The Video Signal

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Monorepos

All digests tagged Monorepos

Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber thumbnail

· 18:26

Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber

Uber has implemented a comprehensive 'Software Factory' powered by agentic AI to revolutionize its SDLC. Key infrastructure investments include a centralized Model Gateway for security and latency control, an MCP Gateway for token optimization, and a Context Graph that unifies knowledge across disparate systems. These building blocks enable autonomous agents (like Minion) to accelerate development from idea generation (via Cortana) through drafting PRs, validating changes in inner loops (e.g., comparing simulator screenshots against Figma specs), and managing maintenance tasks.

Key takeaways

  1. Agentic Adoption Metrics

    Over the last year, agentic AI led to more than 70% of Uber's Pull Requests (PRs) originating from local or cloud agents, resulting in double the lines of code per engineer year-over-year.

  2. Model Gateway Performance 2:06

    The Model Gateway handles all internal model calls through a single endpoint, ensuring PII redaction for over 20 types and running five specialized safety models under a strict 100 millisecond guardrail budget. It processes over 100 million requests daily across 800+ projects.

  3. Token Optimization 5:51

    The MCP Gateway, combined with projecting MCPS into CLI patterns and using Omni MCP, cut fleetwide token usage by more than 40%.

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Every Repo Is a Software Factory Now | Don Syme, GitHub thumbnail

· 1:04:36

Every Repo Is a Software Factory Now | Don Syme, GitHub

This talk explores the concept of 'Continuous AI,' defining it as an evolution that extends traditional Continuous Integration and Continuous Deployment (CI/CD) into subjective, automated activities like documentation updates and bug triage. The core mechanism for this is the use of GitHub Agentic Workflows, which run coding agents with strong guardrails within a bounded repository context. The discussion emphasizes that while AI offers incredible power, maintaining quality gates, controlling costs, and ensuring human oversight remain critical to building reliable 'software factories.'

Key takeaways

  1. Continuous AI vs. CI/CD 5:52

    Continuous AI extends the principles of CI/CD by applying automation to subjective activities (e.g., documentation, bug triage) that are not inherently deterministic like traditional build checks. It requires operationalizing these processes on a permanent basis [00:03:52].

  2. Bounding the Context is Key 10:42

    To prevent automated AI agents from 'going off the rails,' they must operate within a strictly bounded context (e.g., restricted to creating a single pull request or issue) [00:09:42]. This situates the automation, making it manageable and auditable.

  3. The Repo as the Unit of Production 13:59

    GitHub Agentic Workflows are designed around the repository being the primary unit of production and security boundary. This repo-centric approach aligns with established CI/CD principles while enabling advanced AI automation [00:25:19].

  4. Quality Gates and Human Review 5:12

    The focus shifts from human review as a bottleneck to creating automated, high-quality pull requests. The goal is to 'equip the reviewer' with all necessary information (e.g., performance evidence) to make informed decisions [00:52:01].

  5. Complexity Management 3:59

    For maintainers, a single supervisor orchestrator pattern workflow that can perform multiple tasks is preferred over an 'agent zoo' of many individual workflows. This simplifies maintenance and provides better cost control [03:59:00].

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