Looking for inspiration on how to apply AI in your team? Then check Uber: Leading engineering through an agentic shift where the Dev Platform team presents Uber's AI adoption approach and its current state.
Uber's agent platform includes:
🔸 MCP Gateway & Registry. Central MCP gateway to expose external and internal MCPs and provide a secure sandbox for experiments.
🔸 ML Michelangelo platform. An agent builder with no code or SDK solutions with built-in visualization, telemetry, tracing.
🔸 AIFX. A tool to access internal agents infrastructure: provisioning, discovery, configuration, background tasks.
🔸 Minion. Background agent platform to integrate them with CI\CD, slack, PRs.
🔸 Code Inbox. Unified inbox for PRs developers need to review. It tries to find the most relevant person to review the code, track review SLOs, help reassign PRs or make escalation if necessary.
🔸 uReview. Review pipeline that enriched with internal context, best practices, guidelines.
🔸 Autocover. A system to generate unit tests. 3x higher code test quality than generated by generic agent, 5000+ tests generated per month.
🔸 Automigrate. A tool to implement large-scale changes. It consists of problem identifier, code transformer (openwrite, piranha or agents), validation, and campaign manager (route PRs to reviewers, split changes on reasonable PRs, rebases, changes prioritization)
As you can see the team uses AI to support the entire development lifecycle. The whole strategy sounds like "Enable Uber engineers to focus on creative work by eliminating toil". By
toil they mean upgrades, migration, bugfixes, writing docs, cleanup. To sum up, Uber has a very reasonable strategy for AI adoption. Of course, they made a huge investment to their agent platform. But one simple recipe suits everyone: define the most tedious repeatable tasks and give it to AI. People are burnt out, agents are not.
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