Engineering

TL;DR: Turning Figma designs into production-ready code is a repetitive part of building software. To address this, we built an AI agent that reads Figma screenshots and writes the corresponding production code. Our first pilot took four days, where the same feature normally takes two to four weeks. The setup has three parts: a reference implementation the agent studies, a rules document that pins down exactly what to generate, and prompt templates that engineers fill in and run. We've published all of it to our internal skills platform so any team can pick it up.

TL;DR: On May 7, 2026, an AWS thermal event triggered a Coinbase outage. Recovery was delayed by a matching engine locked to the failed zone and a silent failure in AWS's managed Kafka service. Moving forward, we are improving our cross-zone standbys for our low-latency exchange and updating Kafka infrastructure.

TL;DR: AI coding agents made individual engineers faster, but the workflow around them stayed sequential. That’s changed. At Coinbase, engineers are evolving from implementers into orchestrators of agent fleets. The evidence is Mux, an internal multi-agent tool that started as one engineer's side project and grew organically to 600+ users across every org. Power users now merge 3.5x more PRs than baseline.

TL;DR: At Coinbase, ML models handle long-term fraud defense while rules let us respond fast to active attacks. We rebuilt the backtesting data layer, automated schema evolution, and gave analysts a standardized notebook workflow backed by ML libraries. Backtesting is now faster, and analysts can go from spotting a fraud pattern to shipping a new rule in hours, not days.

TL;DR: We built an AI-powered Case Grading Assistant that reduces trainee case review time from ~90 minutes to ~20 minutes – while maintaining human oversight and full auditability. The system uses comparison-based grading (evaluating agent work against known-good reference cases) via LLMs, minimizing hallucination risk and improving consistency at scale. Key insight – anchoring evaluations to explicit reference cases and rubrics enabled faster reviews while supporting more consistently calibrated outcomes.

TL;DR: Coinbase processes over a billion payout transactions a year across staking rewards, USDC rewards, and Coinbase One benefits. We’re sharing the evolution of our Payout Framework—from the limitations of synchronous processing to a high-throughput async architecture that handles rewards across dozens of assets with precision.

Tl;dr: Coinbase has developed a novel, dynamic control policy that replaces static rules to automatically manage risk, resulting in superior financial loss mitigation and more efficient utilization of constrained resources.

TL;DR: Building retail onchain products traditionally requires teams to manage a fragmented stack: smart wallets, bundlers, paymasters, and complex UserOperation construction. To solve this, we engineered two foundational layers: MagicSpend, an atomic onchain funding primitive that authorizes debits from Coinbase balances with revert-safe semantics and Magic Platform, an abstraction layer that codifies ERC-4337 infrastructure. Together, they reduced product shipping timelines by 3x and have processed over $1B in volume.

TL;DR: By leveraging a "Context-First" AI workflow, we shipped Coinbase Business Invoicing in weeks instead of months. We transitioned from manual UI mocking to code-first prototyping and used scoped repository sandboxing to enable safe, cross-stack iteration.

Tl;dr: To meet the scaling demands of Solana, Coinbase has moved away from its legacy chain-agnostic processing model. We engineered a dedicated, high-throughput streaming architecture with parallel block processing, resulting in a 12x increase in transaction processing throughput and a 20% reduction in deposit latency.

We built Smart Targeting to move beyond using manual segmentation to build target audiences towards automated, intelligent discovery.