, policy enforcement, audit trails, routing, runtime strategies, cost attribution, and debugging.
When tool and data access is governed and observable, you stop paying the "integration tax" per agent. You get a stable runway for teams to build on.
But stability alone doesn't create velocity. The next bottleneck is: who can build, and how safely.
(If you want the technical deep dive on this layer, see our MCP Mesh launch post.)
Once tool access is stable, the next challenge is reuse, correctness, and safe velocity.
Enterprises don't usually get stuck because they can't create AI capabilities. They get stuck because those capabilities are created in one-off ways that don't transfer across teams. What we see is:
Here's the shift that matters: in the pre-AI world, developers mostly built and business teams mostly operated. In an AI-native world, business users can increasingly build too.
That's powerful—and risky—unless you give the organization a shared framework where apps are built as durable assets:
A shared framework also makes business-built apps supportable. When a workflow hits an edge case, developers don't have to rebuild it—they can fix and extend the same app because it uses the same schemas, permissions model, observability, and deployment path. That loop is how you get autonomy without accumulating a second, fragile automation stack.
A practical way to run this is to support two build lanes inside the same framework:
Stricter policies, evaluation gates, change management, stronger auditability, and tight cost ceilings.
Hundreds of small workflows that remove meetings, reduce manual reconciliations, and keep teams moving.
You're making it easy to build the right way—and making "what we learned once" reusable across teams.
Even with a great builder framework, many orgs stall at the same place: capabilities exist, but adoption is accidental. Teams keep rebuilding. Proven workflows don't propagate. Trust stays local.
So you need an internal distribution + lifecycle system that turns "someone built it" into "the organization can rely on it". The point here is distribution across teams, across environments (dev → prod), and over time (capabilities evolve without everyone rewriting integrations).
This is also where a store-like model becomes natural - the UI/UX for lifecycle - catalog, adoption, governance signals, and upgrades.
And longer-term, distribution can extend beyond internal reuse. Once capabilities are packaged as MCP-native apps with clear contracts and permissions, you get a path for app makers to publish pre-built MCP apps—and eventually, for those builders to monetize what they create (internally through chargeback/showback, and externally through a marketplace model when it makes sense).
Compounding happens when "what worked here" can become "how we do it everywhere"—with ownership, trust signals, and a controlled upgrade path.
Most orgs feel forced into one of two extremes:
The approach we think works better is flexible consolidation:
This is also why protocol convergence matters: when the substrate is standard, you can consolidate governance without centralizing everything else.
One secure endpoint for MCP traffic across the organization. Centralizes SSO/IAM integration, policy enforcement, audit trails, routing, observability, and cost attribution.
How teams create MCP-native capabilities as durable assets: standardized schemas, explicit permissions, versioning, and curated building blocks.
How capabilities spread safely: discovery, promotion paths from prototype → production, environment separation, ownership, and upgrades.
One secure endpoint for MCP traffic across the organization. It centralizes the cross-cutting concerns you shouldn't rebuild in every app—SSO/IAM integration, policy enforcement, audit trails, routing, observability, and cost attribution—so teams can connect tools and data without creating a new security and debugging surface every time.
This is how teams create MCP-native capabilities as durable assets: standardized schemas, explicit permissions, versioning, and curated building blocks.
It's designed for two build lanes:
Because both lanes share the same contracts and infrastructure, developers can jump in to fix, harden, or extend workflows created by business users—without rewriting them from scratch.
This is how capabilities spread safely: discovery, promotion paths from prototype → production, environment separation, ownership, and upgrades.
Over time, as apps become well-packaged MCP assets with clear contracts and permissions, we expect distribution to extend beyond internal reuse: builders will be able to publish pre-built MCP apps, and eventually monetize them—turning "we built this once" into an asset that can be adopted (and paid for) repeatedly.
We're building this inside deco and inviting enterprise leaders, platform teams, and builders to shape it with us. Share your current setup, and tell us what's breaking (or what's working). Comments are open too—we read everything.
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