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DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags

DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow comb…

Feature InjectionA/B TestingDeveloper ExperienceExperiment Driven DevelopmentStatic AnalysisAI Assisted CodingAutomationModel Context Protocol (MCP)ProductivityAI ArchitecturegitAI CodingLarge language modelsgithubAgentsFeature ToggleDevOpsAI, ML & Data EngineeringArchitecture & DesignDevelopmentnews
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DoorDash’s Flux Runs 130,000 Engineering Tasks Through Cloud-Based Agents

DoorDash has moved engineering agent workloads from developer laptops to its Flux cloud platform. The platform automated 130,000 engineering tasks i…

AI CodingVMSoftware EngineeringAgentsCode ReviewsAI DevelopmentAI SecurityAI HarnessContinuous DeploymentInfrastructureCloudContinuous ImprovementAI ArchitectureDeveloper ExperienceAIOpsDevelopmentAI, ML & Data EngineeringDevOpsArchitecture & Designnews
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Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models

Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic logic and non-determinist…

AgentsQCon AI Boston 2026AI ArchitectureAI SecurityModel Context Protocol (MCP)Agentic AI ArchitectureAI Cost OptimisationSemantic WebLarge language modelsData MeshTranscriptsAI, ML & Data EngineeringArchitecture & DesignDevelopmentpresentation
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