InfoQ Homepage Platform Engineering Content on InfoQ
-
Expedia Uses AI Driven Service Telemetry Analyzer to Accelerate Incident Investigation
Expedia Group has introduced STAR, an internal AI-assisted observability platform that helps engineers investigate production incidents using service telemetry and LLMs. Built with FastAPI, Datadog, Celery, Redis, and Langfuse, STAR follows structured workflows to analyze telemetry, generate root cause assessments, and support incident response while keeping engineers in the loop.
-
Achieving Compliance as a Platform Engineering Team by Helping Developers
When a new platform team set out on implementing their roadmap through forced workflows with poor documentation, developer experience declined. Success came from simplifying governance, prioritizing what matters, and rolling out compliance incrementally through prevention, detection, and communication. Empathy, focus, and shared purpose drove successful adoption.
-
QCon AI New York 2026: Registration Opens for December 15-16 Production-AI Conference
QCon AI New York 2026 (Dec 15-16) has opened registration at The Westin Jersey City Newport. Six tracks on production AI, chaired by Eder Ignatowicz with Faye Zhang and Wes Reisz. First sessions announced in August, full program by November.
-
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.
-
Google and Industry Partners Announce Agentic Resource Discovery Specification for AI Agents
Google and industry partners announced Agentic Resource Discovery (ARD) Specification, an open standard for publishing, discovering, and verifying AI tools, APIs, and agents. ARD introduces a discovery layer built on catalogs and registries, enabling dynamic capability discovery while leveraging existing protocols such as MCP and OpenAPI for execution and emphasizing trust and interoperability.
-
How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone
DoorDash details the architecture behind Ask DoorDash, its AI-powered conversational shopping assistant, combining LLMs, specialized AI agents, MCP-based tooling, and an intelligence layer with persistent consumer memory and live backend data. Early results show up to 24% higher checkout conversion, 17% larger baskets, and improved intent accuracy using memory-backed sessions.
-
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.
-
AWS Details How One Customer Scaled to One Million Lambda Functions
AWS has outlined how ProGlove, an industrial-wearables manufacturer, was able to scale its SaaS platform to run more than one million AWS Lambda functions spread across thousands of dedicated customer accounts.
-
Cycle Introduces EU Control Plane as Sovereignty Debate Continues
Cycle recently introduced a separate EU-based control plane, allowing European customers to keep platform management data and telemetry within Europe. The new offering is designed to improve compliance, operational isolation, and responsiveness for European organizations.
-
Cloudflare Details Unified Data Platform Where Billing Workloads Account for 53% of Queries
Cloudflare details Town Lake, an internal unified data platform, and Skipper, an AI analytics agent unifying access to operational, billing, security, and business data. The platform processed ~91K billing queries, with billing forming majority usage. Built on a lakehouse architecture using Trino, Iceberg, R2, and DataHub, it enables governed cross-system analytics and natural language access.
-
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.
-
Behind the Scenes: Block 450 JVM Repositories into Monorepo to Reduce Dependency Drift
Block, Inc. describes migrating ~450 JVM repositories into a monorepo across Cash App and Square engineering to reduce dependency drift and coordination overhead. The system supports ~8,800 weekly builds with ~10 min p90 CI time. The approach improves cross-service changes, build visibility, and developer experience through dependency graph–based builds, selective CI, and custom IDE tooling.
-
Lyft Uses Mapping Intelligence to Reduce Friction in Gated Community Pickups
Lyft details a new pickup experience to improve reliability in gated communities, where 25–30% of rides face routing and access challenges. The system uses mapping signals, boundary detection, and routing improvements to reduce cancellations and coordination overhead between riders and drivers, highlighting how real-world constraints drive evolution in geospatial systems.
-
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.
-
Pinterest Uses Content Fingerprints for URL Deduplication across Millions of Domains
Pinterest introduced MIQPS, a URL normalization system that identifies which query parameters affect page identity using rendered content fingerprints. It reduces duplicate processing across millions of domains by replacing rule-based approaches with offline analysis, anomaly detection, and runtime parameter maps, improving ingestion efficiency and scalability in large-scale content pipelines.