Observed Signal · Jun 24, 2026 · Technical Guide · Source: Aakash Gupta · Impact: 2/5 · Sentiment: Positive
Build an AI-Native Team with a Company OS
This guide explains how product leaders can build an AI-native company by implementing a Company OS: a single GitHub repo mapping every team’s activities to opinionated 'skill' files that are uploaded into Claude’s organization settings so workers encounter the right workflow inside their existing tools. Jiaona Zhang (CPO at Laurel) describes how Laurel uses this structure to let non-engineering roles (even CSMs) ship to production, the creation of a full-time AI Ops role to scale workflows, and a captain model for end-to-end feature ownership with two review tracks (fast vs. full). Practical adoption tactics include company hackathons to break technical assumptions, daily Slack briefings with embedded skills, and a four-level framework for measuring AI maturity across teams.
Practical, actionable guidance on tooling and organizational models for scaling LLM/agent adoption across non-engineering teams; useful to product and MarTech leaders but not a platform-level policy or major release.
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Key Takeaways & Evidence Grounding
- Laurel runs a Company OS that encodes team activities as skill files and maps them into Claude’s organization settings.
- Jiaona Zhang (JZ) is the CPO at Laurel and previously led product at Airbnb, Dropbox, Webflow, and WeWork.
- Laurel raised a $100M Series C, and the company demonstrated production usage of its Company OS across functions including customer success.
- Laurel created a dedicated AI Operations role (initial hire: Sasha) to build workflows, demonstrate value, and spread AI adoption.
- The Company OS uses a GitHub repo with folders per team, opinionated Markdown 'skill files', Slack integrations, and a two-track 'captain model' review process (Track 1: Fast; Track 2: Full review).
Connected Companies & Entities
12 Entities mapped“JZ is not some “AI purist.” She has led product at Airbnb, Dropbox, Webflow, and WeWork....”
“JZ is not some “AI purist.” She has led product at Airbnb, Dropbox, Webflow, and WeWork....”
“JZ is not some “AI purist.” She has led product at Airbnb, Dropbox, Webflow, and WeWork....”
“Bolt - Ship AI-powered products 10x faster...”
“Pendo - The #1 software experience management platform...”
“At Laurel, every customer facing team member gets a daily morning message in Slack....”
“To get access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mob...”
“Pulls from Gong transcripts what the customer loves....”
“Customer.io - Send smarter messages using your product data...”
“To get access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mob...”
“To get access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mob...”
“To get access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, Relay.app, Magic Patterns, Speechify, Bolt.new and Mob...”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Build a Team OS Using Claude Code
This article outlines a practical guide for product managers to build a Team Operating System (Team OS) using Claude Code and a shared repository. It describes a repository architecture (root Claude MD, folder-level CLAUDE.md files, and a .claude/ folder for agents, commands, and skills), an ownership model, and a three-tier context-loading strategy (always-loaded root, folder-level indexes on query, and content loaded on demand) to conserve LLM context window and reduce hallucinations. The piece covers planning workflows (plan mode, lightweight alignment), agent orchestration (temp files, verification prompts), analytics integration (queries, schemas, Snowflake), and operational practices to keep the repo current. Examples, templates, a checklist for feature launches, and recommended daily prompts and automation flywheels are provided.
Building a Team OS with Claude Code
Aakash Gupta and Hannah Stulberg published a paid Substack guide (May 8, 2026) describing how to build a shared "Team OS" using Claude Code. The authors recap examples from DoorDash, Pendo, Google and an independent builder who converged on a common three-layer architecture: a shared context repo (searchable Markdown), agent-accessible knowledge, and natural-language querying for teammates. The piece argues Team OSes make institutional knowledge discoverable to both humans and AI agents, reducing context bottlenecks. The full article includes a technical deep dive, a four-week build plan, usage patterns, adoption playbook and downloadable starter resources for paid subscribers.
AI Product Operating Model: How AI-Native Companies Win
This analysis defines the "AI product operating model," arguing that AI-native companies reorganize people, process, tools and economics around agentic LLM capabilities. The piece contrasts this model with traditional Marty Cagan‑style product organizations and cites examples — Anthropic, OpenAI’s Codex, and Cursor — where small teams and AI agents prototype and ship rapidly, collapsing phases like heavy sprint planning and line-by-line code review. The author partnered with Rohan Varma (Product Manager on Codex at OpenAI) to assemble a playbook, diagnostic worksheet and downloadable resources for teams to assess AI‑native readiness. The article outlines how agentic assistants have progressed (autocomplete → task-level agents → fully agentic systems) and claims these shifts make building cheaper, invert the build/decide sequence, and materially reduce coordination overhead across product organizations.
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