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Build and orchestrate production-grade multi-agent AI workflows and collaboration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "hive" yet — see the docs or source repo.
Design a production-grade multi-agent architecture for an AI customer support system with four agents: intent detection, knowledge retrieval, response generation, and quality review. Explain each agent's inputs and outputs, collaboration order, retry strategy, and monitoring metrics.
A clear multi-agent architecture plan covering agent roles, orchestration flow, fault tolerance, and key monitoring metrics.
Create a test plan for a system where multiple AI agents collaborate on code review. Cover correctness, consistency, latency, exception handling, and human handoff, and provide actionable evaluation metrics.
A testing and evaluation framework for a multi-agent system to validate production performance.
Provide a production deployment plan for a multi-agent AI service, including service decomposition, logging and tracing, access control, scaling, fault isolation, and release strategy.
A deployment blueprint for reliably operating a multi-agent AI service in production.
Let AI manage Harness CI/CD, GitOps, feature flags, and cost data.
Build AI agents quickly with a model-driven approach and minimal code.
Share, search, and reuse knowledge across connected AI agents.
Coordinate multiple AI agents through weighted consensus for collective decisions.
Discover AI agent harness resources for tooling, evals, orchestration, and observability.
Coordinate multiple AI agents with shared context, task orchestration, and live collaboration.