惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

P
Proofpoint News Feed
L
LangChain Blog
U
Unit 42
Apple Machine Learning Research
Apple Machine Learning Research
The Cloudflare Blog
Martin Fowler
Martin Fowler
T
Tenable Blog
IT之家
IT之家
H
Help Net Security
阮一峰的网络日志
阮一峰的网络日志
Forbes - Security
Forbes - Security
N
Netflix TechBlog - Medium
The Hacker News
The Hacker News
J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Tailwind CSS Blog
美团技术团队
NISL@THU
NISL@THU
T
Threatpost
GbyAI
GbyAI
T
Threat Research - Cisco Blogs
I
InfoQ
Jina AI
Jina AI
Microsoft Azure Blog
Microsoft Azure Blog
人人都是产品经理
人人都是产品经理
C
Check Point Blog
V
Vulnerabilities – Threatpost
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Spread Privacy
Spread Privacy
S
Securelist
大猫的无限游戏
大猫的无限游戏
P
Privacy & Cybersecurity Law Blog
Security Archives - TechRepublic
Security Archives - TechRepublic
爱范儿
爱范儿
小众软件
小众软件
C
Cybersecurity and Infrastructure Security Agency CISA
Cisco Talos Blog
Cisco Talos Blog
Engineering at Meta
Engineering at Meta
S
Security Affairs
S
Secure Thoughts
T
Troy Hunt's Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
aimingoo的专栏
aimingoo的专栏
博客园 - 司徒正美
PCI Perspectives
PCI Perspectives
C
Cisco Blogs
MongoDB | Blog
MongoDB | Blog
Vercel News
Vercel News
月光博客
月光博客
Recorded Future
Recorded Future

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
Word Document Editing for AI Agents - Introducing OfficeAgent.NET 0.1
Ilia Sokolov · 2026-06-12 · via DEV Community

A few weeks ago I wrote about why AI agents struggle with Word documents. The short version: agents produce text, Markdown, JSON, and HTML, but a .docx is an OOXML package, and most of what makes it a Word document lives in XML parts outside the visible text. Something has to translate the agent's output into a valid Word file, and the Word file back into something the agent can reason about. I called this the agent-document layer, and announced an open-source project to build it for .NET.

Today the first version is available. OfficeAgent.NET 0.1 is an open-source (MIT) .NET library that lets an AI agent describe Word document changes as a typed plan, while the library handles the Open XML details.

If your agents generate proposals, contracts, reports, or review packs, this layer is the missing piece of document automation between the model and the final .docx. This article is a first look at what the release contains and how it works.

The core idea: the agent never writes document bytes

The design decision behind OfficeAgent.NET is simple: the language model never produces .docx content directly.

Instead, the agent expresses intent as a plan: a typed, JSON-serializable list of operations. Replace this clause as a tracked change. Fill that content control. Add a row to this table. Attach a comment to that paragraph. JSON works well here because models produce it reliably and .NET code can validate it strictly. The library translates the plan into the Open XML manipulations that carry it out.

Version 0.1 ships 15 operations for text, table, image, and styling manipulations, for comments and document properties, and for accepting or rejecting existing tracked revisions.

The workflow: inspect → find → preview → commit

Every edit follows the same four steps:

  1. Inspect - the agent gets a structured map of the document: the outline, paragraphs with stable ids, styles, content controls, tables, images, and revisions.
  2. Find - the agent searches for text and gets an address back for each match.
  3. Preview - the plan is checked against the current document. Nothing is written; the caller gets a before/after report and any validation errors.
  4. Commit - the plan is applied as one all-or-nothing transaction. If any step fails, nothing is written.

The addresses are called anchors, and they are the safety mechanism. The library issues anchors from inspect and find; the agent reuses them and never invents one. Each anchor carries the content it expects to find, and at commit time the library re-checks it against the live document. If the document changed in the meantime, the operation fails safely instead of editing the wrong place.

Document providers: filesystem now, SharePoint and more later

OfficeAgent.NET does not read and write files directly. Documents come from document providers, an abstraction that lets the library work with any file source. The application registers a document with a provider (for the filesystem provider, by its path) and receives an opaque document id in return. From that point on, inspect, find, preview, and commit all address the document by that id, and the provider takes care of loading from and saving to the underlying store.

Version 0.1 ships a filesystem provider. Other providers, such as SharePoint, a database, or any custom store, can implement the same interface, and more will become available in later versions.

Editing Word documents from a Microsoft Agent Framework agent

The main scenario for OfficeAgent.NET is an agent doing the editing itself. For that, the workflow is exposed as agent tools over Microsoft.Extensions.AI: inspect_document, find_in_document, preview_plan, and apply_plan. Wiring them into a Microsoft Agent Framework agent looks like this:

using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.DependencyInjection;
using OfficeAgent.AgentFramework;
using OfficeAgent.Core;
using OfficeAgent.Core.DocumentProviders;
using OfficeAgent.Word;

var services = new ServiceCollection()
    .AddWordFormat()
    .AddFileSystemDocumentProvider("workspace", "/srv/officeagent/workspace")
    .AddOfficeAgent()
    .BuildServiceProvider();

var client = services.GetRequiredService<OfficeAgentClient>();

// Register the document with the provider; get an opaque id back.
var doc = await client.RegisterAsync(
    "workspace", "/srv/officeagent/workspace/contract.docx");

var tools  = new OfficeAgentTools(client).AsAIFunctions();
var prompt = $"You are editing documentId={doc.ItemId} on connectionId=workspace.\n\n"
           + OfficeAgentTools.SystemPromptGuidance;

AIAgent agent = new ChatClientAgent(
    chatClient,                       // any Microsoft.Extensions.AI IChatClient
    instructions: prompt,
    name:         "OfficeAgent",
    description:  "Edits Word documents using OfficeAgent.NET.",
    tools:        tools.Cast<AITool>().ToList(),
    services:     services);

From here the agent drives the document itself: it inspects, finds anchors, builds a plan, previews it, and applies it through the tools. OfficeAgentTools.SystemPromptGuidance provides ready-made instructions that teach the model the inspect → plan → apply protocol. apply_plan saves the result and returns an output document id rather than sending .docx bytes back through the model; the host reads the result from storage and delivers the file through its own download or attachment API. A runnable Azure OpenAI example is in the samples/AgentEdit project.

Using OfficeAgent.NET as a .NET library

The same workflow can also be driven directly from C# code, without a model in the loop:

// service registration and document setup are the same as in the agent example

var client = services.GetRequiredService<OfficeAgentClient>();
var doc = await client.RegisterAsync(
    "workspace", "/srv/officeagent/workspace/contract.docx");

// inspect → find → preview → commit, addressing the document by its id
var inspect = await client.InspectAsync("workspace", doc.ItemId);
var hit     = (await client.FindAsync(
    "workspace", doc.ItemId, new FindQuery("Acme Corp"))).First();

var plan = new DocumentPlan
{
    Snapshot   = inspect.Snapshot,          // opt in to drift detection
    Operations = new PlanOperation[]
    {
        new ChangeTextOp { Target = hit.Anchor, With = "Globex Inc.", Mode = ChangeMode.Tracked }
    }
};

var preview = await client.PreviewAsync("workspace", doc.ItemId, plan);
if (!preview.IsValid) { /* surface preview.Errors */ return; }

var result = await client.CommitAsync("workspace", doc.ItemId, plan);
if (result.Committed)
{
    using var saved = await client.OpenReadAsync(result.Document);
    // saved.Stream holds the edited bytes; result.Document.ItemId is the new id.
}

The tracked edit lands as a real Word revision, with insert and delete runs that show up in Word's review pane, not as flattened text. The samples/QuickEdit project in the repository contains a runnable version of this flow.

What 0.1 covers, and what it doesn't yet

Version 0.1 covers Word .docx documents and the full editing workflow: inspect, find, preview, and transactional commit, protected by content-verified anchors and optional snapshot-based drift detection. The release includes the 15 operations described above, the filesystem document provider together with the interface other file sources can implement, the agent tools for Microsoft Agent Framework, two runnable samples (QuickEdit and AgentEdit), and a documentation set.

This is a 0.1 preview release. Expect rough edges, and expect some APIs to change based on feedback.

Try it, and tell me where it breaks

dotnet add package OfficeAgent.Core --prerelease
dotnet add package OfficeAgent.Word --prerelease
dotnet add package OfficeAgent.AgentFramework --prerelease   # for the agent-tools path

The source, samples, and documentation are on GitHub: https://github.com/ilia-sokolov/OfficeAgent.NET

This is early work, but the direction is clear: make real Word documents usable from agent workflows without requiring every agent developer to become an OOXML expert. If your agents need to produce real Office documents and this is (or isn't) the shape you would want, open an issue and tell me what's missing. That feedback is the whole point of shipping this early. And if the direction is useful to you, a star on GitHub helps other agent developers find it.