When one person can orchestrate agents in natural language to generate code, debug, deploy and operate software, the protagonist of software production is changing.
At WAIC 2026, Wang Weijia, product lead of Tencent Cloud CloudBase, shared a judgment: in the agent era, the cloud's value isn't just providing resources — it must become a software production line suited to how agents work.
In the past, the cloud mainly served professional developers. Writing code, configuring environments, connecting databases, deploying, troubleshooting — every step depended on manual effort.
Today, more and more "super individuals" are emerging: they don't have to complete every engineering task themselves. Instead, they define goals, orchestrate agents, and use a set of cloud infrastructure to turn ideas into real, runnable, deliverable applications.
That means the cloud needs to be redesigned.
The software producer has changed, so the infrastructure must change too
From Lovable and Vercel v0 to Replit Agent, vibe coding has moved from a tech demo to a scaled product.
But the crux of this competition is no longer "who generates prettier pages" or "whose code generation is faster". The real challenge is whether agents can string together code, data, identity, storage, deployment and operations to deliver a genuinely usable application.
Code generation is getting faster, but integrating real systems is still complex.
How do you create a database? Wire up authentication? Store files? Configure permissions? Locate and roll back after a failed release? These capabilities were mostly designed for humans — operate through a console, understand through docs, debug through vague error messages.
For an agent, these steps generate a large amount of "infrastructure noise".
So the second half of AI programming isn't just about making agents smarter — it's about making their working environment clearer, more readable, more executable and more verifiable.
From prompt to loop: getting agents into real production
Getting an agent to generate code once isn't hard. Getting it to complete tasks reliably in a real production environment is the bigger engineering challenge.
This requires understanding agent engineering across four layers:
- Prompt Engineering: solving "how to ask";
- Context Engineering: solving "what the agent should know";
- Harness Engineering: solving "how to constrain the agent";
- Loop Engineering: solving "how to keep the agent running autonomously".
Of these, Loop Engineering closes the full loop on a task: trigger, goal, action, verification, memory. An agent no longer just answers questions — it advances continuously around a goal and corrects itself based on environment feedback.
Harness Engineering adds production-grade guardrails to this loop: architecture boundaries, least privilege, tests, type checks, CI, log auditing, and necessary human approval.
Both point to one principle: every agent action should get feedback in a real runtime environment, and iterate on that feedback.
The agent era needs an AI-native cloud
A traditional cloud platform's core interaction subject is human: the console is for humans to operate, the docs are for humans to read, the errors are for humans to debug.
An AI-native cloud's core interaction subject is the agent. It must provide standardized tool calls, structured context, machine-readable errors, secure sandboxes, serverless resources and observable feedback.
In other words, the cloud is shifting from a "human-machine interface" into an agent's working environment.
For an agent, a suitable cloud should have four traits:
- Readable: let the agent understand resources, configuration, permissions and state;
- Executable: let the agent operate cloud resources through standard tools;
- Verifiable: let the agent check results, locate problems and keep correcting;
- Controllable: keep automation within bounds through sandboxes, permissions and auditing.
CloudBase: making cloud capability the agent's language
Around the AI-native cloud, CloudBase is reorganizing serverless, BaaS, hosting, multi-tenancy, permissions and billing into infrastructure better suited for agent invocation.
For developers, CloudBase lowers the barrier to operating cloud resources through MCP, Skills and Plugins.
CloudBase MCP wraps database, authentication, storage, cloud functions, logs, domain, security, deployment and rollback into tools callable through standard protocols. Agents can understand and operate cloud capabilities directly, without jumping around complex console flows.
CloudBase Skills distills mini program development, authentication, WeChat Pay, database calls, cloud function authoring, LLM orchestration and permission control into domain knowledge agents can call.
Through Plugins in coding environments like Claude Code and Codex, developers can use these capabilities directly in their daily workflow.
The goal is clear: shorten the agent's execution loop, cut wasted tool calls and token consumption, and let the agent shift its attention from operational details back to business goals.
In a controlled test generating and deploying a Todo app, compared with the traditional VM approach, CloudBase cut completion time from 990 seconds to 260 seconds, tool calls from 79 to 36, and token consumption by 52%. At the same time, the security exposure surface narrowed from SSH to HTTPS API.
Infrastructure adapted to agents brings not just faster delivery, but a shorter, more stable, more secure production loop.
From developer efficiency to vibe-coding platform capability
The agent era's challenges aren't limited to individual developers.
When a vibe-coding platform offers "generate an app" to a large number of users, multi-tenant isolation, permission management, resource quotas, lifecycle, cost accounting and runtime safety all become foundational questions for whether the platform can scale.
If these capabilities are patched together ad hoc at the application layer, the platform quickly spirals into complexity and cost.
CloudBase's approach is to push these capabilities down to the infrastructure layer:
- Give each user or project an independent environment;
- Isolate the control plane from the runtime with sandboxes;
- Support elastic scaling and pay-as-you-go with serverless;
- Complete resource quotas, cost accounting, runtime monitoring, backup and teardown at the tenant level.
This makes "user-generated apps" more than a one-off code output — they become isolated, operable, billable, continuously evolving cloud resources.
On CloudBase, a batch of such products has already grown: Tencent Tusi lets users generate installable apps with a single sentence; WorkBuddy helps product managers, founders and independent developers produce deliverable full-stack projects. They look like different products on the surface, but fundamentally they're all pushing the same thing: letting one person orchestrate agents and cloud infrastructure to complete a software production process that once required a small team.
The cloud is becoming the agent's production line
The biggest change the electricity revolution brought wasn't swapping steam engines for electric motors — it was redesigning the entire production line around electricity.
The same is true of AI.
If you just drop agents into the traditional cloud console and ops workflow, the efficiency gains are easily cancelled out by environment noise. The real opportunity lies in reconstructing the cloud around how agents work: tools callable, context understandable, permissions controllable, results verifiable, resources elastically provisioned.
In the future, the cloud will no longer be just a platform where engineers rent resources.
It will become the production line where agents generate, run, verify, publish and maintain software.
And the meaning of the "super individual" isn't one person doing everything — it's one person orchestrating a set of agents and a full stack of cloud infrastructure to turn their ideas into real-world products and services quickly.
Redesigning the cloud for the super individual is redesigning software productivity for the agent era.
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