Runtime acts as a centralized control plane for artificial intelligence coding agents, providing a secure, governed environment where these agents can perform software development tasks. By offloading agent execution from local developer machines to sandboxed cloud environments, Runtime addresses key enterprise challenges such as security, visibility, and cost management. It enables organizations to scale the use of AI agents across various departments, including engineering, product management, design, and finance, without compromising production safety.

Functionality is centered around mission control for AI coding agents. The platform handles the orchestration of ephemeral or persistent sessions, ensuring that agents operate with the necessary context, tools, and guardrails. It integrates with existing developer workflows—such as Slack, GitHub, and Linear—allowing team members to trigger agents via natural language requests or automated tickets. Once triggered, the agents function within isolated environments that mirror the organization's technical stack, allowing them to write, test, and ship code via pull requests while adhering to defined access policies.

Some of the key features are:

• Isolated Sandboxes: Every agent session runs in a dedicated, secure virtual machine that provides a clean filesystem, terminal access, and live preview capabilities.
• Centralized Guardrails: Administrators can define global policies, spend limits, allowlists, and approval gates to govern agent activities and maintain security standards.
• Comprehensive Observability: A mission control dashboard offers real-time visibility into every agent session, including prompt history, tool calls, file modifications, and total resource costs.
• Integration Ecosystem: Native support for common developer and business tools like GitHub, Linear, PagerDuty, Datadog, and Slack enables end-to-end automated workflows.
• Flexible Deployment: Available as a fully managed cloud service or as a self-hosted solution for organizations that require data residency control and network isolation within their own VPC.
• Agent Agnostic: The platform natively supports various popular coding agents such as Claude Code, Codex, Gemini CLI, and GitHub Copilot, allowing teams to swap or utilize multiple agents simultaneously.
• Environment Templates: Snapshot capabilities allow teams to define standard environments once, enabling instant provisioning of consistent, pre-configured development workspaces.

Operationally, Runtime functions by bridging the gap between high-level intent and low-level code execution. Users initiate tasks through a web interface, API, or messaging platform. Runtime then provisions the required cloud infrastructure, injects the necessary secrets and context, and monitors the agent as it iterates on the codebase. Once a task is complete—such as resolving a production alert or drafting a new feature implementation—the results are posted for human review and approval. This loop ensures that all AI-generated contributions follow standard CI/CD and review processes before being merged.

Some common use cases include:

• Automated Alert Triage: Connecting monitoring services like PagerDuty or Datadog to an agent that investigates system latency and submits a pull request with a potential fix.
• Cross-Functional Backlog Clearing: Enabling non-engineering teams to resolve minor tickets or perform routine updates by interacting with a scoped AI agent.
• Standardized Developer Onboarding: Providing temporary, perfectly configured sandboxes for new developers to explore codebases without manual local environment setup.
• Cost-Controlled Scaling: Implementing per-team or per-project spend limits to monitor and manage the financial impact of AI-powered development across the entire organization.