EdgeBytes | From Apps to Agents: The Race to Own the Enterprise Operating Layer | 3.21.26
Hi everyone— Welcome back to EdgeBytes from The Enterprise Edge, where you get signal over noise in the enterprise AI era.
This week we’re going to try to knit together some conclusions that can be drawn from three recent happenings: First, a startup called Eragon raised $12 million at a $100 million valuation. Second, M&A activity is projected to hit roughly $600 billion in 2026, up 30–40% year-over-year. And third, NVIDIA’s new open agent development platform is a clear attempt to define the infrastructure layer for agentic AI across the enterprise stack.
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I’m Mark Vigoroso, founder & CEO of The Enterprise Edge, and today we’ll quickly break down the significance of these events and what customers, partners, and competitors should take away.
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A $12 million raise at a $100 million valuation for a startup called Eragon would normally be a footnote. Early capital, ambitious pitch, long road ahead. But the idea they’re funding is what matters: an LLM-based operating layer that sits across systems like Salesforce, Snowflake, Tableau, and Jira—and replaces navigation with intent.
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Not dashboards. Not workflows. Not even applications as we’ve defined them for the last 20 years.
Just: “What do you want done?” And the system orchestrates the rest.
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If that sounds familiar, it should. Because this isn’t just a startup pitch—it’s the logical endpoint of what Microsoft is doing with Copilot, what Salesforce is doing with Einstein, what ServiceNow is doing with Now Assist, and what SAP is doing with Joule.
But Eragon is skipping the incrementalism. They’re not adding AI to software—they’re proposing AI as the software.
That distinction matters.
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Because if the interface becomes the agent—and the agent becomes the orchestrator—then the underlying applications risk becoming commoditized execution layers. Valuable, but interchangeable. Necessary, but no longer central to the user experience.
And that’s where the tension begins.
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Now layer in the second signal: M&A activity projected to hit roughly $600 billion in 2026, up 30–40% year-over-year.
That’s not just financial momentum. That’s structural pressure.
Mid-market enterprise software firms—especially those with narrow feature sets, fragmented data models, or weak AI integration—are entering a compression cycle. Their differentiation is being eroded from above by platform vendors embedding AI, and from below by agentic layers abstracting their functionality.
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So what do they do? They consolidate. They get acquired. Or they try to scale fast enough to matter in a platform ecosystem that increasingly rewards breadth, data gravity, and orchestration capability.
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And that brings us to NVIDIA.
Their open agent development platform—now being integrated by Adobe, Atlassian, Cisco, SAP, Salesforce, ServiceNow, Siemens, and others—isn’t just another developer toolkit. It’s an attempt to define the infrastructure layer for agentic AI across the enterprise stack.
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NVIDIA is doing three things simultaneously:
First, they’re standardizing how agents are built and deployed—across industries, across vendors.
Second, they’re anchoring those agents to their compute ecosystem—GPUs, CUDA, and increasingly, full-stack AI infrastructure.
And third, they’re positioning themselves as the neutral layer that sits beneath competing application vendors.
That’s a powerful position.
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Because if Salesforce and SAP and ServiceNow are competing for control of the application layer—and startups like Eragon are competing for control of the interface layer—NVIDIA is quietly securing the substrate that both depend on.
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Now step back and connect these three signals.
You have:
• Startups trying to collapse the application layer into a prompt-driven interface
• Incumbents racing to embed AI into their existing suites
• Infrastructure players standardizing the agent layer beneath them
• And capital markets accelerating consolidation among everyone in the middle
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This is not random activity. It’s convergence toward a new enterprise operating model.
One where value shifts toward three things:
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Control of context — who owns the data, the relationships, the history
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Speed of orchestration — how quickly intent turns into execution across systems
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Compounding intelligence — whether the system gets smarter with every interaction
The companies that win will not be the ones with the most features.
They’ll be the ones that reduce the distance between decision and outcome.
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Now let’s talk about relative positioning.
Microsoft remains the most structurally advantaged in this transition. They control productivity, cloud, identity, and are deeply embedding AI across all three. Their distribution is unmatched.
Salesforce and ServiceNow are well positioned at the workflow and engagement layers—but they face a real risk of interface abstraction if they don’t maintain control of how users interact with their systems.
SAP’s advantage is different: deep transactional data and mission-critical processes. If they can successfully operationalize AI within that core—and not just around it—they retain defensibility.
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NVIDIA, meanwhile, is becoming the default layer for industrial-scale AI. Not because they own the applications—but because they’re making themselves indispensable to every application.
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And then you have the mid-market.
This is where the pressure is most acute.
Companies without a clear data moat, without embedded AI, and without ecosystem relevance are now on the clock. The projected M&A surge isn’t optional—it’s the market deciding who gets to remain part of the stack.
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Now—what does this mean for buyers?
For CEOs, CFOs, and CIOs, the implication is not “buy more AI.”
It’s: rethink how your enterprise actually produces outcomes.
Because the emerging model is compressing cycle times—decision to action, insight to execution, plan to result.
And that has measurable impact.
According to McKinsey, companies effectively deploying AI in operations are seeing productivity gains of 20–40% in targeted functions. Deloitte’s 2025 enterprise AI study shows organizations with integrated AI platforms achieve materially faster time-to-decision and improved margin performance.
But those gains are not coming from tools alone.
They’re coming from integration, orchestration, and adoption.
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So here’s the guidance.
For CEOs:
Prioritize platforms that shorten execution cycles across functions—not just optimize individual tasks. Measure success in time-to-outcome, not feature utilization. If your organization still requires five systems and three handoffs to complete a core process, you are structurally behind.
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For CFOs:
Shift evaluation from cost of software to yield on decisions. The right AI-enabled platforms will reduce latency in revenue generation, cash conversion, and cost control. Demand proof in operating metrics—cycle times, throughput, and margin expansion—not just ROI projections.
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For CIOs:
Design your architecture for interoperability and agent orchestration. Avoid locking into closed systems that limit how intelligence flows across your stack. Prioritize vendors participating in open ecosystems—like NVIDIA’s agent framework—while maintaining governance over your data layer.
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And one more thing.
Be cautious about chasing the interface trend without validating the underlying execution layer.
A prompt is only as powerful as the systems it can reliably orchestrate.
Which brings us back to Eragon.
They may not win. Most startups at this stage don’t.
But the direction they’re pointing is real.
And when a startup, a trillion-dollar chip company, and the largest enterprise software vendors in the world all start moving toward the same architectural endpoint—
It’s worth paying attention.
Because the next phase of enterprise software won’t be defined by what systems you have.
It will be defined by how fast those systems can act on what you need.
And whether they get better every time they do it.
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That’s all for now. Thank you for being with us. Would love to hear your reactions, experiences, and other thoughts. Leave a like, share this video or drop a comment below. See you on the next episode of EdgeBytes. Signal over noise.