The Future of Software: AI Agents That Build, Deploy, and Maintain Applications

We're in the middle of a shift that happens once per generation in software. The first shift was from mainframes to personal computers. The second was from desktop software to cloud services. The third - happening right now - is from human-written software to agent-built software.

Most people think AI agents are just better autocomplete for code. They're not. They're a fundamental change in how software gets created, deployed, and maintained. And it requires entirely new infrastructure to support.

Where We Are Today

The Copilot Era (2022-2024)

AI suggests code completions as you type. You're still the driver - the AI is riding shotgun. It makes you faster, but you're still writing every feature, debugging every bug, deploying every release.

The Agent Era (2025-present)

AI agents act autonomously. You describe what you want, and the agent writes the code, runs tests, deploys it, and monitors the result. You review and approve, but you're no longer writing every line.

Today's agents:

• Claude Code writes full features from descriptions
• Cursor Composer generates multi-file changes
• OpenClaw orchestrates multiple agents for complex tasks
• GPT-o models reason through architectural decisions

These agents are impressive but limited. They work inside a terminal or IDE. They don't have persistent state. They don't manage infrastructure. They need a human to click "deploy."

Where We're Going

Phase 1: Build Agents (Now)

Agents that write code. You describe a feature, the agent implements it. Still needs human review and deployment.

Infrastructure need: Isolated environments where agents can write and test code safely.

Phase 2: Ship Agents (2025-2026)

Agents that write, test, AND deploy code. You describe a feature, review the PR, and the agent handles everything from merge to production deployment.

Infrastructure need: Automated deployment pipelines that agents can trigger. Preview environments for every PR. Rollback capabilities.

Phase 3: Operate Agents (2026-2027)

Agents that monitor production, detect issues, and fix them. An agent notices elevated error rates, investigates the cause, writes a fix, tests it in a staging environment, deploys a canary, and rolls out to production. The on-call engineer gets a notification: "Error rate spike detected. Root cause: null pointer in PaymentService.processRefund(). Fix deployed. Error rate returning to baseline."

Infrastructure need: Per-agent monitoring environments. Safe staging/canary infrastructure. Audit trails for every agent action. The ability to contain and rollback any change.

Phase 4: Architect Agents (2027+)

Agents that design systems. You describe a business need ("We need to support 10x more concurrent users"), and the agent analyzes the current architecture, identifies bottlenecks, designs a migration plan, and executes it. Might take hours or days, involving multiple specialist agents collaborating.

Infrastructure need: Long-running agent environments with persistent state. Multi-agent collaboration with private networking. Resource management for complex, multi-day tasks.

What This Means for Infrastructure

Every phase requires something traditional cloud doesn't provide:

Ephemeral, disposable environments

Agents need to spin up environments, try things, and throw them away. "Let me test this migration against a copy of the database" - that's a workspace created for 5 minutes and deleted. Current cloud: spin up an RDS instance (10 minutes), test, delete (10 minutes). Future: create an isolated database workspace (130ms), test, delete (instant).

Secure by default

Agents executing code autonomously is a security nightmare if the environment isn't isolated. You can't review every command an agent runs in real-time. The environment must be safe no matter what the agent does.

Persistent agent state

Operate and Architect agents work on timescales of hours to days. They need persistent storage, long-running processes, and the ability to resume after interruptions. Lambda's 15-minute limit doesn't work. Spot instances that can be terminated don't work.

Agent-to-agent networking

Build agents collaborate with test agents. Deploy agents coordinate with monitoring agents. Archive agents talk to database agents. These need private, secure networking - not internet-facing, not shared infrastructure.

Identity and permissions

Each agent needs its own identity with specific permissions. The code-writing agent can modify files but can't deploy. The deploy agent can update production but can't change code. The monitoring agent can read logs but can't modify anything. Like IAM, but for agents, and much simpler.

The Infrastructure Layers

┌─────────────────────────────────────┐
│  Application Layer                   │
│  (Your AI product / SaaS)           │
├─────────────────────────────────────┤
│  Agent Framework Layer               │
│  (LangChain, CrewAI, OpenClaw)      │
├─────────────────────────────────────┤
│  Workspace Layer (Oblien)           │
│  - Per-agent isolated environments   │
│  - Encrypted storage                 │
│  - Private networking                │
│  - Auto-scaling                      │
│  - Lifecycle management              │
├─────────────────────────────────────┤
│  Hardware Layer                      │
│  (Firecracker microVMs / KVM)       │
└─────────────────────────────────────┘

The workspace layer is the missing piece in most AI architectures today. Agent frameworks handle the "thinking" - deciding what to do. The workspace layer handles the "doing" - safe execution of the agent's decisions.

What Developers Will Do

Software developers aren't going away. Their role is shifting:

Today

Write code → Test code → Debug code → Deploy code → Monitor code. Most time spent in the write-test-debug loop.

Near future

Describe intent → Review AI-generated code → Approve deployment → Handle edge cases. Most time spent on review and specification.

Further future

Define business requirements → Set guardrails and policies → Review agent decisions → Handle novel situations. Most time spent on system design and oversight.

The job becomes more like an architect and less like a builder. You define what the building should do. Agents figure out how to build it.

The Companies That Will Win

Two types of companies benefit from this shift:

Type 1: AI-native SaaS

Companies building products powered by AI agents. Code generation platforms (Lovable, Bolt, v0), AI coding assistants, automated testing tools, AI-powered DevOps.

These companies need workspace infrastructure from day one. Their product IS agents running code.

Type 2: Agent-augmented companies

Traditional companies that adopt agents for internal operations. Engineering teams using AI for code review. Customer support using AI for automated resolution. Marketing using AI for content generation.

These companies adopt agents gradually but eventually need the same infrastructure - isolated environments, secure execution, scalable workspaces.

What Needs to Be True

For this future to work, several things need to be true:

1. VM boot times must be sub-second - disposable environments need to feel instant. ✅ (Firecracker achieves this today)

2. Cost must approach zero when idle - the economics of per-agent VMs only work if idle infrastructure doesn't cost money. ✅ (pause-resume solves this)

3. APIs must be agent-friendly - agents can't configure YAML or click buttons. They need simple programmatic APIs. ✅ (SDK-first design)

4. Security must be default, not configured - autonomous agents will exploit any misconfiguration. Secure-by-default is mandatory. ✅ (microVM + encryption + zero-trust)

5. AI models must keep improving - agents need to be reliable enough to trust with deployment and operations. 🔄 (improving quarterly)

Infrastructure (1-4) is ready today. AI capabilities (5) are catching up fast. The gap between "agents that can write code" and "agents that can ship code" is shrinking every quarter.

Getting Ready

Whether you're building AI products or adopting agents internally:

1. Start with isolated environments - don't run agents on shared infrastructure. The security risk isn't worth it.

2. Design for agent-native workflows - APIs, not GUIs. Programmatic, not manual. Disposable, not persistent (unless the agent needs persistence).

3. Build guardrails, not walls - agents should have freedom within boundaries. A sandbox with full capability inside, zero capability outside.

4. Plan for multi-agent systems - the future isn't one big agent. It's many specialist agents collaborating. Your infrastructure needs to support private networking between agent workspaces.

5. Measure everything - track what agents do, how long they take, what they cost, and whether their outputs are correct. Observability for agents is as important as it is for servers.

The companies that figure out agent infrastructure early will have a massive advantage. Not because the AI models are proprietary - everyone has access to the same models. The advantage is in how you run those models safely, efficiently, and at scale.

Learn more → Oblien Documentation | Architecture Patterns for AI Agents