InfoQ Homepage Performance Evaluation Content on InfoQ
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Multi-Agent Patterns from Spotify’s AI Powered Advertising Platform
Pratik Rasam explains how Spotify Ads Manager builds multi-agent AI systems in production using Google ADK Java, detailing guardrails, domain ownership, and key architectural patterns for scale.
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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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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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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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Performance Mythbusting Panel
The panelists answer audience questions on real-world applied performance proofs across stacks including Java, .NET and Python.
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Performance and How to Measure It
Matt Warren takes a look at how to measure, what to measure and how get the best performance from .NET code, considering examples from the Roslyn codebase and StackOverflow (the product).
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Java vs. C Performance
Cliff Click takes a look at Java vs C performance. He discusses both languages' strong and weak points and the programming context surrounding language choices.
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Performance Appraisals - The Bane of Agile Teams
Mike Mallete discusses the why performance appraisals fail and what can be done instead.
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Performance from the User‘s Perspective
Alois Reitbauer explains why the server response time and synthetic transactions are not good enough performance indicators, presenting a way of measuring page performance as perceived by the user.
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Anomaly Detection, Fault Tolerance and Anticipation Patterns
John Allspaw discusses fault tolerance, anomaly detection and anticipation patterns helpful to create highly available and resilient systems.
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Performance Testing at the Edge
Alois Reitbauer shows how to do performance testing during development, testing, and production by starting early in the development phase, breaking the test into pieces, and testing continuously.
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DevOps Requires Visibility: Monitoring, Testing and Performance
Examining the role of monitoring and testing techniques in solving DevOps problems.
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