InfoQ Homepage Performance Content on InfoQ
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Building Reusable Evaluation Frameworks for Agentic AI Products
Susan Chang shares how Elastic built a production-grade AI agent evaluation framework, detailing lessons on tracing, LLM-as-a-judge, programmatic evals to prevent regression across engineering teams.
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Beyond Observability: Evolving Production Operations in the Age of AI
The panelists share how AI and automation reshape production operations, turn system data into actionable insights, and change architectural practices for modern, complex software delivery.
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Context is the New Code
Patrick Debois explains how treating context like code transforms the Software Development Life Cycle into a Context Development Life Cycle to scale AI agent workflows reliably across teams.
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Adaptive Recommenders in the Real World: Inference, Evals, and System Design
Mallika Rao shares why building adaptive recommendation systems requires shifting focus from isolated ML models to real-time feedback loops, retrieval freshness, and production-level constraints.
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From AI Agent Demo to Production: Automated Testing and Evaluation
Zhou Yu explains why 95% of AI agents fail to reach production and shares how simulation-driven evaluation, synthetic users, and automated CI/CD testing unlock enterprise deployment.
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From S3 to GPU in One Copy: Rethinking Data Loading for ML Training
Onur Satici discusses Vortex, an open-source columnar file format designed to bypass CPU bottlenecks, enabling ultra-fast S3-to-GPU data streaming and dynamic query pruning at up to 60 Gbps.
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Beyond Prompting: Context Engineering for Production-Grade AI
Ricardo Ferreira explains how context engineering overcomes LLM latency, cost, and memory limitations in AI systems, sharing architectural lessons from building a custom Alexa backend with Redis.
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Can Claude Fix Itself? Using LLMs for Incident Response
Anthropic's Alex Palcuie shares how LLMs transform incident response, highlighting where Claude excels at log analysis and why automated AI SREs still can't replace human judgment.
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Continuous Delivery for Foundational Platforms
Ian Nowland explains why standard CI/CD models fail for foundational platforms, sharing lessons from AWS and Datadog on how engineering leaders can implement safe progressive deployments.
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Enchant Your AI and APIs with eBPF Magic 🪄
Dan Finneran explains how to use eBPF and AI gateways in Kubernetes to transparently observe, modify, and control unowned AI agent API calls without altering source code.
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From Fab To Token - The State Of The Market
Jordan Nanos explains how hardware constraints, data center scale, and chip co-design shape modern AI performance and tokenomics from silicon to inference.
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Adopting Memory-Safety and Fine-Grained Compartmentalisation with CHERI
David Chisnall explains how CHERI architecture unifies hardware capabilities and pointer metadata to deliver memory safety and fine-grained, efficient software compartmentalization.
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