In the AI era, your data security foundation.
An enterprise AI agent runtime built around our PipelineLoop architecture: task loop, context management, multi-model failover, and a skills and tools ecosystem are built in. The engine and data run in your environment.
01 — Our engine
Not a black-box call into a model SDK, but our own middleware-driven execution loop. Every step passes through a pluggable chain, so behavior is controllable and the process is observable.
Not finished? Loop again until there is a result.
12 built-in middleware layers, combinable per use case.
Hand the request straight to a model SDK: what happens inside is invisible, failures mean a full retry, and behavior cannot be shaped by the business.
Every model call and tool execution passes through middleware: budgets, guardrails, and recovery policies attach as needed. The process is traceable, reviewable, and interruptible.
02 — Core capabilities
From the task loop to the tool ecosystem, the building blocks an AI application needs are here.
Our ReAct main loop plus middleware: budget control, context planning, stall detection, and error recovery combine per task, so long-running work keeps moving.
Context budgets are calculated per model, with history compressed and trimmed automatically. Long conversations and large documents stay organized.
OpenAI-compatible multi-model clients and fallback chains switch automatically when the primary model is unavailable, keeping work running.
A tool registry, standard Agent Skills (SKILL.md), and an MCP client (local stdio / remote Streamable HTTP).
Guard and safety middleware, sub-agent orchestration, and human confirmation for critical actions give complex flows boundaries and brakes.
SSE streaming, persisted events, resumable runs, and trace tracking make execution observable and reviewable.
03 — Secure and controllable
From deployment to model choice, you set the data boundary.
The engine, skills, and toolchain run on your servers or inside your network, keeping data and execution in-house.
OpenAI-compatible and privately deployed models are supported. For sensitive data, nothing leaves the intranet.
An ask_user confirmation node pauses before steps involving important data or external actions, and waits for a human.
Our Go engine has a minimal dependency closure and no black-box framework. Events, traces, and runtime state are recorded.
Not sure what it should cost? Read “Private AI deployment costs” → · Comparing Dify, Coze, or Bailian? Read “Enterprise AI agent foundation comparison” →
04 — An embeddable foundation
LingHarness is a standalone Go module that servers, desktop apps, and internal systems can import directly. Choose your models, tools, and deployment; keep control of the capability.
// Import the core engine
import (
"harness.czlytech.com/harness"
"harness.czlytech.com/skill"
"harness.czlytech.com/llm"
)
// Register tools, mount middleware,
// and run your own AI agent.A runtime proven in production enterprise agent projects
05 — Use cases
Knowledge Q&A, document processing, and process lookup, answered from your own knowledge base without sending data to the public internet.
Approvals, work orders, and reports connect to the agent, with human confirmation as a backstop and clear boundaries for automation.
For strict data boundaries: privately deployed models plus an on-premises runtime make AI usable and governable.
Build your own AI products and services on the foundation, keeping the capability in-house.
Book a technical call and we’ll propose a deployment that fits your data boundary.