Blog - Atlassian Insights & Tutorials

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Run AI on your own hardware with LM Studio — the first Jira app to do it — or zero-key inside Atlassian with the new Forge LLM provider.
Declarative AI actions go beyond validation: draft comments, create sub-tasks or linked issues, and generate DOCX/PDF/PPTX — all in plain English.
Skills, Memories, and a shared documentation library ground every decision in your team's real context.

CogniRunner — the semantic layer for Jira workflows — just got its biggest update yet. If you've used it to block a vague bug report or flag a likely duplicate, the core idea is familiar: write a plain-English prompt, pick a field, and let an AI model judge , not just structure. This release pushes that idea in three directions: the AI runs, it can do after it decides, and it understands your project.

Option	What it means	Best for
LM Studio (local)	Inference and your field data stay on your own hardware. Zero per-token cost.	Maximum privacy, air-gapped or cost-sensitive teams.
Atlassian Forge LLM	Zero-key, runs inside Atlassian's platform — no API key, no external egress.	The fastest start; data never leaves Atlassian.
BYOK cloud	Bring your own key: OpenAI, Anthropic, Azure OpenAI, OpenRouter, AWS Bedrock.	A specific model, vision support, or your own account.

What's New in CogniRunner: Local AI, Zero-Key Models & Actions That Write Back

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Run AI where your data lives

From validation to action

Smarter context: Skills, Memories & the docs library

Agentic by default — the AI searches Jira for you

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