Ci cd

Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic

Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic

Sachin Malhotra's talk outlines a critical framework for safely deploying autonomous agents in production, moving beyond simple token-based access. He introduces 'asymmetric verbs,' refilling 'rate limits,' 'trip wires' for aggregate monitoring, and the 'undo test' as a lens. A central tenet is that infrastructure (via a proxy) must stamp an agent's identity, preventing agents from circumventing controls and ensuring accountability, thus providing a "budget" instead of an unbounded "token."

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

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

Uber has transformed its software development with an agentic AI-powered factory, leading to a dramatic increase in engineer productivity. The presentation details six key infrastructure components: a unified model gateway with strict PII and safety guardrails, an MCP gateway for streamlined agent tool access and token optimization, agentified dev pods for rapid execution, a managed skills marketplace, a comprehensive context graph, and the Cortana AI assistant. Adam Huda then demonstrates an end-to-end feature development workflow, highlighting a critical shift to inner-loop validation (stopping short of CI) and automated, managed maintenance loops. The ultimate takeaway is that the bottleneck has moved from technical execution to strategic decision-making: "should we build it?" rather than "can we build it?"

Guide, Verify, Solve — Anirban Chatterjee, Sonar

Guide, Verify, Solve — Anirban Chatterjee, Sonar

Anirban Chatterjee discusses the emergence of "verification debt" in AI-driven software development, where AI-generated code introduces persistent quality issues despite initial productivity gains. He proposes a **zero-trust, multi-layered verification** approach, integrating it into an "Agent-Centric Development Cycle" (ACDC) to ensure code quality, security, and compliance by empowering AI agents with real-time feedback and remediation capabilities through tools like SonarQube, Gitarr, and Sonar Vortex.

Front End Testing with GitHub Actions • Amy Kapernick • YOW! 2025

Front End Testing with GitHub Actions • Amy Kapernick • YOW! 2025

This presentation by Amy Kapernick at YOW! Australia 2025 details how to implement robust front-end testing using GitHub Actions. It covers the rationale for comprehensive front-end testing, the necessity of a live deployment preview, and practical steps for automating tests like Lighthouse for performance/accessibility and Playwright for UI validation. The talk also delves into continuous deployment workflows and explores GitHub Actions' broader utility for workflow automation beyond testing, such as generating issues from code comments.

What Is AI Code Refactoring? Agentic AI & Safe Code Changes

What Is AI Code Refactoring? Agentic AI & Safe Code Changes

This video explores AI code refactoring, differentiating between inline and autonomous agentic approaches. It highlights how AI can leverage pattern recognition for tasks like improving readability or reducing duplication, thereby addressing technical debt. A key focus is on the safety guardrails, detailing a multi-step, human-in-the-loop process involving planning, searching, reporting, human approval, patching, and verification through testing, ensuring AI-driven changes are safe for production and can integrate into CI/CD pipelines.

BDD, ADR, PRD, WTF: Capturing Decisions for Humans and AI Alike — Michal Cichra, Safe Intelligence

BDD, ADR, PRD, WTF: Capturing Decisions for Humans and AI Alike — Michal Cichra, Safe Intelligence

Michal Cichra from Safe Intelligence explains how to maintain consistency in AI-driven software development by capturing decisions and enforcing rules. He argues for reviving Behavior-Driven Development (BDD) with Cucumber to close the loop left by spec-driven development. The core idea is to enforce architectural and product decisions (ADRs, PRDs) through an automated loop of git hooks and CI, ensuring both human and AI developers adhere to established standards.