InfoQ Homepage QCon AI Boston 2026 Content on InfoQ
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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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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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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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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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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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You Can't Trust Your AI Bill. And Neither Can I
Erik Peterson shares why AI costs are eclipsing cloud bills, explaining how token spend, unit economics, and AI autonomy impact software architects and engineering leaders.
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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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Building GenAI Platform at DoorDash
Swaroop Chitlur and Sidd Kodwani explain how DoorDash built and scaled an enterprise GenAI platform, revealing key architectural bets, pivots, and operational principles driving real business impact.
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From Consumers to Builders: Turning 200 of our Team into Agent Creators in Two Weeks
Ben Maraney explains how Forter enabled R&D teams to build internal AI agents fast by simplifying tools, using custom MCP servers, and removing organizational roadblocks to maximize engineering ROI.
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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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The Agent Harness: Control Planes, Invariants, and Approval Boundaries for Production AI Agents
OpenAI’s Vinoth Govindarajan explains how to build reliable AI agent harnesses in production, focusing on state ownership, mutation ordering, scoped authority, and user-visible proof of action.
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