Introducing Exo Harness for Runta runtimes. agents announcement runtime security Integration at a glance. Exo inside Runta: ephemeral executors, durable state, and runtime platform services. Select the diagram to view it at full size. Exo Harness is available as a built-in runtime in Runta, with model access, interactive work, and background services ready in one managed environment. Inside the runtime, Exo's interactive REPL, scheduler service, and adapter service feed the executor layer. Executors hold the agent's policies, tools, skills, and model calls, and can evolve while keeping their state ephemeral. The Exoharness API connects them to the durable state and secrets managed by the Harness. Runta surrounds this agent architecture with runtime services: checkpoints, Watchfox, auto-scaling, secret stubs, and network access control. Model requests pass through that network boundary to an approved provider endpoint, connecting Exo's agent logic to Runta's managed infrastructure. Model access with protected credentials. API keys are protected by Runta. Exo stores a placeholder called a secret stub, while Runta's egress gateway supplies the real credential only when forwarding requests to the approved model provider. This keeps the actual API key out of Exo's saved configuration while giving the agent access to the selected model. A workspace you can return to. Exo uses local-process mode to run commands directly inside the Runta runtime, with access to its files, tools, and network. Runta provides a resumable environment, so you can pause and return to your workspace without rebuilding it from scratch. Exo handles the agent's work while Runta manages the environment's lifecycle. What is Exo Harness? Exo Harness is a minimal architecture for building agents. It separates trusted infrastructure for state, resources, and security from agent-specific logic that can evolve, stop, resume, and rewind across runs. Exo has three layers: * The Harness protects durable state, including append-only conversation history, artifacts, secrets, and sandbox lifecycle, and cannot be altered by the agent. * The Executor controls prompts, model calls, tools, and memory. It can be replaced or modified by the agent because it holds no durable state. * The Sandbox provides an isolated environment for running commands and experiments, with checkpoints that let environment state be restored alongside conversation state. This separation gives the system the potential to self-evolve: the agent can iteratively rewrite and improve its own execution logic while the Harness preserves a stable, recoverable foundation. How to use Exo Harness on Runta. 1. Create your Runta account. 2. Add your model API key to Runta Secrets. Add the API key for your model provider in the Runta dashboard's Secrets section. 3. Create an Exo runtime. Use your free $50 credit to create a runtime with Exo Harness. Select your model and the secret you added in the previous step. 4. Open the web terminal. Use the dashboard's web terminal to interact with the Exo agent. Runta configures the selected model and prepares the agent so you can start working directly from the terminal. The Exo source code is located in the /opt/exo folder. Create an Exo runtime with the Runta skill. npx skills add https://runta.com/docs

Runta raises seed for AI agent compute. Runta secures a16z-led seed funding to build a new compute execution layer for AI agents, addressing the growing demand for specialized CPU resources. Visual TL;DR. AI Agent Boom exacerbates Existing Compute Inadequate. Existing Compute Inadequate causes CPU Shortage. AI Agent Boom drives CPU Shortage. CPU Shortage prompts Runta Founded. Runta Founded secures funding a16z Leads Seed. a16z Leads Seed funds New Execution Layer. New Execution Layer enables Provides 'Computer' Abstraction. New Execution Layer aims to Solves Compute Gap. Provides 'Computer' Abstraction contributes to Solves Compute Gap. * AI Agent Boom: growing demand for autonomous AI agents requiring specialized compute resources * Existing Compute Inadequate: high-level abstractions and developer experience optimization not suitable for AI agents * CPU Shortage: AI agents need stateful OS environments, leading to significant CPU demand * Runta Founded: startup focused on re-architecting compute infrastructure for AI agent ecosystem * a16z Leads Seed: Andreessen Horowitz leads funding round for Runta's AI agent compute vision * New Execution Layer: Runta building compute execution layer specifically for AI agents' needs * Provides 'Computer' Abstraction: Runta's approach offers a complete, stateful operating system environment for agents * Solves Compute Gap: addresses the growing demand for specialized CPU resources for AI agents Visual TL;DR Andreessen Horowitz (a16z) has led the seed funding round for Runta, a startup focused on re-architecting compute infrastructure for the burgeoning AI agent ecosystem. This investment underscores a shift in computing demands, moving beyond developer-centric platforms towards the raw needs of autonomous AI. The current trend of high-level abstractions and developer experience optimization, while beneficial for human coders, has inadvertently made existing compute stacks less suitable for AI. AI agents, in contrast, operate more like traditional computing entities, requiring a complete, stateful operating system environment that can run efficiently either locally or in the cloud.