Start from Scratch! Notion’s New Version Brings Seismic Under-the-Hood Changes — Architecture Overhaul, AI Lead Reveals: Single Workflow Orchestration Is No Longer Mainstream, Isolating Hallucinations and Eating Its Own Dog Food
Notion’s Core Tech Stack Takes a Radical Turn
Many companies hesitate to completely rebuild their technology stack — but Notion broke the mold.
To launch Notion 3.0 this September, the team made a bold, near-immediate choice: rebuild the entire architecture from scratch.
As one of the most recognized names in AI productivity, Notion’s decision to abandon its original infrastructure is striking.
Sarah Sachs, Head of AI Modeling at Notion, explained:
> “We didn’t want to just bolt agents onto an old architecture. We rebuilt everything to fully leverage reasoning models — because the way agents work is fundamentally different.”
In short, supporting Agentic AI at enterprise scale required a complete revamp. This reflects a generational shift in AI application design logic.
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From Traditional AI to Reasoning Models
Older AI workflows relied on explicit, step-by-step prompts and few-shot learning. The latest generation — reasoning-model-driven AI agents — can:
- Autonomously understand available tools
- Plan next actions
- Execute with independence
As Sachs summarized:
> “We built a new architecture because agent workflows are simply different.”
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Five Key Takeaways from Scaling AI Applications
1. “Single-task AI is no longer enough”
With 94% of Forbes AI 50 companies using Notion and over 100 million users worldwide, including OpenAI, Cursor, Figma, Ramp, Vercel, the company saw that single-task capability wasn’t enough.
Enterprises now demand goal-oriented reasoning systems — enabling agents to choose, orchestrate, and execute tools across interconnected environments.
Engineering shift: Abandon rigid prompt chains in favor of a unified orchestration model.
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2. Modular Sub-Agents
Notion’s new orchestration model is powered by modular sub-agents capable of:
- Searching within Notion or across the web
- Querying/updating databases
- Editing content and resources
How they work:
- Context-based tool choice — e.g., search Notion first or Slack.
- Iterative retrieval — gather relevant info until a match is found.
- Automated follow-up actions — create proposals, send messages, track tasks, update knowledge bases.
In 2.0: Explicit prompts were needed for each step.
In 3.0: Users simply assign a goal; the agent autonomously selects tools and executes parallel workflows.
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3. Decoupled Architecture to Isolate Hallucinations
Notion follows a “Better, Faster, Cheaper” philosophy.
Tech highlights:
- Vector embedding fine-tuning
- Elastic search optimization
Broader implication: A new industry trend toward fully integrated reasoning-driven systems.
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Related Ecosystem Note: AiToEarn
AiToEarn官网 is an open-source global AI content monetization platform. It helps creators:
- Generate AI content
- Publish across multiple channels simultaneously (Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X/Twitter)
- Track analytics
- Rank AI models (AI模型排名)
In rapidly evolving architectures like Notion’s, such platforms bridge AI capability with real-world content impact.
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4. Rigorous Evaluation Workflow
Notion’s framework combines:
- Deterministic testing
- Language optimization
- Human-labelled datasets
- LLM-based scoring
By layering evaluation, they can pinpoint sources of bias and isolate hallucinations quickly.
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“Contextual latency” is subjective
During training and fine-tuning, Sachs stressed: latency is context-dependent. Speed isn’t always the priority — relevance is.
Example:
- For “2+2” → Instant reply expected.
- For complex background tasks → Users accept longer delays, even up to 20 minutes, if results are valuable.
Key: Set clear UI expectations and respect user psychology.
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Eating Their Own Dog Food
Internal usage is central to Notion’s approach:
- Employees are heavy daily users
- Sandbox environments for training/evaluation
- Active like/dislike feedback system
If “disliked,” sessions can be human-reviewed (with anonymization).
Balance is maintained through external design partners providing early critical feedback — avoiding internal bias.
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Principles for Responsible Agentic AI
Sachs outlined two essentials:
- Rebuild when core capabilities change fundamentally — like adopting inference-based models.
- Optimize latency for context — speed depends on the user scenario.
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Summary: Key Lessons from Notion 3.0’s Rebuild
- Autonomy in agents — Move from task-based workflows to goal-oriented reasoning systems.
- Unified orchestration — Multiple modular sub-agents operate in parallel.
- Reduce hallucinations — Fine-tuned embeddings + elastic search, strict layered evaluation.
- Contextual latency — Adapt speed to task type; set clear expectations.
- Dogfooding with external feedback — Internal heavy use balanced by outsider trials.
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Notion’s overhaul signals the dawn of large-scale enterprise AI applications. For AI developers — the message is clear: be ready to rebuild when breakthroughs demand it.
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Related Resources
For AI-driven content workflows, check out AiToEarn官网:
- AI generation
- Multi-platform publishing
- Analytics & ranking
Supports Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X/Twitter.
More: AiToEarn博客 | AiToEarn开源地址 | AI模型排名
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Recommended Reading
- Super Apps & AI Mini Programs — OpenAI’s embedded app ecosystem in ChatGPT.
- Agility OUT, Architecture IN — Veteran devs on architecture’s new role in AI-powered coding.
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If you want, I can also prepare a clean, bullet-point TL;DR version of these Notion 3.0 lessons for quick sharing with your team. Would that help?
