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Scaffold a branded AI agent harness with CLI, MCP, memory, and learning.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "metaharness" yet — see the docs or source repo.
Create a branded AI agent harness for my frontend team called Nova Dev. Include an npx CLI, MCP server, project memory, a learning loop, and default configs for Claude Code and Codex. Provide the folder structure, initialization commands, and sample key config files.
A practical agent harness blueprint with project structure, CLI setup steps, and core configuration examples.
Design a continuously learning AI agent harness for my existing Node.js monorepo. It should read repository conventions, store past decisions, expose tools through MCP, and define a release workflow. Focus on the memory storage design and learning loop mechanism.
A tailored design for an existing codebase covering memory, learning, tool interfaces, and release workflow.
Create a secure release plan for an enterprise-facing AI agent harness. Include witness-signed releases, recommendations for hardware-isolated sandbox execution, a version audit workflow, and compatibility notes for OpenClaw and RVM. Output implementation steps and a risk control checklist.
An enterprise-ready secure release plan covering signing, isolation, auditing, and compatibility guidance.
Build and iterate meta-harness scaffolding for fixed models via propose-score-Pareto loops.
Give AI agents unified access to Harness for deployment and DevOps workflows.
Expose internal agent tools as a standard MCP server for unified access.
Build AI agents quickly with a model-driven approach and minimal code.
Let AI manage Harness CI/CD, GitOps, feature flags, and cost data.
Connect multiple AI coding agents to collaborate on development tasks efficiently.