Coder Agents Enable Running AI Coding Workflows on Self-Hosted Infrastructure

"Coder Agents is a model-agnostic platform designed to let organizations run AI coding agents on their own infrastructure, rather than relying on cloud-based services. This allows teams to maintain full control over code, data, and execution environments. Coder Agents aims to break the tight coupling between agent tools and model providers, which often leads to vendor lock-in, by separating the infrastructure that runs the agents from the AI models they use."

"It provides an orchestration layer that allows teams to standardize workflows on a common platform while retaining the flexibility to choose and switch between models. Intelligence continues to come from the models, but how agents execute, how workspaces and compute are provisioned, and how behavior is controlled become consistent across the organization."

"Coder Agents provide a conversational interface and API for assigning tasks such as writing code, generating tests, or creating pull requests in the foreground or as background tasks. It centralizes control over model access, prompt management, execution policy, and observability. The API also enables more complex, automated workflows that can be triggered from systems like CI/CD pipelines, GitHub Actions, Slack, and other integrations."

"Coder CEO Rob Whiteley noted on LinkedIn that building an agent is not the hard part. Instead, the real complexity lies in running agents safely and reliably, which requires careful management of models, tools, repositories, dependencies, context, and guardrails. That's why we built Coder Agents. It solves running parallel agents with models of your choice and on the infrastructure of your choice."

Coder Agents is a model-agnostic platform for running AI coding agents on an organization’s own infrastructure instead of cloud services. It separates the infrastructure that executes agents from the AI models used, reducing tight coupling and vendor lock-in. The platform provides an orchestration layer that standardizes how agents run across teams while keeping flexibility to choose and switch models. It offers a conversational interface and an API for assigning tasks like writing code, generating tests, and creating pull requests as foreground or background jobs. Centralized control covers model access, prompt management, execution policy, and observability. The API supports automated workflows triggered by CI/CD, GitHub Actions, Slack, and other integrations, with emphasis on safe, reliable agent execution through guardrails and dependency management.

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