Here is Chinese version 中文版
Here is YouTube video overview
Think of an "AI Agent" as a smart assistant that can perform tasks on its own. The main goal is to build these agents so they are stable, produce verifiable results, and can be reused, managed, and expanded upon. The original text lays out a blueprint for how to build a truly "general purpose" AI agent and then explains what types of agent tasks are well-suited for a coding environment (like an IDE) and which are not.
Part 1: The Essential Components of a General AI-Agent
To build a robust and trustworthy AI agent, you need a layered system. Intelligence (the AI model) is just one piece of the puzzle.
- Interaction/Console (The User Interface): This is how you talk to the agent, see what it's doing, and approve its actions. It could be a plugin in your code editor, a website, or a command-line tool. Its main job is for you to interact with and review the agent's work.
- Orchestration (The Workflow Engine): This layer is the brain of the operation. It plans the steps, executes them, and then critiques the results. It manages the tools the agent can use and handles errors or retries. Think of it as a sophisticated workflow manager like LangGraph.
- Runtime/Sandboxing (The Secure Execution Environment): This is a safe, isolated space where the agent performs its tasks, often using containers like Docker. It ensures the agent only has the permissions it absolutely needs (a concept called "least-privilege") and can run for a long time even if you close the user interface.
- Memory & Knowledge (The Brain's Database): This is where the agent stores short-term working notes, project-specific information, and a larger knowledge base. It uses techniques like RAG (Retrieval-Augmented Generation) and Knowledge Graphs (KG) to ensure the information it uses is accurate and to double-check high-risk actions.
- Policy/Governance (The Rulebook): This component sets the rules for what the agent is allowed to do, ensuring it complies with data privacy and other regulations. It's like a set of guardrails to keep the agent in check, and can be implemented with tools like Open Policy Agent (OPA).
- Observability (The Monitoring System): This allows you to see everything the agent is doing. It logs all actions and events so you can trace what happened, analyze performance, and figure out the root cause of any failures.
- Eventing/Scheduling (The Task Trigger): This allows the agent to be triggered by specific events, run on a schedule (like a cron job), or process tasks from a queue.
- Intelligence (The AI Model): This is the core AI, like a Large Language Model (LLM), that provides the reasoning and problem-solving abilities. The key takeaway is that the intelligence is just the source of the capability; the reliability comes from all the other systems supporting it.
Part 2: What's Needed for Multiple Agents to Work Together
When you have more than one agent working together (a multi-agent system), you need a few extra components:
- Defined Roles and Contracts: Each agent has a clear job with well-defined inputs and outputs.
- Coordination: A system to route tasks, divide labor, and resolve disagreements, perhaps through voting or cross-checking each other's work.
- Shared Memory: A common place for agents to share information and status updates.
- Failure Isolation: If one group of agents fails, it can be isolated so it doesn't bring down the whole system.
Part 3: What Coding IDEs Are GREAT For
An Integrated Development Environment (IDE) is the software developers use to write, test, and debug code. They are excellent for AI agents that involve a human in the loop, work on short tasks, and have access to a lot of local files and context.
Here are the types of agent tasks that work well in a coding IDE:
1. For Writers and Researchers (in a Word Processor or Research Tool like Zotero)
- Citation Correction Agent: Similar to fixing code, this agent could scan a research paper, identify a poorly formatted citation, and suggest the correct format (e.g., APA, MLA) based on the document's bibliography. The writer just has to click "accept."
- Argument Consistency Agent: This agent acts like a "linter" for your writing. It could read a 30-page report and flag sections where your argument contradicts an earlier point or where you've used inconsistent terminology for the same concept.
- Evidence Gap Finder: Much like a test coverage tool, a user could ask the agent to review their article and identify any claims or statements that are not supported by a citation or data. It would highlight these "uncovered" claims for the writer to address.
- Content Repurposing Agent: A user could highlight a section of a detailed report and ask the agent to "create a LinkedIn post and three tweets from this." The agent generates the drafts directly in the application for the user to review, edit, and approve before posting.
2. For Data Analysts (in a Spreadsheet or a tool like Jupyter Notebooks)
- Data Cleaning Agent: The agent could scan a newly imported dataset, identify common errors like missing values, inconsistent date formats, or outliers, and present a list of suggested fixes (e.g., "Fill missing salaries with the average value?"). The analyst approves or rejects each change.
- Visualization Recommender: An analyst could select a range of data, and the agent would automatically suggest the most effective chart type (e.g., "This looks like time-series data; I recommend a line chart.") and create it with proper labels and a title upon approval.
- Formula & Logic Auditor: For a complex spreadsheet, this agent could trace the dependencies of a final cell back to its inputs, creating a visual map to help the analyst find errors in the logic or a broken formula.
3. For Graphic Designers (in an application like Figma or Adobe Photoshop)
- Brand Guideline Agent: A designer could run this agent on a set of marketing materials, and it would automatically flag any colors, fonts, or logos that don't comply with the company's official brand guidelines, suggesting one-click fixes.
- Asset Variation Generator: Similar to generating boilerplate code, a designer could finalize one ad design and ask the agent to automatically generate 10 different size variations required for an ad campaign, smartly rearranging the elements to fit each new dimension. The designer then gives a final review.
- Accessibility Checker: This agent could analyze a user interface design and flag elements that fail accessibility standards, such as low-contrast text or buttons that are too small, and suggest specific changes to make the design more inclusive.
4. For Legal Professionals (in a Document Review Platform)
- PII Redaction Agent: When reviewing a document for public release, a lawyer could use an agent to automatically identify and suggest redactions for Personally Identifiable Information (PII) like names, addresses, and social security numbers. The lawyer performs the final review to ensure nothing was missed or incorrectly flagged.
- Clause Consistency Checker: In a long contract, this agent could verify that the definitions and terms used in one section (e.g., "Confidential Information") are consistent with how those same terms are used in other clauses throughout the document.
5. For Software Engineer
- Fixing Code: Finding errors, generating patches, and running tests to create minimal, correct changes.
- Refactoring and Linting: Cleaning up code across multiple files, like renaming variables consistently or removing unused code.
- Generating Tests: Creating unit and integration tests to improve code coverage.
- Planner-Executor-Critic Model: An agent that breaks down a task, performs a "dry run" for the developer to review, and then executes it after approval.
- Small-Scale Integrations and Migrations: Adding a new library, updating configurations, or making small-scale code changes.
- Developer Experience and Repository Operations: Automating tasks like generating changelogs, release notes, or auditing dependencies.
- Lightweight Evaluations: Quickly testing different AI prompts or models on a small scale. Of course. The key idea is that any application that acts as a "workbench" for a specific type of work can benefit from AI agents that are highly interactive, context-aware, and supervised by a human.
Part 4: What Coding IDEs Are NOT a Good Fit For
IDEs are not the right place for agents that need to run for a long time on their own, handle sensitive data, or operate in a distributed environment. These tasks require a more robust backend system.
Here are the tasks that are a poor fit for an IDE:
- Long-Running or "Headless" Tasks: These are tasks that need to run in the background, independent of a user interface, such as monitoring systems, data pipelines, or processing tasks from a queue.
- Tasks with Strong Security and Compliance Needs: Handling personally identifiable information (PII), financial data, or medical records requires a secure environment with strict access controls and auditing.
- Distributed, Multi-User, or Cost-Sensitive Tasks: Running tasks across multiple machines, managing resources for many users, or needing to closely track costs requires a more powerful backend orchestration system.
- Large-Scale Data Processing: Big data transformations and production pipelines are far beyond the scope of a local, interactive environment.
In Conclusion: The Right Tool for the Right Job
The power of a "general" AI agent comes from a well-structured system with clear layers of responsibility. A coding IDE is an excellent "front-end" for human-AI collaboration on development tasks that are short, interactive, and context-rich. However, for tasks that are long-running, require high security, or are distributed, you need a dedicated backend "Agent Runtime/Orchestrator." By combining these two, you get the best of both worlds: high-quality AI-assisted development without compromising on reliability and compliance for more complex, autonomous tasks.
Disclosure: This article was drafted with the assistance of AI. I provided the core concepts, structure, key arguments, references, and repository details, and the AI helped structure the narrative and refine the phrasing. I have reviewed, edited, and stand by the technical accuracy and the value proposition presented.
Chinese version
下面是 完整的繁體中文翻譯，已保持 原本的 Markdown 格式：
將「AI Agent」想成一個能自己執行任務的智慧助理。目標是打造一個 穩定、可驗證、可重複使用、可管理、可擴展 的 agent 系統。本文提出如何打造真正「通用型 AI Agent」的藍圖，並說明哪些 agent 任務適合在 coding IDE 中執行，哪些不適合。
Part 1: 通用 AI-Agent 的核心組成
要建立一個可靠可信的 AI agent，需要一個多層系統。Intelligence（AI 模型）只是其中一個元件。
- Interaction/Console（使用者介面）： 你與 agent 溝通的地方，看到它在做什麼，並批准它的動作。可能是 IDE 插件、網站或命令列工具。主要用途是讓你能互動、審查 agent 的輸出。
- Orchestration（工作流程引擎）： 是整個系統的運作大腦。它規劃步驟、執行、再批判結果。管理 agent 可使用的工具，負責錯誤處理與重試。可以把它想像成進階版的 LangGraph。
- Runtime/Sandboxing（安全執行環境）： agent 執行任務的隔離空間，通常使用 Docker 等容器。確保 agent 只有必要的權限（least-privilege），且即使你關閉介面它也能持續運作。
- Memory & Knowledge（知識與記憶系統）： 用來儲存短期筆記、專案資訊與更大的知識庫。使用 RAG（Retrieval-Augmented Generation）、Knowledge Graphs（知識圖譜） 等方式，確保引用資訊準確，避免在高風險動作出錯。
- Policy/Governance（策略 / 治理規則）： 控制 agent 可以做什麼，確保符合資料隱私與合規規範。就像一套「護欄」，可用 Open Policy Agent (OPA) 等工具實作。
- Observability（可觀察性 / 監控系統）： 讓你能看到 agent 所做的所有行為，記錄所有事件，方便追蹤狀況、分析績效、找出失敗原因。
- Eventing/Scheduling（事件觸發 / 排程）： 允許 agent 被事件觸發、按排程執行（像 cron job），或從 queue 處理任務。
- Intelligence（AI 模型）： 提供推理與解題能力，是核心能力來源。但可靠性來自整個系統，而不是模型本身。
Part 2: 多 Agent 協作時需要額外具備的元素
當你不只一個 agent，而是 多 agent 系統，就需要額外的架構來協作：
- Defined Roles and Contracts（明確角色與協議）： 每個 agent 有清楚的任務，輸入與輸出明確定義。
- Coordination（協同機制）： 分配任務、處理衝突、甚至透過投票或交叉驗證彼此的結果。
- Shared Memory（共享記憶）： 一個共同空間存放資訊與狀態更新。
- Failure Isolation（故障隔離）： 某一組 agent 出錯時不會拖垮整個系統。
Part 3: Coding IDE 特別擅長的 Agent 工作類型
IDE（Integrated Development Environment）是開發者寫程式、測試與 debug 的環境。
IDE 非常適合 有人工審核、任務短、具有大量上下文的 agent。
以下是 非常適合在 IDE 或專業工具中運作的 agent 工作類型：
1. Writer / Researcher（在 Word Processor 或 Zotero 類工具）
- Citation Correction Agent（引用格式修正） 掃描文件，找出引用格式錯誤並建議修正（APA、MLA等）。使用者只需按「接受」。
- Argument Consistency Agent（論點一致性檢查） 像寫作版的 linter，檢查論述前後是否矛盾或用詞不一致。
- Evidence Gap Finder（缺乏證據區塊偵測） 找出未附引用來源或數據的論述，像 test coverage 一樣標示「未覆蓋區域」。
- Content Repurposing Agent（內容再利用） 使用者選文章段落 → agent 生成 LinkedIn 貼文與三則 tweets，等待審核。
2. Data Analyst（在 Spreadsheet 或 Jupyter Notebook）
- Data Cleaning Agent（資料清理） 掃描 dataset，偵測遺漏值、格式不一致或 outlier，並建議修正。
- Visualization Recommender（視覺化建議） 自動建議最合適的圖表類型並生成（例如：時間序列 → 折線圖）。
- Formula & Logic Auditor（公式邏輯稽核） 追蹤複雜公式的依賴關係，視覺化流程，找出邏輯或公式錯誤。
3. Graphic Designer（Figma / Photoshop）
- Brand Guideline Agent（品牌識別檢查） 找出不符合 brand guideline 的顏色、字型、logo，並給出一鍵修復建議。
- Asset Variation Generator（素材尺寸自動生成） 一份廣告 → 自動生成 10 種尺寸排列，設計師只需最後審核。
- Accessibility Checker（無障礙檢查） 偵測 UI 中不符合無障礙規範的元素並提出具體改善方案。
4. Legal Professional（文件審查系統）
- PII Redaction Agent（個資遮罩） 自動找出 PII（姓名、地址、社會安全號碼）並建議遮罩。
- Clause Consistency Checker（條款一致性檢查） 確認合同中定義的名詞前後使用一致。
5. Software Engineer（在 IDE）
- Fixing Code（修錯）
- Refactoring / Linting（重構與清理）
- Generating Tests（自動產生測試）
- Planner-Executor-Critic（計畫 → 執行 → 批改）
- Small-Scale Integrations（小規模整合與遷移）
- Repository Operations（產生 changelog、release notes、依賴稽核）
- Lightweight Evaluations（快速 prompt / model 測試）
關鍵理解：
任何作為 某種工作「工作台 / workbench」 的應用，都能讓 agent 在其中與人深度互動、即時審查、快速迭代。
Part 4: Coding IDE 不適合的 Agent 類型
以下類型的 agent 不適合放在 IDE：
- Long-Running / Headless Tasks（長時間或無頭任務） 如監控系統、資料管線、queue 處理。
- High Security / Compliance（高安全性・高度合規需求） 涉及個資、金融資料、醫療紀錄的任務。
- Distributed or Multi-User Tasks（分散式、多使用者或需要成本控管）
- Large-Scale Data Processing（大型資料處理 / 大規模 pipeline）
這些任務需要 後端 Agent Runtime / Orchestrator 來管理，而不是 IDE。
In Conclusion: 用對工具，AI Agent 才能爆發最大價值
「通用 AI Agent」的強大來自 清楚分層、責任分離的架構。
- IDE：最佳化 短、即時、需要人類審核 的任務
- Backend Agent Runtime：最佳化 長時間、高安全、多 agent 協作、自動執行 的任務
將兩者結合，你可以擁有：
✅ 高品質 AI 協助開發效率
✅ 不影響可靠性、安全與合規
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