~/conference

QCon AI Boston 2026

date:2026-06-01..2026-06-02
location:Boston, United States
series:QCon AI Boston · one edition

QCon AI Boston, July 8–9, 2027. Bring more production experience to your AI decisions with senior practitioners sharing trade-offs, results, and hindsight.

software architects, platform engineers, machine learning engineers, engineering managers, site reliability engineers, developer experience engineers, data engineers, security engineers

deterministic context engines; semantic tool routing; checkpointed agent orchestration; skill store and executable skills; verification gates for agentic code; organizational context ingestion; MCP server catalog management; PII schema extraction and masking; end-to-end agent audit trails; agent trust lifecycle practices; real-time personalization with bandits

titleauthorlength
From Prompt Hacking to Architectural Determinism: Engineering Reliable GenAI SystemsHugo Guerrero50m
Models fail when updates, context drift, and window compression change outputs, so the claim is to stop relying on prompts and instead make the LLM a router…
Talk on
Beyond Sandboxes: Architecting Durable Runtimes for AI AgentsViren Baraiya26m
Argues production AI agents need an orchestration layer that persists state, checkpoints every step, and recovers from crashes — demonstrated by killing a run…
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Solving Context Bloat: Semantic Tool Routing in Multi-Server MCP EnvironmentsHugo Guerrero49m
Concrete setup: a TypeScript Volcano agent behind a Kong AI gateway calls OpenAI (GPT‑4) and registers MCP servers via a catalog. Adding one MCP server jumps a…
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Making AI Reliable in the SDLC: The Role of Deterministic Context at ScaleJulien Godfroid, Max Kozinenko39m
Claims concrete outcomes: grounding LLM coding agents in CAST’s deterministic, graph-based context cuts token use about 50%, doubles result accuracy and…
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Leverage AI to Protect Your Data Assets... from AIDaniyar Mussakulov31m
Claim: flexible NoSQL data hides PII and AI-driven code changes speed leaks unless data is governed. Demonstrates schema extraction from live MongoDB/JSON…
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Building the Context Engine AI Agents NeedBrandon Waselnuk31m
Claim: agents fail from missing organizational context, not intelligence. A context engine built by Unblocked ingests Git, Jira/Linear, Notion/Confluence…
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Make Quality Executable: Verification Gates for Agentic Engineering at ScaleNnenna Ndukwe51m
Claims that embedding verification gates at planning, implementation, local pre PR, CI and PR stages is the scalable way to keep code quality while using…
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The Trust Lifecycle for Safe, Secure and Reliable AI AgentsAnuj Tambwekar38m
Vizu presents an Agent Trust Lifecycle: build agents with deterministic tools for IDs and math, add a grounding guardrail to verify outputs, and run multi-pass…
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From Demo to Production: Why Agentic AI Systems Fail - and How to Fix ThemVenugopal Jidigam39m
Claim: agentic failures are a systems problem, not a model one. A platform built for 10M+ users hits four concrete failure patterns: context overload from…
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Vibecoding your Own Multi-Agent WorkstationRobert Brennan46m
Argues you should fork an open-source harness (Open Hands, OpenClaw, Hermes), deploy it in cloud VMs or isolated containers, and start with low-risk review…
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Autonomous Marketing at Klaviyo: Architecting Composer and 1:1 Personalization at ScaleLiang Zhang10m
Klaviyo runs on a real-time data platform that ingests 2.5 billion daily events from 350 integrations and 7.3 billion customer profiles to power autonomous…
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The MCP Megalith: Surviving the 1,000-Tool Explosion and Auth FragmentationNeil Mansilla51m
MCP adoption jumps from ~100 to 30,000 MCP servers in months, causing context‑window bloat, quarterly spec reversals, and OAuth fragmentation that breaks…
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Klaviyo Customer Agent: Building Bi-Directional Conversational Commerce at ScaleElliot Penson54m
Klaviyo builds a real-time, bi-directional commerce agent that serves millions of queries across web, SMS and email by combining multi-tenant RAG pipelines…
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