Agent Harness and its 12 Core Modules

To date, AI Engineering has evolved through three primary stages: Prompt Engineering, Context Engineering, and Harness Engineering.

Prompt Engineering:

Core Question: Did the model understand what you were saying?
核心问题：模型有没有听懂你在说什么？

Perfecting Instructions: Transforming vague requests into precise, step-by-step commands to eliminate ambiguity.
完善指令：将模糊的请求转变为精确的、分步骤的命令，以消除歧义。
Persona-Setting: Assigning a specific identity to the model, such as “Senior Data Architect,” to calibrate its professional depth and tone.
角色设定：为模型赋予特定身份，例如“资深数据架构师”，以调整其专业深度和语气。
Formatting: Mandating outputs in specific structures like JSON or SQL to ensure they are machine-readable and ready for downstream systems.
格式化规范：规定输出必须为 JSON 或 SQL 等特定结构，确保机器可读性并能直接被下游系统调用。

Context Engineering:

Core Question: Does the model have enough—and correct—information?
核心问题：模型有没有拿到足够而且正确的信息？

Reducing Hallucination: Grounding the model’s responses in private enterprise data to ensure it doesn’t “hallucinate” or invent facts.
减少幻觉：将模型的回答锚定在企业私有数据中，确保它不会凭空猜测或捏造事实。
Retrieval-Augmented Generation (RAG): Using vector databases to provide the model with real-time, relevant document snippets during the generation process.
检索增强生成 (RAG)：利用向量数据库在生成过程中为模型提供实时的、相关的文档片段。
Knowledge Management: Organizing enterprise data so the model understands the relationships between different business entities.
知识管理：组织企业数据，使模型能够理解不同业务实体之间的关联逻辑。

Harness Engineering:

Core Question: Can the model consistently execute correctly in a real-world environment?
核心问题：模型在真实的执行里能不能持续做对？

Reliability Evaluation: Building automated testing frameworks to verify that the model remains stable and accurate across thousands of requests.
可靠性评估：建立自动化的测试框架，验证模型在数千次请求中能否保持稳定和准确。
Tool-Calling Verification: Ensuring the API calls or database queries generated by the model are syntactically correct and safe to execute.
工具调用验证：确保模型生成的 API 调用或数据库查询指令在语法上是正确的，且执行起来是安全的。
Operational Monitoring (LLMOps): Tracking AI performance, latency, and drift in production, similar to how we monitor traditional data pipelines.
生产监控 (LLMOps)：在生产环境中跟踪 AI 的性能、延迟和漂移，就像监控传统数据流水线一样。

What is Agent Harness

Agent Harness is an orchestration framework or runtime environment that connects, manages, and controls the various components needed to run an autonomous AI agent — including the LLM, tools (APIs), memory, context windows, parsing logic, and safety guardrails — allowing the agent to execute complex, multi-step tasks reliably.

Agent = Model + Harness

Agent Harness 是一个编排框架或运行环境，用于连接、管理和控制运行自主 AI 智能体所需的各种组件，包括大语言模型、工具（API）、记忆、上下文窗口、解析逻辑以及安全护栏，从而使智能体能够可靠地执行复杂的多步骤任务。

智能体 = 模型 + Harness

The landscape of AI Agent architecture is currently in a “pre-standardization” phase, very similar to the early days of cloud computing between 2008 and 2012. We are in an era where everyone is inventing terminology as they go. There is no simple, standard, or unified definition yet. Instead, interpretations vary widely depending on an individual’s standpoint, specific interests, and areas of focus. Don’t focus too much on memorizing exact module names. Focus on understanding the functional responsibilities instead. So I emphasized the operational AI platform side. That means Retrieval, Tool Calling, Workflow, Evaluation, and Observability were emphasized because these are the core of Enterprise AI-ready Platforms today. I focus on:

RAG
Vector DB
Tool Calling
Workflow
Evaluation
Observability

AI Agent 架构目前还处于“标准化之前”的阶段，很像 2008–2012 年早期云计算时期，大家都在边发展边发明术语。没有什么简单的，标准的，统一的东西。各自从各自的立场角度，兴趣和关注点出发都有不同的解释。不要太执着于具体模块名字。更重要的是理解“功能职责”。所以我重点讲的是“企业 AI 平台运行层”，更突出 Retrieval、Tool Calling、Workflow、Evaluation、Observability，因为这些是现在企业AI集成的核心。

Agent Harness 12 Core Modules

Step	Category	Module	English Explanation	中文解释
1	AI Brain	Prompt System	Defines the AI’s role, goals, instructions, constraints, and response behavior so the model knows what it should do.	定义 AI 的角色、目标、规则和行为方式，告诉模型“你是谁、应该做什么”。
2	AI Brain	LLM Reasoning	The LLM understands the user request, performs reasoning, generates ideas, and decides how to respond.	LLM 理解用户请求，进行推理、分析，并决定如何回答或执行任务。
3	Enterprise Knowledge	Context Management	Selects, filters, compresses, and organizes the most relevant context within token limits so the AI has the right information.	选择、过滤、压缩并组织最相关的上下文，确保 AI 在 token 限制内拥有正确的信息。
4	Enterprise Knowledge	Memory	Loads conversation history, user preferences, and long-term memory so the AI can maintain continuity and personalization.	加载历史对话、用户偏好和长期记忆，让 AI 保持连续性和个性化。
5	Enterprise Knowledge	Retrieval/RAG Pipeline/Knowledge Base	Searches enterprise documents, databases, and knowledge sources to retrieve external information the LLM does not already know.	从企业文档、数据库和知识库中检索信息，补充 LLM 本身不知道的企业知识。
6	Enterprise Actions	Planning/Agent Loop	Breaks large or complex tasks into smaller executable steps and determines the execution strategy.	将复杂任务拆解成多个可执行步骤，并制定执行策略。
7	Enterprise Actions	Tool Calling	Allows the AI to call APIs, SQL, Python, enterprise systems, search engines, or external applications to perform real actions.	让 AI 调用 API、SQL、Python、企业系统或外部工具，真正执行实际操作。
8	Enterprise Actions	State Management	Tracks execution progress, workflow status, retries, temporary variables, and current task state during runtime.	跟踪运行过程中的执行进度、状态、重试、临时变量和当前任务情况。
9	Enterprise Workflow	Orchestration/Multi-Agent Orchestration	Coordinates multiple tools, workflows, agents, and execution paths so the overall system works together correctly.	协调多个工具、工作流、Agent 和执行路径，让整个系统协同工作。
10	Enterprise Workflow	Evaluation	Evaluates answer quality, correctness, relevance, task completion, and hallucination risk before returning results.	在返回结果前评估答案质量、正确性、相关性、任务完成度和幻觉风险。
11	Enterprise Operations	Guardrails	Enforces security rules, permissions, compliance policies, risk controls, and safe AI behavior.	执行安全规则、权限控制、合规要求和风险控制，防止 AI 做危险操作。
12	Enterprise Operations	Observability	Monitors logs, traces, token usage, latency, failures, and overall AI system health for debugging and operations.	监控日志、链路、token 用量、延迟、错误和系统健康状态，用于运维和调试。

The Core Logic of Enterprise AI
企业 AI 的核心逻辑

Category	Purpose	中文
AI Brain	Makes the AI understand and reason	让 AI 能理解和推理
Enterprise Knowledge	Gives AI the right enterprise information	给 AI 正确的企业知识
Enterprise Actions	Allows AI to perform actual work	让 AI 真正执行任务
Enterprise Workflow	Coordinates complex execution flows	协调复杂工作流
Enterprise Operations	Keeps the AI system safe, stable, and observable	保持系统安全、稳定、可监控

Simple Enterprise Agent Harness Architecture
企业 Agent Harness 简化架构图

User
  ↓
Prompt System
  ↓
LLM (GPT/Claude/Gemini)
  ↓
Planning Engine
  ↓
Tool Calling / Retrieval
  ↓
Enterprise Systems
(SQL / API / SharePoint / Databricks)
  ↓
Memory + State Tracking
  ↓
Guardrails + Evaluation
  ↓
Monitoring / Observability
  ↓
Final AI Response

English	中文
The user asks the AI to perform a task.	用户要求 AI 执行任务。
The Prompt System defines the AI’s role and behavior.	Prompt System 定义 AI 的角色和行为。
The LLM understands the request and reasons about it.	LLM 理解用户请求并进行推理。
The Planning module breaks the task into steps.	Planning 模块把任务拆成多个步骤。
Tool Calling lets the AI access databases, APIs, or enterprise systems.	Tool Calling 让 AI 调用数据库、API 或企业系统。
Retrieval searches enterprise documents and knowledge bases.	Retrieval 检索企业文档和知识库。
Memory and State track progress and conversation history.	Memory 和 State 管理历史和任务状态。
Guardrails enforce security and compliance rules.	Guardrails 执行安全与合规限制。
Evaluation checks answer quality and hallucinations.	Evaluation 检查 AI 回答质量和幻觉问题。
Observability monitors the entire AI workflow and system health.	Observability 监控整个 AI 工作流和系统状态。