# qcon ai newyork 2026 pc picks

Published articles for qcon ai newyork 2026 pc picks.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## QCon AI New York 2026 Program Committee Picks: Agent Identity, Context Costs, and Open-Source Infere

DevFeed: [QCon AI New York 2026 Program Committee Picks: Agent Identity, Context Costs, and Open-Source Infere](<https://devfeed.tech/articles/qcon-ai-new-york-2026-program-committee-picks-agent-identity-context-costs-and-open-source-infere-106251.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/10/qcon-ai-newyork-2026-pc-picks/>)

Author: Artenisa Chatziou

Published: 2026-10-29T11:00:00Z

Content type: news

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [AI Context Management](<https://devfeed.tech/topics/ai-context-management.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [actions](<https://devfeed.tech/tags/actions.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agent-identity](<https://devfeed.tech/tags/agent-identity.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [api](<https://devfeed.tech/tags/api.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [audio](<https://devfeed.tech/tags/audio.md>), [authorization](<https://devfeed.tech/tags/authorization.md>), [context](<https://devfeed.tech/tags/context.md>), [costs](<https://devfeed.tech/tags/costs.md>), [identity](<https://devfeed.tech/tags/identity.md>), [inference](<https://devfeed.tech/tags/inference.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [new-york](<https://devfeed.tech/tags/new-york.md>), [news](<https://devfeed.tech/tags/news.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [qcon-ai-new-york-2026](<https://devfeed.tech/tags/qcon-ai-new-york-2026.md>), [qcon-ai-newyork-2026-pc-picks](<https://devfeed.tech/tags/qcon-ai-newyork-2026-pc-picks.md>), [qcon-software-development-conference](<https://devfeed.tech/tags/qcon-software-development-conference.md>), [questions](<https://devfeed.tech/tags/questions.md>), [standards](<https://devfeed.tech/tags/standards.md>), [teams](<https://devfeed.tech/tags/teams.md>)

### AI overview

The QCon AI New York 2026 committee's six picks focus on production challenges in agentic systems and AI engineering. Sessions cover delegated authority, credential handling, and agent security; LLM context compression, caching, and retrieval, including failure modes in tool use; how coding agents may reshape engineering teams and junior roles; and open-source inference with Agentic API, vLLM, and llm-d, where cost per completed task is emphasized over token price alone. The remaining picks examine agent standards including MCP, A2A, and OAuth, distinguishing shipped capabilities from open work, and audio AI trade-offs such as end-to-end versus cascaded architectures and latency versus quality. The source presents these as conference session topics and committee selections, not as settled conclusions.

### Source excerpt

Who authorizes an agent's actions, and how should teams manage growing context costs? The QCon AI New York 2026 program committee shares six session picks that tackle these questions, alongside open-source inference, agent standards, and audio AI. By Artenisa Chatziou