The global technology stack is quietly shifting from cloud‑native to AI‑native, and ultimately agent‑native. For executive teams, this is less a matter of collecting new acronyms and more a question of how their organisations will work, decide and compete over the next decade. This evolution represents a fundamental shift in cloud infrastructure: from managing computing power at scale to managing and orchestrating intelligence at scale.From plumbing to behaviour To put it simply, cloud‑native was about plumbing: elastic infrastructure, containers and microservices that made software cheaper to scale and easier to ship. AI‑native marks a different kind of change—applications designed with models at the centre, not as decorative features glued onto legacy stacks. In AI‑native products, models are not “features”; they are the decision engines that interpret messy data, personalise experiences and automate judgments, from credit decisions to supply-chain forecasts. That is why AI‑native platforms increasingly resemble operating systems for intelligence: they integrate models, data, feedback loops and human oversight into a single fabric that learns continuously.For most enterprises, this phase has already begun in pockets—pilots in customer support, risk, marketing, and internal productivity tools—long before anyone updated the strategy slides.The agent-native inflection Agent‑native goes further still. In this new paradigm, Intelligent agents are emerging as a new class of machine user alongside engineers and applications. Traditional cloud interfaces designed for human clicks are being rewritten into standardized skills and command-line tools that agents can natively understand and execute. Autonomous agents combine language models, memory and tool use to plan and execute multi‑step workflows across enterprise systems, with humans in the loop for supervision and judgment. McKinsey’s work on “the agentic organization” describes this as a new operating paradigm in which humans and AI agents work side by side at near‑zero marginal cost, reshaping the foundations of business models, operating models and governance.Gartner, meanwhile, expects AI agents to move from niche experiments to standard infrastructure within this decade. One forecast suggests that by 2026, 40% of enterprise applications will integrate task‑specific AI agents, up from less than 5% in 2025. Another projects that average agent counts per large company could expand from around 15 in 2025 to tens of thousands by 2028, creating what they describe as “armies of digital workers” as well as a new class of governance challenges.Why this matters to C‑level leaders First, the nature of “digital transformation” is changing from “systems of record” to “systems of action”. Cloud-native modernised plumbing; AI‑native and agent-native modernise behaviour, determining who (or what) makes which decisions at what speed.In practical terms, agentic AI compresses cycle times between sensing, deciding and acting across the business—from detecting fraud to launching micro-campaigns and rerouting inventory. Companies that implement these loops early are not merely more efficient; they become structurally faster organisations, able to adjust strategy through software rather than reorganisations.Second, the workforce model is quietly evolving. Some firms now talk about “onboarding non-human employees”: agents with email addresses, system credentials and defined responsibilities, supervised by humans. This raises thorny questions about accountability, audit, risk and culture—but it also opens the door to scaling capabilities without linear headcount growth.Third, the build‑versus‑buy calculus is shifting. As AI makes bespoke software and workflow automation dramatically cheaper to assemble, boards will revisit reliance on monolithic Software-as-a-Service (SaaS) for processes that can instead be expressed as intent and orchestrated by agents. CIOs and CFOs will find that the cost curve of “owning” key capabilities looks different when AI can generate, maintain and adapt large portions of the stack.Competitive advantage, quietly earned None of the analysts suggest that simply deploying more agents will guarantee success; indeed, Gartner warns of high failure rates for generative‑AI initiatives and emphasises the need for strong security and oversight as agents gain autonomy. Enterprise-grade agent-native clouds must therefore provide robust runtime sandboxes, identity management, and task-level security controls to safely govern these autonomous entities. McKinsey’s guidance is similarly cautious: the real gains show up only where organisations redesign workflows end‑to‑end to be AI‑first, embed human‑in‑the‑loop governance, and invest in culture and skills so that humans can work effectively with agents as colleagues rather than tools.The competitive advantage, then, lies less in any single model and more in organisational imagination and execution. Early adopters that “agentify” carefully chosen workflows, build an internal platform layer for agents, and reshape roles around hybrid teams are likely to move faster and learn more cheaply than peers that treat agents as isolated pilots.A humble agenda for the C‑suite This trend invites less bravado and more careful architecture. The temptation will be to declare an “agentic transformation programme”, but the wiser path is to treat agent-native as an operating thesis and move in measured steps.A practical agenda might include:Design for governance first. Put human‑in‑the‑loop checkpoints, audit trails and enterprise-grade identity and access controls at the heart of any agent roll‑out, rather than as compliance afterthoughts.Create an “agent platform” layer. Standardize how agents access data, tools and credentials, using secure orchestration frameworks rather than bespoke integrations scattered across departments.Treat speed of implementation as a competitive variable. Evidence from early adopters suggests that the advantage lies less in any single model and more in how quickly organisations can wire agents into real processes and iterate.None of this requires executives to become AI theorists. It does, however, require treating agents as organisational units—subject to the same discipline of strategy, risk, measurement and culture as human teams.No#AgenticAIDr FeiFei LiCTO and President of International BusinessAlibaba Cloud16 Oct, 2026