InfoQ Homepage Platforms Content on InfoQ
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QCon AI Boston: Production AI Moves beyond Prompts to Platforms, Harnesses, and Evals
QCon AI Boston 2026 focused on the operational challenges of deploying AI agents, emphasizing the need for robust production infrastructure. Key themes included improving context management, ensuring security through a "harness" around agents, and adopting a comprehensive engineering model for AI.
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Developing and Deploying a Platform that the Business Understands and Developers Actually Want
A lot of platform teams face a problem: they build a lot of really cool stuff, and then their developers don't use it. Be visible to management, talk to stakeholders and listen to their problems, make your value measurable with metrics like DORA, create narratives, and show the hidden pain to make it personal: these are lessons that Lucas Hornung and Christian Matthaei presented.
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How Open Source Enables Collaboration in Creating a Platform
A platform is a collaboration system: platform teams depend on application teams, and both need shared standards. Engineers trust a platform through its predictable behavior, not its features. Being an engineer is about problem-solving and being passionate about it. And being an engineer means sharing your passion for problem-solving.
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Airbnb Shares Architecture behind Sitar-Agent Dynamic Configuration Sidecar for Kubernetes Services
Airbnb engineers detailed Sitar-agent, a Kubernetes sidecar for dynamic configuration delivery across tens of thousands of pods, processing updates several times per minute. The system was redesigned with Java, Amazon S3 snapshot bootstrapping, and a migration from Sparkey to SQLite to improve reliability, startup performance, and configuration availability at scale.
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Shifting Platform Development from Projects to Products
A company shifted from project- to product-thinking after their platform outgrew single-team use. The limitations that they felt with their platform were one-off deliveries, lack of product vision, and weak feedback loops. They have moved toward a self-service, API-driven, multi-tenant infrastructure with clearer ownership and better abstractions.
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Building and Scaling a Platform with Project-as-a-Service
When a platform started with total developer autonomy, teams felt overwhelmed and ended up solving the same problems in completely different ways. The company shifted to enablement over support, working together with teams intensively, and helping teams feel confident and capable, turning the right way into being the easiest way.
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How a Culture of Data-Driven Conversations Can Support Platform Engineering
To provide SRE as a service, a team built a center of excellence, introducing Federated SREs and roles like production manager and technical tribe lead. They created a culture of data-driven conversations where SLOs and SLAs were democratised. Surviving growing cognitive load meant continuously simplifying architecture and embedding sovereignty and resilience into platform design decisions.
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Inside Google’s System for Coordinated A/B Testing across its Global Service Fleet
Google has shared details of its fleet wide large scale A/B experimentation system designed to standardize experiment assignment, exposure logging, and configuration propagation across distributed services. The approach enables consistent measurement across products, reduces experiment conflicts, and improves reliability of data driven decision making at scale.
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LinkedIn Consolidates Hiring Data Pipelines to Power AI Driven Talent Systems
LinkedIn introduced a unified integrations platform to standardize and reconcile hiring data across systems. The platform reduces onboarding time by 72%, improves data consistency and completeness, and enables scalable AI-driven hiring features through standardized schemas, orchestration workflows, and centralized data processing.
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Platform as a Product: Delivering Value While Balancing Competing Priorities
Software platforms must be treated as products. Success requires balancing engineering, design, usability, security, and value for internal customers and the organisation, Abby Bangser mentioned in her talk Platform as a Product. A product mindset, clear ownership, and continuous investment prevent bottlenecks, platform decay, and wasted effort, enabling scalable, sustainable value over time.
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Reducing Onboarding from 48 Hours to 4: inside Amazon Key’s Event-Driven Platform
Amazon Key modernized its event platform by adopting a centralized, event-driven architecture built on Amazon EventBridge. The redesign processes millions of daily events with millisecond latency, improves schema governance, automates cross-account routing, and reduces service onboarding time from 48 hours to four, while maintaining 99.99 percent reliability.
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Ramp Builds Internal Coding Agent That Powers 30% of Engineering Pull Requests
Ramp has shared the architecture of Inspect. This internal coding agent has quickly reached about 30% adoption for merged pull requests in the company’s frontend and backend repositories. The fintech company shared a detailed technical specification. It explains how they created a system that gives AI agents the same access to the development environment as human engineers.
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DoorDash Applies AI to Safety across Chat and Calls, Cutting Incidents by 50%
DoorDash deploys SafeChat, an AI-driven safety system for moderating chat, images, and voice calls between Dashers and customers. Using a layered text moderation architecture, machine learning models, and human review, SafeChat detects unsafe content in real time, enabling immediate actions and reducing low- and medium-severity safety incidents by roughly 50 percent.
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Solving Fragmented Mobile Analytics: Uber’s Platform-Led Approach
Uber Engineering outlines its platform-led mobile analytics redesign, standardizing event instrumentation across iOS and Android to improve cross-platform consistency, reduce engineering effort, and provide reliable insights for product and data teams.
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Benchmarking beyond the Application Layer: How Uber Evaluates Infrastructure Changes and Cloud Skus
Uber’s Ceilometer framework automates infrastructure performance benchmarking beyond applications. It standardizes testing across servers, workloads, and cloud SKUs, helping teams validate changes, identify regressions, and optimize resources. Future plans include AI integration, anomaly detection, and continuous validation.