InfoQ Homepage Presentations
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Beyond the Harness: Canvas for AI Agents
Nimisha Asthagiri explains why shifting from rigid harness engineering to a bounded canvas model empowers autonomous AI agents while managing architecture drift and cognitive debt.
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The Ladder is Missing Rungs: Engineering Progression When AI Ate the Middle
Alasdair Allan shares data on the "missing rungs" of software engineering, explaining how AI coding tools stunt junior developer skill growth and disrupt future technical leadership pipelines.
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Wrangling Telemetry at Scale: a Guide to Self-Hosted Observability
Colin Douch explains the trade-offs of self-hosted observability, detailing how to connect metrics, logs, and traces while managing complexity and scaling costs across microservice architectures.
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Serving LLMs at Scale: the Hidden KV Cache Advantage
Khawaja Shams explains how distributed systems engineering solves modern LLM inference bottlenecks, covering KV caching, memory cliffs, prompt prefilling, and disaggregated execution.
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Move Fast, Don’t Break Trust: Shipping Constantly with Humans and beyond
Suhail Patel explains how Monzo built a paved platform stack, opinionated tooling, and fast CI checks to help engineers and AI agents ship microservices safely and continuously at scale.
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Actors for Agents: Patterns for Production AI
Manju Rajashekhar discusses why the actor model provides the ideal runtime architecture for moving AI agents from simple demos to production-grade, long-running, fault-tolerant systems.
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AI is an Amplifier: Scale High Performance, Not Dysfunction
Nathen Harvey discusses key findings from the 2025 DORA research, explaining how AI adoption, internal platforms, and organizational guardrails drive real software engineering performance.
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Beyond the Prototype: Scaling Framework Agnostic AI Agent Infrastructure with Ray
The speakers explain how to run and autoscale LLM agents in production using Ray, detailing system architecture, control planes, multi-region deployments, and CI/CD pipelines with LLM evals.
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Exploding GPUs and the AI Infrastructure Challenge
Andrew Martin explains why AI security relies on Kubernetes security, highlighting new threat vectors like prompt injection, unconstrained agents, and the unique risks facing GPU infrastructure.
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What 25 Trillion Tokens Reveal about How Developers Actually Adopt AI Agents
Brendan O'Leary shares real-world data from 40T tokens to explain how trust, multi-model routing, and context construction drive actual developer AI adoption over raw benchmarks.
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How to Find Resilience Bugs in Systems that Don't Exist
Hillel Wayne explains how formal methods principles - like properties, verification, and environments - help software engineers uncover subtle concurrency bugs and architectural flaws early.
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How Eve Online Leverages Head Based Sampling to Observe "Fun"
Nicholas Herring explains how EVE Online balances cost and fidelity using 100% head-based dynamic and deterministic sampling to monitor a 20-year-old monolith alongside modern domain services.
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