AI Agents in Production: Enterprise AI Harness Architecture on Kubernetes
An engineer published on Habr an architecture for running AI agents in production. Instead of writing a custom runtime (like Anthropic), the author assembled infrastructure from open components so that ordinary platform teams could deploy agents in Kubernetes. The reference architecture includes four functional layers with clear integration points between them.
AI-processed from Habr AI; edited by Hamidun News
An engineer has published a reference architecture for Enterprise AI Harness on Kubernetes on Habr. Instead of writing a custom runtime solution, the infrastructure was built from open-source components so that production agents could be deployed not only by Anthropic, but by any platform team.
Why are production agents a blind spot?
In the community, you can find dozens of materials and courses on how to design, train, or configure an AI agent. But far fewer people know how to safely, reliably, and predictably deploy it to production. Usually, such knowledge is locked behind corporate doors at companies like Anthropic, OpenAI, or Google, which have the resources and expertise to write their own runtime from scratch. This includes complex engineering questions: state management, fault tolerance, context isolation, monitoring, and security.
What layers are included in the architecture?
The author proposed a reference architecture broken down into four functional layers:
- Orchestration layer: managing the agent's lifecycle, scheduling tasks, handling errors and retry policies, managing request queues
- Integration layer: connecting to external services, REST APIs, tools and data stores
- Execution layer: actually running the agent, managing context and memory, calling LLM APIs
- Observability layer: logging all operations, performance metrics, distributed tracing, setting up alerts
Clear contract points are defined between layers, allowing individual components to be replaced without a complete system rewrite. This is critical for production: you cannot run the entire stack on a single implementation. For example, if an orchestration tool doesn't work for some reason, or if you need integration with an existing system, you can choose an alternative without rewriting the integration or execution layers.
Who will find this architecture useful?
The solution targets platform teams in large companies or ambitious startups that want to:
- Enable developers to independently deploy AI agents without having to write infrastructure from scratch
- Avoid lock-in to cloud services like Azure AI Services or AWS Bedrock
- Guarantee reliability, security, and predictability in production
The solution runs on Kubernetes—the industrial standard for containerization in enterprise. This means that most existing DevOps tools (CI/CD pipelines, network policies, backup strategies) will work out of the box without modification.
What does this mean
Production deployment of AI agents is transitioning from experimental mode to standard engineering practice. Open reference architectures and best practices lower the barrier to entry and accelerate implementation for teams that are not ready or willing to write their own runtime from scratch. This is especially important in a corporate context, where reliability and security compliance matter more than prototyping speed.
- Oct 24, 2026Lovable builds GPT-Live, Claude and Gemini Omni into its chat: 9 AI tools in one place
- Oct 24, 2026GPT-6.1 Sol in Devin Scores 60.4% on FrontierCode at $0.31 per Task
- Oct 24, 2026Math Advisers Say OpenAI's Flood of Proofs Broke the Field's Rules
- Oct 24, 2026Vercel Takes Python to GA and Launches Daemons for Background Workers
❓ Key Questions Answered
Why are production agents a blind spot?
In the community, you can find dozens of materials and courses on how to design, train, or configure an AI agent. But far fewer people know how to safely, reliably, and predictably deploy it to production. Usually, such knowledge is locked behind corporate doors at companies like Anthropic, OpenAI, or Google, which have the resources and expertise to write their own runtime from scratch. This includes complex engineering questions: state management, fault tolerance, context isolation, monitoring, and security.
What layers are included in the architecture?
The author proposed a reference architecture broken down into four functional layers: Orchestration layer: managing the agent's lifecycle, scheduling tasks, handling errors and retry policies, managing request queues Integration layer: connecting to external services, REST APIs, tools and data stores Execution layer: actually running the agent, managing context and memory, calling LLM APIs Observability layer: logging all operations, performance metrics, distributed tracing, setting up alerts Clear contract points are defined between layers, allowing individual components t
Who will find this architecture useful?
The solution targets platform teams in large companies or ambitious startups that want to: Enable developers to independently deploy AI agents without having to write infrastructure from scratch Avoid lock-in to cloud services like Azure AI Services or AWS Bedrock Guarantee reliability, security, and predictability in production The solution runs on Kubernetes—the industrial standard for containerization in enterprise. This means that most existing DevOps tools (CI/CD pipelines, network policies, backup strategies) will work out of the box without modification.
Delegate Deep Research, Coding & Workflows to an AI Agent
Deploy autonomous multi-step execution with NomadNet Agent (agent.nomadnet.ai) — automated web research, data synthesis, code generation, and complex task orchestration.
The AI world, distilled — once a week
Seven stories that actually mattered, hand-picked. No noise, no reposts, no press releases.
Done! Check your inbox for a confirmation.