AI agents need an operating model before they touch enterprise workflows.
AI Harness is self-hosted TypeScript infrastructure for building provider-neutral LLM agent systems inside an application or platform, with typed agents, workflows, tools, skills, state, sandboxing, run events, traces, and eval helpers.
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Enterprise-Druck
AI pilots are easy to demo and hard to operate. Prompts, tools, model calls, approvals, traces, state, and provider choices scatter across notebooks, SDK snippets, SaaS automations, and hidden scripts. That is not an enterprise operating model. It is liability with a chat interface.
Produktantwort
AI Harness puts the AI runtime inside the application boundary. Teams define typed agents for model-driven loops, typed workflows for orchestration, explicit tools and skills for capability, provider adapters for model choice, and run events, traces, state, sandboxing, review gates, and eval helpers around the execution path.
Betriebsmodell
The application creates a harness session; agents handle typed model loops, workflows orchestrate business processes, tools and skills provide capability, adapters connect providers, and state, sandbox, telemetry, run events, review gates, and eval helpers wrap the execution boundary.
Entscheidungspfad
AI Harness is for the point where AI stops being an experiment and starts touching work that has owners, budgets, risks, and audit trails.
The core move is simple: do not let the model provider become the architecture. Put the agent runtime inside the enterprise application boundary. Keep the model call behind adapters. Keep tools explicit. Keep workflows typed. Keep approvals, state, traces, and evals close to the business process.
Agents handle the model-driven loop: prepare messages, call a model, execute tools, continue until a validated output exists, and emit run events. Workflows handle the surrounding business process: sequence agents, branch, parallelize, apply deterministic logic, request review, write artifacts, and persist state.
That gives decision makers a path from AI prototype to enterprise platform: provider choice remains open, tool execution is controlled, outputs are validated, review gates are visible, and operational evidence exists without turning prompts and customer data into accidental log retention.
Enterprise-Bezug
Fähigkeiten
Typed agents for LLM conversation loops with tool execution and validated outputs
Typed workflows for sequencing, branching, parallel agents, review gates, durable writes, and artifacts
Provider-neutral model operations for text, structured objects, multimodal work, embeddings, and reranking
TypeScript, built-in, and MCP tools plus reusable skill directories
State, sandboxing, logs, traces, run events, and provider-neutral eval helpers
Provider adapters for OpenAI, Anthropic, Amazon Bedrock, and Azure AI Foundry
Enterprise Use Cases
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