Coinbase Forge

Executive Summary

Coinbase's internal coding agent — now called Forge, previously Cloudbot and originally Claudebot — produces 5% of all merged pull requests and reduced PR cycle time from ~150 hours to ~15 hours. Built by just two engineers initially, it has evolved into a Slack-native, Linear-integrated system serving 1,000+ engineers. LangChain identified Forge (alongside Stripe's Minions and Ramp's Inspect) as one of three independent implementations of the same architectural pattern, which they open-sourced as Open SWE.

Attribute	Value
Company	Coinbase
Current name	Forge
Previous names	Cloudbot, Claudebot
Type	Internal tool
Adoption	5% of merged PRs
PR cycle time	150h → 15h (10x)
Initial team	2 engineers
Interface	Slack, Linear, GitHub

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Name Evolution

The naming confusion is worth clarifying:

• Claudebot — the original name, reflecting its Claude-based origins. Used in early internal references and Chintan's February 2026 tweet.
• Cloudbot — the name used by LangChain, DevOps.com, and most third-party coverage after the system went multi-model. Chintan clarified: "it's actually using all sorts of underlying models. It's not something that is specific to Claude."
• Forge — the current name, used in the Linear case study (the most detailed public source). Described as "a custom agentic harness his team built that can be executed from Slack, Github, and Linear."

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How It Works

The Linear case study provides the clearest picture of Forge's workflow:

Bug-to-Fix Pipeline

Slack message flags a bug
    ↓
Automated workflow creates Linear issue
    ↓
Issue auto-labelled, sized, and assigned
    ↓
Bug assigned to Forge (automatic)
    ↓
Forge accesses codebase + Linear context
    ↓
Writes fix, drafts PR, creates mobile build
    ↓
Pushes back to Slack for human review

Question Handling

Forge also handles engineering questions. Instead of pulling a teammate out of deep work, questions in Slack get routed to agents: Linear handles project status and timelines, Forge handles code-specific queries like debugging services, analyzing failing endpoints, or investigating funnel dropoffs.

Linear as Source of Truth

This is a key architectural decision. Chintan told his team to treat Linear as the single source of truth for everything — product requirements, designs, bug reports, shipping status, team structure. The reason: agents need structured, accessible context to work autonomously. Scattered knowledge across Slack threads and people's heads is navigable by humans but a wall for agents.

"I'm not designing things for humans anymore," Chintan says. "I'm designing things for agents. And they need different things."

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The "Delete Your IDE" Experiment

In January 2026, Chintan asked his entire engineering organization to delete their IDEs and write zero lines of code for two weeks. Every engineer at Base had to do their job without touching a code editor. The goal: figure out how work actually gets done when agents handle most of the coding.

This forced the team to identify which workflows depended on human coding (and shouldn't) versus which genuinely required human judgment.

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Speedrun Culture

The adoption mechanism is worth studying:

• Weekly "Speedruns" — power users demo new agent workflows to the entire org, then everyone does it live
• First speedrun (early 2025) — 120 engineers pushed 80 PRs in one sitting, crashed GitHub
• CEO Brian Armstrong attending by mid-year — 500+ PRs in 15 minutes
• Crashed GitHub 4-5 times (Chintan admits with pride)
• Applied AI Award presented by CEO at quarterly town halls

The adoption wasn't driven by training programs or change management. Engineers didn't learn a new behavior — they asked questions in Slack like they always did. The only difference was who answered.

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Continuous Development Pattern

Engineers now operate in a cycle:

1. Wake up, review PRs agents generated overnight
2. Spin off 10-15 new agents to work throughout the day
3. Go heads-down on complex work themselves
4. End of day: review, launch another batch overnight
5. Repeat

Chintan tracks "autonomous operation time" as a key metric — how many minutes an agent can run without human intervention. That number keeps climbing.

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Open SWE Connection

In March 2026, LangChain released Open SWE — an open-source framework capturing the architectural pattern that three companies built independently:

• Stripe — Minions
• Coinbase — Cloudbot/Forge
• Ramp — Inspect

The convergence: all three arrived at cloud sandboxes, Slack/Linear invocation, subagent orchestration, and automatic PR creation. Open SWE provides these components as a customizable framework built on LangGraph and Deep Agents.

One additional detail from SFEIR's analysis: Forge auto-merges its PRs when tests pass and automated review is positive.

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Strengths

• Proven at scale — 5% of merged PRs across 1,000+ engineers is real production impact
• Cultural adoption mastery — speedruns, CEO buy-in, public Slack channels, awards. This is a playbook for enterprise AI adoption.
• Linear integration — structured source of truth for agents, not scattered knowledge
• Full lifecycle — bug detection in Slack → issue creation → code fix → PR → mobile build → review, all automated
• Continuous development — agents work overnight, humans review in the morning. Development never stops.
• Architecture validated — three independent implementations converged on the same pattern, now open-sourced as Open SWE

Weaknesses

• Internal only — not a product for sale, limited replicability without deep study
• 5% is still early — Ramp claims 30%, Abnormal 13%. Room to grow.
• Linear dependency — the "source of truth" approach requires committed buy-in to a specific tool
• Cost not disclosed — running agents at this scale with multiple models isn't cheap
• Name confusion persists — Claudebot/Cloudbot/Forge creates ambiguity in external references

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Competitive Positioning

Company	Agent Name	% of PRs	Key Metric
Ramp	Inspect	30%	Highest disclosed adoption
Abnormal AI	Internal	13%	Growing fast
Coinbase	Forge	5%	500+ PRs in 15 min speedruns
Stripe	Minions	N/A	1,000+ PRs/week

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Relevance to Tembo

Forge validates several patterns central to Tembo's thesis:

1. Slack-native invocation — meet developers where they are, don't make them learn a new interface
2. Structured context — agents need a source of truth (Linear), not scattered conversations
3. Continuous development — agents working overnight while humans sleep and review in the morning
4. The orchestration convergence — three major companies independently built the same architecture. This is the canonical pattern.
5. Cultural adoption matters as much as technical capability — speedruns, CEO buy-in, and public wins drove adoption more than the tooling itself

The Open SWE framework makes this architecture accessible to any team. The question for Tembo: how do you provide this as a managed service rather than asking every company to build it themselves?

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Research by Ry Walker Research

Disclosure: Author is CEO of Tembo, which offers agent orchestration as an alternative to building in-house.