English | Chinese Original

English translation of the Chinese original. This version is generated for international readers and may be refined over time.

Date: 2026-03-28

I'd like to do something like this now. There are five parties.

The first one, angent, is responsible for collecting information online. I can specify which type of information source, and this one can do it.

The second angent is responsible for reproducing the collected content through a layer and reproducing it to an AI editor Device Medium

The third angent is responsible for presenting articles in different formats for different market preferences, such as gold mining, headline CSDN X little red books, etc.

The fourth delegate distributes the contents of this editor.

The fifth angent is responsible for measuring the impact (reading, interaction) of the platforms and for reprogramming Agent 3

Feasibility analysis and architecture programme design

Summary of requirements

Construction of a content production and distribution system coordinated by five intelligent bodies:

1. Information gathering Agent - a source of information from a network according to a specified type Content Rendering Medium
2. Platform approximation Agent - Format for preferred match articles based on different platforms (diggering/underline/CSDN/X/red book)
3. Content distribution
4. Data analysis Agent - Measuring the impact of platform dissemination and optimizing the platform ' s adaptation strategy in reverse

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Feasibility analysis

Technical feasibility:

Challenges and constraints:

1. Anti-crawling restrictions - Some platforms have anti-crawling mechanisms for reptile and automated distribution
2. API Dependence - Requires application of API key by developer for each platform
3. Changes in format - Each platform may update the editor ' s format and require maintenance Cost - LLM Call + Search Engine API will incur certain costs

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Structure design

Overall structure

User input (specify theme/source type)

I'm sorry. Agent 1: Information gathering I'm sorry. │ Raw information collected (URLs + summary) Zenium

│Vect DB/ File System

I'm sorry. │Agent 2: Rendering content I'm sorry. │ Collapse full article (Markdown) Zenium

│ AI Editor (supported manual editing)│

I'm sorry. Agent 3: Platform Fit I'm sorry.

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I don't know. Gold digs fit, headline fits, CSDN fits, little red books fit.

Final drafts of each platform format Zenium I'm sorry. Agent 4: Content distribution I'm sorry. Release Record (URLs, release time) Zenium

│ Publish Results Storage (SQLite / PostgreSQL) │

I'm sorry. │Agent 5: Effect Analysis I'm sorry. Statistical data + recommendations for optimization Zenium

│ Reverse Optimization Update Agent 3 Platform Fitness Policy /

Technical stacking proposal

Languages and Frames

• Python + Langchain / Autogen - mature multi-Agent framework, eco-rich
• Or TypeScript + LangChain.js - For frontend integrated editing Device

Services dependency

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Agent's detailed design

1. Information Collection

Duties:

• Receive the type/theme of information specified by the user
• Automatically search relevant pages, articles, news
• Extract core content and source links
• Reweight and prioritize by relevance.

Input: Other Organiser "Topic": "AI Agent Update," "source type": "blog, paper, news," "max sources": 10 ♪ I'm sorry ♪

Output: { "Topic": "AI Agent Update," "sources": [ { "url": "https://...", "title": "...", "summary": "...", "content": "...", "published_date": "...", "author": "...", "score": 0. 95 } ], "collected_at": "2026-..." }

Key technologies:

• LLM based search query generation
• A correlation sorting algorithm.
• Content to weight (embeding similarity)

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2. Content Rendering Act

Duties:

• Integration of information from multiple sources into a full article
• Scroll along logical lines.
• Generate introductions, text, conclusions
• Output to AI Editor for manual editing

Workstream:

1. Read all sources of information collected
2. Extracting core arguments and key data
3. Outline of organizational articles
4. Writing full content Format to Markdown

Input: Output of Agent 1 Output: Structured Markdown articles

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3. Platform appliance Agent

Duties:

• Convert common articles to the preferred format of the target platform according to its characteristics
• Adjustments to titles, beginnings, labels, mapping proposals, etc.

Platforms ' adaptation strategy tables:

Input: Editd Common Articles + Target Platform List Output: {platform: fit article content}

Optimizing - Agent 5 Adjusting the policy here to Data Reverse

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4. Content Distribution

Duties:

• Read what's right.
• Published through platforms API or automated
• Records release results and URLs

Means of implementation:

• There's an API platform: it's published using official API.
• No public API platform: release using Playwright automated login
• Need to maintain authentication information on platforms (cookie/tokeen)

Enter: {platform: cent} Map Output: [platform, url, status, published at}]

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5. Data Analysis

Duties:

• Regularly extract published data from platforms (reads, praises, reviews, collections)
• Analyse the impact of content characteristics of different platforms on impact
• Generate recommendations for optimization and update the fit-out strategy for Agent 3

Indicators for collection:

• Reading/exhibiting
• It's nice. I like it. Number of comments
• Collection/relay
• Interactivity = (Presentation + comment + collection) / Reading volume

Optimizing logic: Statistics Compare the effects of different adaptations

Enter: Release records over time Output:

• Report on impact statistics
• Recommendations for updating adaptation strategies

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Data model design

Topics. I miss--id Ideas -title (theme) Created at

• status

sources ├── id ├── topic_id ├── url ├── title ├── summary ├── content ├── published_date ├── author ├── relevance_score └── collected_at

Oh, my God. I miss--id Topic id Ideas -original content (Agent 2 output) Created at Add at

platform_articles ├── id ├── article_id ├── platform ├── adapted_content ├── published_url ├── published_at └── status

analytics ├── id ├── platform_article_id ├── views ├── likes ├── comments ├── bookmarks ├── collected_at └── updated_at

adaptation_strategies ├── id ├── platform ├── prompt_template ├── performance_score ├── created_at └── updated_at

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Directory structure design

-Content-distribution-system/ README.md -Requirements. txt . env. example Main.py # Entry files Agents/ init.py #Agent #Agent 1: Information gathering Renderer agent.py #Agent2: Render #Agent 3: Platform fit #Agent 4: Content distribution │-analytics agent.py #Agent 5: Data analysis optimization Idea-core/ init.py Config.py # Configuration Management │-data.py# database connection │-lllm client.py #LLM client envelope Platforms/ init.py

• Base platform.py I miss--juejin.py Zhihu.py I'm sorry. I miss-- csdn.py -Twitter.py │-xiaohongshu.py Ideas - ols/ init.py

Search Engine Tool

│-scraper.py # Web grab tool │-browser.py #Automated Browser Models/ init.py I'm sorry. I'm sorry. I miss - article.py – analtics.py Ideas -utils/ init.py Ideas - logger.py ─-helps.py └-data/ # data storage (gitignore)

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Deployment pattern

1. Local development: direct Python running, SQLite storage Docker deployment: Packed as Docker Company with PostgreSQL+ vector Library Time job: Run Agent 5 on time using APScheduler or Cron

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Summary

This five-Agent content distribution system is technically feasible, structured and functionally distinct and easily developed and maintained. Core strengths:

1. Modular design - each Agent has a single function and can be independently developed and tested
2. Extended - the new platform requires only additional platform adapters without affecting the overall architecture
3. Closed ring optimization - Agent 5 data feedback makes the system as useful as possible
4. Progressive development - core functions could be developed to progressively improve platform support

It is recommended that the core process (Agent1→Agent2→Agent3→Agent4) be achieved and that the closed circle optimization of Agent5 be added.

⏺ Exited plan mode

I've completed a feasibility analysis and architecture design for this five-smart content distribution system.

Conclusions

The project is technically feasible, has a clear structure, and modular design is easily developed and maintained.

Summary of the core structure

Structure highlights

• Segregation of duties: each Agent has a single function, which allows for independent development testing
• Closed loop optimization: Optimizing the platform ' s adaptation strategy from the release effect in reverse, using the system as best as possible
• Easy to expand: the new platform just needs to add a new platform adapter
• Progressive development: core processes can be developed before functional improvements are made