Tom Moor introduces himself as the engineering lead at Linear, presenting Linear’s story with AI, the features built, and views on the future of software development.
Linear started as an issue tracker, evolving into an operating system for engineering and product teams, focusing on speed, clarity, and friction reduction.
By early 2023, Linear initiated a small internal team to experiment with AI, particularly for summarization and similarity features, without prior AI experience.
Early efforts centered around search, leading the team to experiment with vector databases, ultimately settling on OpenAI embeddings stored in PG Vector on GCP for pragmatic reasons.
Initial AI-powered features included “similar issues” suggestions using basic cosine embedding comparisons, natural language filters for issue navigation, and automatic issue creation from Slack threads.
A co-pilot feature was attempted but not shipped due to not meeting Linear’s quality bar.
Advancements in AI Capabilities and Platform Foundations 04:44
By late 2024, increased AI capabilities (larger context windows, planning and reasoning models, multimodal APIs) led Linear to rebuild its search index.
Transitioned to hybrid search using Turppuffer, moved embeddings from OpenAI to Coher for improved performance, and completed a large-scale backfill of embedding data.
These advancements established a more robust search foundation for new features and integrations.
Developed “product intelligence,” an improved similar issues feature using a pipeline with query rewriting, hybrid search, reranking, and deterministic rules.
This feature maps relationships between issues, explaining how and why they relate, enabling suggested labels, assignees, possible duplicates, and relevant project/team matches.
Enables efficient triage for large organizations processing many tickets.
Customer Feedback Analysis and Workspace Updates 07:24
Added customer feedback analysis, leveraging LLMs to synthesize feedback from multiple channels and recommend features or projects, reportedly outperforming 90% of job candidates in analysis tasks.
Introduced daily or weekly "pulse" features that summarize workspace updates, available as audio via the mobile or desktop app for convenient consumption.
Created an “issue from video” feature that parses video bug reports to identify reproduction steps and automatically generate issues, saving time and effort.
The Agent Platform: Coordination and Extensibility 09:39
Linear aims to make the platform pluggable to accommodate diverse team workflows, introducing the concept of agents as scalable, cloud-based teammates.
Recently launched a platform for agent integration, supporting agent orchestration alongside human users within the same workspace.
Demonstrated integration with coding agents (e.g., Codegen, Charlie) capable of planning, creating PRs, conducting root cause analysis, and linking issues to code.
Bucket, a feature flagging platform, is integrated as an agent that can create and manage feature flags directly in Linear.
Discussion of PM agents and Intercom's Finn agent, which can automate customer replies when issues are resolved.
Working on richer agent surfaces: making agent deliberations and tool calls visible, allowing users to interrupt agents mid-task, and supporting agents across different roles.
Widespread agent adoption will reduce backlog size and eliminate excuses for unresolved issues, with agents able to process and potentially resolve a significant portion automatically.
Anticipates increased productivity and quality as agents take on repetitive or menial tasks, enabling teams to build more and faster.