Releasesby IbisNotion 3.8: Context for your agents
Update11 days agoUnverifiedAdded Oct 20, 2026
Notion 3.8: Context for your agents
Topics: AI agents
More Notion releases
Every Notion releaseAgents, rebuilt to do more for less
Agents, rebuilt to do more for less
Notion 3.7: Agent skills for your whole team
Notion 3.7: Agent skills for your whole team
Control which AI models your agents can use
Control which AI models your agents can use
Ask your agent to suggest edits
Ask your agent to suggest edits
Your Developer Portal, now in the sidebar
Your Developer Portal, now in the sidebar
Model selection, simplified
Model selection, simplified
Share context with Custom Agents from the Share menu
Share context with Custom Agents from the Share menu
AI Meeting Notes can now trigger Custom Agents
AI Meeting Notes can now trigger Custom Agents
Also shipped on Oct 13, 2026
Notion in October 2026What's included:
- Three training methods: Supervised fine-tuning (SFT) , direct preference optimization (DPO) , and reinforcement learning with group relative policy optimization (GRPO) . Each method trains a low-rank adapter (LoRA) or the full model weights. Custom loss functions : Train with any objective you compute in PyTorch from the model's log-probabilities.
The AI.AGG function is generally available (GA)
The AI.AGG function is generally available (GA). You can use this function to semantically aggregate unstructured input data based on natural language instructions.
BigQuery generative AI functions now support the gemini-4-argon model
BigQuery generative AI functions now support the gemini-4-argon model. This model is generally available .
Gemini 4 Argon in CodeMender (Preview)
CodeMender now supports Gemini 4 Argon. To use it, pass --model gemini-4-argon to cm find , cm verify , or cm fix . Gemini 3.8 Flash remains the default model. For more information, see Specifying the model .
Gemini 4 Argon and Gemini 4 Argon Cyber Permissive
Gemini 4 Argon ( gemini-4-argon ) is available in General Availability (GA) . Gemini 4 Argon is Google's frontier model that excels at deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science.
On-demand state repartitioning is now generally available
On-demand state repartitioning is now generally available. You can resize the number of shuffle partitions for a stateful Structured Streaming query or Lakeflow pipeline without losing checkpoint state. See On-demand state repartitioning for stateful streaming queries .