I’m not normally fond of year-end technology retrospectives, but 2025 was indeed a year of quantum leaps in the art of the possible and it has filled us all with measured optimism paired with some healthy and well-earned skepticism where AI is concerned. When I put architecture in perspective, I’m inclined to take a longer view of automation in all its variations over a decade. That’s why 2025 feels more like a footnote in a long series of events culminating in the perfect storm of opportunities we’ve been contemplating for some time now.
The composable infrastructure revolution
We’ve been moving toward self-aware, composable infrastructure in architecture for a while now and infrastructure-as-code was merely the first major inflection.
Let’s be honest, the old way of building IT infrastructure is breaking down. As an enterprise architect, the vicious cycle is very familiar. Tying agentic architecture demand-patterns to legacy infrastructure without careful consideration is fraught with peril. The old pattern is really predictable now: You provision systems, maintain them reactively and eventually retire them. Rinse and repeat.
That model is now officially unsustainable in the age of AI. What’s taking its place? Composable and intelligent infrastructure that can proactively self-assemble, reconfigure and optimize on the fly to match what the business needs.
For IT leaders, this shift from rigid systems to modular, agent-driven infrastructure is both a breakthrough opportunity and a serious transformation challenge. And the numbers back this up: the global composable infrastructure market sits at USD $8.3 billion in 2025 and is projected to grow at 24.9% annually through 2032.
What’s driving this hyper-accelerated growth? Geopolitical disruptions, supply chain chaos and AI advances are reshaping how and where companies operate. Business environments are being driven by reactive and dynamic agentic experiences, transactions and digital partnerships everywhere, all the time. Static infrastructure just can’t deliver that kind of flexibility based on marketing exercises that describe solution offerings as “on-demand,” “utility-based,” “adaptive” and “composable.” These are little more than half-truths.
A 2025 Forrester study commissioned by Microsoft found that 84% of IT leaders want solutions that consolidate edge and cloud operations across systems, sites and teams. As an architect in the consumer goods space, I found that our IT team would produce endless slide decks about composable enterprises ad nauseam, but infrastructure-as-code was the level of actual capability for some time.
Leaders wanted composable architecture that can pull together diverse components without hyperextended interoperability efforts. IBM’s research reinforces this, showing that companies with modular architectures are more agile, more resilient and faster to market — while also reducing the technical debt that slows everyone down.
The problem has been one of capacity and fitness for purpose. Legacy infrastructure and the underlying systems of record simply weren’t designed with agentic AI patterns in mind. My conversations with pan-industry architecture colleagues reflect the same crisis of expectation and resilience around agentic architectures.
Consider McKinsey’s 2025 AI survey that demonstrated 88% of organizations now use AI regularly in at least one business function and 62% are experimenting with AI agents. But most are stuck in pilot mode because their infrastructure can’t scale AI across the business.
If there are any winners in this race, they’ve broken apart their monolithic systems into modular pieces that AI agents can orchestrate based on what’s actually happening in real time.
AI agents: The new orchestration layer
So, what’s driving this shift? Agentic AI — systems that understand business context, figure out optimal configurations and execute complex workflows by pulling together infrastructure components on demand. This isn’t just standard automation following rigid, brittle scripts. Agents reason about what to assemble, how to configure it and when to reconfigure as conditions change.
The adoption curve is steep. BCG and MIT Sloan Management Review found that 35% of organizations already use agentic AI, with another 44% planning to jump in soon. The World Economic Forum reports 82% of executives plan to adopt AI agents within three years. McKinsey’s abovementioned State of AI research further highlights agentic AI as an emerging focus area for enterprise investment and describes AI agents as systems that can plan, take actions and orchestrate multi-step workflows with less human intervention than traditional automation.
As McKinsey puts it: “We’re entering an era where enterprise productivity is no longer just accelerated by AI — it’s orchestrated by it.” That’s a fundamental change in how infrastructure works.
IBM is betting big on this future, stating that “the future of IT operations is autonomous, policy-driven and hybrid by design.” They’re building environments where AI agents can orchestrate everything — public cloud, private infrastructure, on-premises systems, edge deployments — assembling optimal configurations for specific workloads and contexts. The scope of automation ranges from helpful recommendations to closed-loop fixes to fully autonomous optimization.
What composable architecture actually looks like
I recall no shortage of Lego-induced architecture references to composability over the last decade. Sadly, we conflated them with domain services and not how business capabilities and automation could and should inform how the Legos are pieced together to solve problems. Traditional infrastructure comes as tightly integrated stacks — hard to decompose, inflexible and reactive. The new composable model flips this, offering modular building blocks that agents can intelligently assemble and reassemble dynamically based on what’s needed right now.
Composability demands modularity and responsive automation
The foundation is extreme modularity — breaking monolithic systems into discrete, independently deployable pieces with clean interfaces. Composable infrastructure lets you dynamically assemble and disassemble resources based on application demands, optimizing how pooled resources get allocated and improving overall efficiency.
This goes far beyond physical infrastructure to include services, data pipelines, security policies and workflows. When everything is modular and API-accessible, agents can compose complex solutions from simple building blocks and adapt in real time.
Bringing cloud and edge together
Enterprise organizations are no longer treating cloud and edge as separate worlds requiring manual integration. The new approach treats all infrastructure — from hyperscale data centers to network edge — as a unified resource pool that agents can compose into optimal configurations.
McKinsey identifies edge-cloud convergence as essential for agentic AI: “Agents need real-time data access and low-latency environments. Combining edge compute (for inference and responsiveness) with cloud-scale training and storage is essential.” They further highlight how Hewlett Packard Enterprise (HPE) expanded its GreenLake platform in late 2024 with composable infrastructure hardware for hybrid and AI-driven workloads — modular servers and storage that let enterprises dynamically allocate resources based on real-time demand.
Agents running the show
Even IBM with its storied fixed-infrastructure history is all-in on agentic AI infrastructure capabilities — including agents and Model Context Protocol (MCP) servers — across its portfolio, making infrastructure components discoverable and composable by AI agents. These agents don’t just watch the infrastructure state; they actively orchestrate resources across enterprise data and applications, creating optimal configurations for specific workloads.
Management interfaces across IBM cloud, storage, power and Z platforms are becoming MCP-compatible services — turning infrastructure into building blocks that agents can reason about and orchestrate. Vendor-native agentic management solutions introduced similar AI-driven orchestration enhancements in 2024, letting large enterprises dynamically allocate resources across compute, storage and networking.
Self-aware and self-correcting infrastructure
Instead of manually configuring every component, composable architectures enable intent-based interfaces. You specify business objectives — support 10,000 concurrent users with sub-100ms latency at 99.99% availability — and agents figure out the infrastructure composition to make it happen.
Emerging intelligent infrastructure player Quali describes this as “infrastructure that understands itself” — systems where agentic AI doesn’t just demand infrastructure that keeps up, but infrastructure built from composable components that agents can understand and orchestrate.
Getting scale and flexibility in real time
Traditional infrastructure forces a choice: optimize for scale or build for adaptability. As architects, there are clear opposing trade-offs we must navigate successfully: Scale relative to adaptability, investment versus sustaining operations, tight oversight versus autonomy and process refactoring versus process reinvention.
Composable architectures solve this by delivering both. The dual nature of agentic AI — part tool, part human-like — doesn’t fit traditional management frameworks. People are flexible but don’t scale. Tools scale but can’t adapt. Agentic AI on composable infrastructure gives you scalable adaptability — handling massive workloads while continuously reconfiguring for changing contexts.
Self-composability and evolved governance
Agent-orchestrated infrastructure demands governance that balances autonomy with control. The earlier-mentioned MIT Sloan Management Review and BCG study found that most agentic AI leaders anticipate significant changes to governance and decision rights as they adopt agentic AI. They recommend creating governance hubs with enterprise-wide guardrails and dynamic decision rights rather than approving individual AI decisions one by one.
The answer lies in policy-based composition, defining constraints that bound agent decisions without prescribing exact configurations. Within those boundaries, agents compose and recompose infrastructure autonomously.
When AI agents continuously compose and recompose resources, you need governance frameworks that look nothing like traditional change management. A model registry that includes MCP connects different large language models while implementing guardrails for analytics, security, privacy and compliance. This treats AI as an agent whose decisions must be understood, managed and learned from — not as an infallible tool.
Making it happen in 2026
What should IT leaders do? Here are the most critical moves from my perspective.
Redesign work around agents first. Use agentic AI’s capacity to implement scalability and adapt broadly within parameterized governance automation rather than automating isolated tasks. Almost two-thirds of agentic AI leaders expect operating model changes. Build workflows that shift smoothly between efficiency and problem-solving modes.
Rethink roles for human-agent collaboration. Agents are an architect’s new partner. Reposition your role as an architect in the enterprise to adopt and embrace portfolios of AI agents to coordinate workflows, and traditional management layers change. Expect fewer middle management layers, with managers evolving to orchestrate hybrid human-AI teams. Consider dual career paths for generalist orchestrators and AI-augmented specialists.
Keep investments tied to value. Agentic AI leaders anchor investments to value — whether efficiency, innovation, revenue growth or some combination. Agentic systems are evolving from finite function agents to multi-agent collaborators, from narrow to broadly orchestrated tasks and other ecosystems and agents, and from operational to strategic human-mediated partnership.
The bottom line
The companies that will win in the next decade will recognize composability as the foundation of adaptive infrastructure. When every part of the technology stack becomes a modular building block and intelligent agents compose those blocks into optimal configurations based on real-time context, infrastructure becomes a competitive advantage instead of a constraint.
Organizations that understand agentic AI’s dual nature and align their processes, governance, talent and investments accordingly will realize its full business value. My architect’s perspective is that agentic AI will challenge established management approaches and, yes, even convince many of its ability to defy gravity. But with the right strategy and execution, it won’t just offer empty promises — it will deliver results. Further, our grounded expectations around the capacity of aging infrastructure and legacy demand patterns must guide us in ensuring we make intelligent decisions.
The question isn’t whether to embrace composable, agent-orchestrated infrastructure. It’s how fast you can decompose monolithic systems, build orchestration capabilities and establish the governance to make it work.
This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time, as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects.
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