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Cloud computing has spent nearly two decades evolving around a relatively stable assumption: humans write software, humans decide how it should be deployed, and humans remain responsible for operating the infrastructure underneath it. Even as automation transformed development through containers, infrastructure as code, CI/CD and Kubernetes, the engineer remained at the center of the process. Artificial intelligence is beginning to challenge that model.

Coding assistants are rapidly evolving into autonomous engineering agents capable of doing far more than generating snippets of code. They can inspect repositories, modify applications, execute commands, run tests, analyze failures and increasingly interact with deployment infrastructure. As these systems become more capable, the industry is confronting a question that reaches far beyond the quality of AI-generated code: what should cloud infrastructure look like when software is increasingly created and operated by machines?

That question is central to a discussion between Sean Falconer and Anurag Goel, founder and CEO of⁠Render, published by Software Engineering Daily. The conversation explores how AI-generated software could reshape cloud architecture, developer platforms and the infrastructure abstractions that have dominated the industry for years.

Today’s cloud platforms carry the fingerprints of the humans who operate them. Developers write applications, platform engineers define deployment environments, DevOps teams configure infrastructure and site reliability engineers investigate failures. Kubernetes, Terraform and automated deployment pipelines have removed enormous amounts of manual work, but they still largely execute architectures and policies established by people.

AI agents introduce a different kind of infrastructure consumer. An autonomous system does not necessarily need dashboards, carefully arranged menus or graphical configuration tools. It can communicate directly with APIs, command-line interfaces and machine-readable infrastructure services. More importantly, it can perform those interactions continuously and at a speed that would be impossible for a human operator.

A sufficiently capable coding agent could receive a requirement, modify an application, provision a database, create an isolated environment, deploy the new version, run tests, inspect logs and destroy the environment when the experiment is finished. If the implementation fails, the process could begin again seconds later with a different approach.

Infrastructure designed around occasional human decisions starts to look very different when those decisions occur thousands of times automatically.

The acceleration of software production also creates a security problem that cannot be separated from infrastructure design. Traditional development assumes that production code passes through several layers of scrutiny. Engineers write it, colleagues review it, automated systems test it and security controls analyze it before deployment.

AI dramatically increases the volume of code that can move through that process. An autonomous agent can generate hundreds or thousands of lines in minutes, modify dependencies, create configuration files and execute commands while pursuing a broader objective. Human review cannot necessarily scale at the same rate.

This makes isolation increasingly important. AI-generated software may need to be treated as untrusted until it has demonstrated that it behaves as expected. Instead of allowing an agent to experiment directly inside persistent development or production infrastructure, platforms can create disposable environments where generated code can execute without exposing sensitive systems.

The concept is familiar—sandboxing has existed for decades—but AI changes its potential scale. Rather than creating an occasional sandbox for suspicious software, future development platforms may generate enormous numbers of short-lived execution environments as a normal part of the engineering lifecycle.

An agent might create an environment, deploy a proposed fix, execute tests, inspect the results and destroy everything within minutes. Another environment could immediately replace it. Infrastructure that once existed for months or years increasingly becomes temporary by design.

This shift also challenges one of the defining characteristics of modern cloud engineering: complexity.

Kubernetes gave organizations extraordinary flexibility in how they deploy and operate applications. It also introduced a substantial operational burden. Many large companies eventually created internal platforms specifically to shield developers from that complexity, building layers for deployment, networking, observability, security, secrets, databases and service discovery.

In effect, engineering organizations repeatedly construct simpler platforms on top of increasingly sophisticated infrastructure.

That tradeoff becomes more important when AI can generate applications faster than organizations can deploy them. If an agent can build a functional service in minutes but requires hours of configuration before that service can run safely, infrastructure becomes the limiting factor.

The natural response is to move upward in abstraction.

Instead of requiring an agent to understand containers, ingress controllers, networking policies, load balancers, storage classes and autoscaling rules, a platform can expose a much simpler instruction: deploy this application under these constraints. The platform determines how the underlying infrastructure should satisfy that request.

This does not necessarily mean Kubernetes disappears. More likely, it becomes increasingly invisible. The same pattern has already occurred throughout computing: developers gradually stopped thinking about physical servers, many stopped managing virtual machines directly, and higher-level platforms increasingly hide container orchestration as well. Agentic development could push that abstraction boundary further.

The rise of AI agents may also change what constitutes a good cloud developer experience. Traditional platforms invest heavily in dashboards, documentation, configuration interfaces and visual tools because their users are human engineers. Autonomous agents have different requirements.

They need deterministic APIs, structured responses, predictable behavior and clearly defined permissions. An elegant dashboard provides little value to an agent if the same operation cannot be performed reliably through an API.

This creates the possibility of cloud platforms being designed simultaneously for two very different audiences. Humans may continue to use visual interfaces to understand systems, define policies and supervise operations, while agents interact with the same infrastructure through machine-oriented control surfaces.

In that environment, API quality becomes more than a developer convenience. It becomes part of the fundamental architecture through which autonomous software interacts with computing resources.

Giving AI systems direct access to infrastructure inevitably introduces risk. An incorrectly constrained agent could expose a service to the internet, delete a database, generate unexpected cloud costs, leak credentials or modify production resources while attempting to complete an otherwise legitimate task.

Agent-friendly infrastructure therefore cannot simply mean giving models broader permissions. It requires building stronger boundaries around what those models are allowed to do.

Short-lived credentials are likely to become increasingly important. Temporary environments should receive temporary identities, production access should remain separate from experimentation, network communication should be restricted by default and resource consumption should operate within explicit limits. Destructive actions may require additional authorization, while significant infrastructure changes should remain fully auditable.

The principle resembles zero trust, but applied to autonomous software engineering. An agent receives enough authority to complete the current task, rather than inheriting the broad privileges of the developer or platform account supervising it.

This distinction matters because AI agents are fundamentally different from conventional automation. A deployment script executes a sequence of operations explicitly defined by engineers. An agent receives an objective and determines how to achieve it. That flexibility makes autonomous systems powerful, but it also means infrastructure must be prepared for actions that were not individually anticipated by the people who designed the workflow.

AI agents will not only deploy applications; they will increasingly be expected to understand why those applications fail. That makes observability another area likely to evolve.

Today’s monitoring systems were largely designed to present information to engineers through dashboards containing logs, metrics and distributed traces. Autonomous engineering requires the same information to be accessible programmatically and presented with enough context for machines to reason about it.

An agent investigating a failed deployment needs to determine which service generated an error, what configuration changed, whether memory or CPU consumption increased and what happened immediately before the failure. It then needs to connect that operational evidence to the source code it controls.

Observability consequently becomes part of a continuous machine feedback loop. Code generates infrastructure behavior, telemetry describes that behavior and the agent uses the telemetry to modify the code. The boundary between software development and operations becomes increasingly difficult to identify.

Traditional software engineering is organized into recognizable stages: implementation, review, testing, deployment, observation and debugging. AI agents can increasingly move through several of those stages without waiting for human intervention.

A future engineering workflow could begin with a developer describing an objective and defining its constraints. An agent generates the implementation, creates an environment, deploys the application, runs automated tests, examines telemetry, discovers a failure, modifies the code and repeats the process until the defined acceptance criteria are satisfied.

Human engineers do not necessarily disappear from this process. Their role moves upward. They become increasingly responsible for architecture, requirements, constraints, security policies and evaluating whether the resulting system actually solves the intended problem.

This could prove to be one of the more consequential changes introduced by AI coding systems. The important shift is not simply that developers type less code, but that implementation itself becomes part of a larger automated loop.

The economics of infrastructure could change alongside these engineering practices. If software becomes significantly cheaper to produce, considerably more software is likely to be created. Every autonomous agent may also generate multiple temporary environments while building that software.

A single feature could involve dozens of experimental deployments, temporary databases, test services and isolated sandboxes before anything reaches production. Multiplied across thousands of agents, this creates a new class of cloud workload characterized by extreme churn.

These workloads may exist for seconds or minutes rather than months. They need to start quickly, remain strongly isolated and disappear just as efficiently. Traditional cloud economics built around relatively persistent virtual machines and long-running applications may therefore give way to infrastructure optimized for ephemeral computation.

The ability to create and destroy secure computing environments almost as easily as an AI model generates another block of code could become an important competitive advantage for cloud platforms.

The broader implication is that AI agents represent something more consequential than another developer tool. They are emerging as a new category of infrastructure user.

They communicate through APIs, operate continuously, generate potentially untrusted software, consume infrastructure at machine speed and increasingly participate in the full lifecycle of an application. Cloud platforms designed primarily to help humans manage software may therefore need to evolve into environments where machines can safely manage software under human-defined constraints.

That evolution favors higher-level abstractions, rapid provisioning, ephemeral environments, machine-readable observability, short-lived identities and significantly stronger isolation. It also favors platforms capable of hiding infrastructure complexity without eliminating the controls organizations need for security and governance.

Cloud computing has repeatedly moved upward through layers of abstraction. Physical servers gave way to virtual machines, virtual machines were increasingly replaced by containers, and containers produced orchestration systems such as Kubernetes. Developer platforms subsequently emerged to hide much of that orchestration complexity.

AI agents may create the pressure for another transition.

The defining infrastructure question of the next generation may therefore not be whether AI can write production-quality software. The more consequential question is whether the cloud can safely accommodate a world in which software is generated, tested, deployed, observed and repaired at machine speed—and where the entity performing much of that work is no longer a human engineer.