Software development teams are adopting agents at a pace that should excite investors. In less than a year, agentic development workflows have reached roughly 80% adoption inside early enterprise and high-growth engineering organisations, a level of usage that took traditional IDEs (Integrated Development Environments) more than a decade to achieve. The velocity is the signal that agentic AI is becoming a major platform shift.
And history is clear about what happens next. Every big wave of computing brings new winners in the infrastructure space. AWS maximised its position to become the biggest cloud provider. NVIDIA became synonymous with AI model training by pivoting to provide a foundational, high-performance hardware and software ecosystem. We can expect the same thing from agentic AI. The applications will get all the attention, but the infrastructure layer will hold the long-term value.
Shrewd investors will fund AI infrastructure because it will enable agents to go to work and help grow enterprise businesses. That invisible layer will be the place where budgets are set, purchases are made, and the real winners stand out from the hype in 2026.
The disaggregation happening right now
Enterprise buyers historically reject vertically integrated stacks. Yet that’s what’s happening in AI right now and no one is talking about it. Emerging markets – like AI and autonomous coding – naturally gravitate toward the simplicity of ‘one tool to rule them all’, with a single vendor offering the model, the agent, the IDE, the deployment pipeline, and governance in one package. In practice, though, this doesn’t work in the enterprise. The value of simplicity is eclipsed by the need to maintain control over how these systems operate.
That’s why engineering leaders are doubtful about end-to-end platforms, even when the user experience is good. It doesn’t matter if it’s a tightly bundled agent + IDE + cloud package or an AI model provider pushing a full-stack workspace because the same problems are repeated.
There are three reasons why buyers are rejecting vertically integrated stacks:
1. Control. Enterprises need ownership of workflows, telemetry, and decision boundaries. If agents become active participants in development (not just autocomplete helpers) then who controls them becomes existential. More importantly, companies want the freedom to change their mind and not be locked into a single option. Freedom to choose will drive many decisions.
2. Performance. Teams want the freedom to tune infrastructure for their workloads (latency, routing, caching, codebase indexing, and compute allocation). The fastest teams are already optimising agent performance like they optimise CI.
3. Compliance. Security teams are not going to accept “trust us” as agents touch source code, secrets, proprietary data, and regulated workflows. This also means when a company finds a governance option that fits into their workflow, it will have to run as part of multiple agent workflows.
A clear procurement pattern is emerging with App > Agent > Workspace becoming three distinct buying decisions rather than a single bundled purchase. In other words, enterprises care most about having control of where the agent runs, what guardrails it follows, and the infrastructure that makes it secure and reliable).
Where the real money will be made
AI tools (like ChatGPT or GitHub Copilot) at the application layer mostly make money from human interaction time, which includes developers typing prompts, asking questions, writing code, and reviewing suggestions. That’s useful, but it’s limited. People go to bed. People switch contexts. The productivity is limited to when the human is online, working.
With machines running all the time, the workspace infrastructure layer makes money from something else entirely. Agentic development can plan tasks, run tests, open PRs, refactor code, scan for vulnerabilities, make documentation, fix dependencies, and instrument systems all on its own. It runs all the time and grows with computing power, not with people. The productivity impact will shift to multiple agents running all the time.
If a single developer effectively coordinates 10 agents, one handling code generation, another managing tests, another doing dependency analysis, and another performing security scanning, the infrastructure requirement explodes. Each agent needs a dedicated mix of:
• Compute (often GPU-backed inference, plus CPU for build/test loops)
• Storage and indexing (codebase embeddings, retrieval layers, logs, artifacts)
• Governance overhead (audit trails, policy enforcement, role-based access)
• Execution environment (sandboxes, containers, reproducibility, isolation)
Even if each agent is ‘cheap’ on paper, the system-level cost becomes meaningful at scale. The enterprise isn’t paying for a chatbot subscription anymore. Instead, it’s paying for an always-on operational layer that looks more like a cloud platform than a SaaS tool.
And the market rewards that kind of infrastructure. Historically, infrastructure companies trade at higher-quality multiples because they have:
• Deep integration into workflows
• High switching costs
• Expanding usage-based economics
• Clear land-and-expand dynamics
This means durable, sustainable revenue at the largest, most dependable companies on the planet with the most critical, highly regulated security requirements.
Infrastructure winners are often the platforms enterprises standardise on. Think Snowflake, Databricks, and MongoDB. Agentic AI is creating the next version of that story, except the usage multiplier may be far more aggressive.
The enterprise buying pattern
CIOs and CTOs want platforms. That’s why self-hosted, governed deployments are becoming the most popular choice for businesses. During the agentic era, businesses are using autonomous execution, which raises the stakes around security, reliability and control. As a result, buyers are committing to infrastructure earlier, and investors should expect the infrastructure layer to become the main battleground sooner than anticipated.
The pattern is predictable:
• Step 1: Start with a dev team pilot. A small group proves the workflow gains and establishes internal champions. Word starts to spread.
• Step 2: Expand to adjacent technical teams. Data science, ML ops, security engineering, and platform engineering quickly demand access.
• Step 3: Centralise governance. Once multiple teams depend on agentic workflows, security, compliance, and IT standardise the environment. That’s where “platform budget” replaces “pilot budget.”
This is why vertical integration fails at enterprise scale. When one vendor tries to bundle everything, they collide with the enterprise’s need to mix and match across different models, compliance constraints, deployment patterns, security policies, and cloud environments.
What we often see in conversations with customers is that they experiment with multiple agents and attempt to integrate them with existing tooling, but then throw them out because of integration and usability issues – not the value of the agent but how easy or difficult it was toonboard.
Infrastructure wins long-term value because it is the neutral base that lets businesses switch models and tools while keeping the same operational layer.
And timing is important. 2025 was the year of experimentation, but in 2026, pilots will become platform mandates and the business will choose a default “agent workspace” as it did with cloud providers and data platforms.
Category definition advantage
The infrastructure provider that uses “AI Development Infrastructure” as a formal procurement line gains the first-mover advantage in enterprise mindshare. Once that happens, buyers stop thinking about individual agent products and start standardising on a governed infrastructure layer. They make checklists for evaluations, set internal standards, and train teams around the category leader.
That category leadership creates a flywheel:
• A partnership ecosystem forms around the standard
• Integrations become the default path for adjacent vendors
• Enterprises build internal workflows that assume the platform exists
• Switching costs rise through organisational adoption
This effect happens faster with open source. If the foundational layer can be changed and works with businesses, it becomes the language that everyone on the team speaks. It also makes a distribution engine where developers go first, then governance catches up, and finally procurement.
Creating categories is a way to get into the budget structure of a business. When the spending wave peaks, the first company to make “AI dev infrastructure” feel like a must-have will be the one that businesses choose to use.
The 2026 AI investment thesis
The next major enterprise value-creation cycle in AI will be driven by who owns the layer where autonomous agents operate.
In 2026, three tailwinds will come together: AI adoption velocity, enterprise governance requirements, and infrastructure disaggregation. The market size will become straightforward to frame:
• Number of developers × agent multiplier × infrastructure cost per workspace
Because agentic workflows run continuously, scale across organisations, and require real compute, storage, and governance, that equation gives a category measured in billions. The companies that own the workspace layer will win. A self-hosted, open, and governed infrastructure layer that businesses can use as a standard even as models and tools change.
If you want to understand where the durable value in agentic AI will show up, look past the applications themselves and focus on the infrastructure layer enterprises will standardise on. The companies that own the governed, scalable workspace where agents actually run will define the next wave of enterprise value creation for the next twenty plus years
