Linear’s AI Agents Are Transforming Product Development Workflows

Linear’s AI Leap: From Issue Tracker to Agent Orchestration Platform

July 29, 2025, 2:38 am IDT
Updated: July 29, 2025, 9:30 pm IDT

Linear started as a product development platform that many initially saw as “just an issue tracker.” But it’s now moving beyond that label by embedding AI agents as core collaborators within engineering workflows. At the AI Engineer World’s Fair in San Francisco, Tom Moor, Linear’s Head of Engineering, shared how the company is shaping a future where AI teammates work alongside developers, not just as tools but as active participants.

Linear has already earned trust from high-performance teams like OpenAI, Ramp, and Vercel. Now, it's leveraging AI to fix persistent inefficiencies in product development cycles.

Starting Small with AI

Linear’s AI journey began in early 2023 with a small, focused team experimenting with practical applications. Despite lacking prior AI expertise, they concentrated on delivering immediate value—features like summarizing issues and natural language filtering. Early attempts at a “Copilot” style assistant were paused because the quality didn’t meet Linear’s high standards.

This cautious approach—favoring subtle, dependable AI over flashy but unreliable features—earned respect from industry watchers who praised Linear’s “subtle + useful AI implementations.”

AI Advances Fuel New Possibilities

By late 2023 and early 2024, AI models improved significantly. Technologies such as the o3 model, multimodal capabilities, and expanded context windows made AI experiments less fragile and more intelligent. Moor described this phase as a “leap forward” that reinvigorated the team’s efforts.

Linear adopted a hybrid search system combining Turbopuffer with Cohere embeddings to build what they call “Product Intelligence.” This semantic graph connects issues through query rewriting, hybrid search, and reranking, giving teams a clear map of how issues relate to each other and their broader context.

Agents as Cloud-Based Teammates

Linear’s vision goes beyond adding smart features. They are building a platform where AI agents become scalable cloud-based teammates, each with their own identity and audit trails. These agents can interact with issues, create plans, and even troubleshoot bugs.

Examples include:

• CodeGen: An AI agent that researches best coding practices and submits pull requests.
• Bucket: An agent focused on managing feature flags.

This approach aims to eliminate the problem of backlogs that never get fully addressed. AI agents will take on repetitive tasks and speed up development cycles, freeing human teams to focus on higher-impact work.

Clear and Transparent Agent Interaction

Linear emphasizes that AI agents must communicate clearly and transparently. Agents should respond quickly and precisely, using accurate terminology and leaving clear audit trails, just like human teammates. They avoid guessing or being “clever,” instead clarifying ambiguous requests and preferring silence over unnecessary noise. The goal is to ensure every AI interaction adds tangible value.

For product developers interested in how AI can integrate into workflows and automate tedious tasks, Linear’s approach offers a practical example of evolving tools that prioritize quality, transparency, and team alignment.

To explore more about AI applications in product development and automation, check out Complete AI Training’s automation courses.