2026 Schedule
Schedule subject to change.
Beginner
For those learning about a topic for the first time or looking to gain foundational knowledge before starting a project. For these presentations, you will walk away with a conceptual understanding, knowledge of basic tools, and/or the first steps required to try it out.
Intermediate
Sessions for those actively working with the technology daily and who want to improve their efficiency, testing, and/or application of best practices. In these sessions, you may learn concrete code examples, testing strategies, common pitfalls, and/or methods to solve medium-complexity problems.
Advanced
For the attendees who are actively designing or leading projects with a particular technology and want to understand complex trade-offs, scalability, and integration challenges. These sessions may allow you to gain unique, production-tested insights, architectural patterns, performance optimization tips, and/or analysis of design trade-offs.
Monday, November 16th, 2026
Badge Pickup - Market Street Foyer & Continental Breakfast - Pacific Level
Break
Track:
Architectures You've Always Wondered About
Live Resharding Without Regret: Lessons from Building Valkey's Atomic Slot Migration
Sharding is easy. Resharding under heavy load is notoriously difficult. How do you move gigabytes of state across live database nodes without dropping keys, blocking the main event loop, or breaking client abstractions?
Track:
Guardrails & Safety Nets (Evals) for the New Landscape
Token Efficiency at Uber
Details coming soon.
Ballroom A
Track:
Modern API Design for Humans and Agents
APIs That Can't Afford to Be Wrong: Lessons from Moving Money
Most API design advice assumes a forgiving environment: retries are cheap, data has a single source of truth, and a slow response is only an inconvenience. Financial infrastructure assumes none of that. Card networks give you three seconds to auth or decline.
Track:
Resilience Engineering: Everything Fails All the Time
The Freeze Paradox: Why Stopping Deployments Doesn't Stop Failures
When systems fail at Netflix and in an environment this complex, they sometimes will, the instinctive response is to freeze deployments.
Track:
Sponsored Solution Track I
Legacy vs. AI-Native Is the Wrong Question: Architecting the Modern Platform
Engineering leaders modernizing operational platforms are often presented with a false binary: choose the control and reliability of established enterprise platforms or embrace AI-native tools optimized for speed, automation, and intelligent experiences.
Break
Track:
Architectures You've Always Wondered About
Architecting Nubank's Global Financial Infrastructure
Instant payment frameworks are transforming global finance, but few institutions have faced the infrastructure scale required by Brazil's Pix network.
Ballroom BC
Track:
Guardrails & Safety Nets (Evals) for the New Landscape
How Airbnb Guardrailed Its AI Customer Support Agent
When Airbnb launched its AI customer support assistant, the feature made the news — the guardrails built to make it safe to ship did not. Yet an AI agent that talks to millions of customers, remembers a conversation across turns, and can trigger account actions is a
Track:
Modern API Design for Humans and Agents
Everything I Knew About Developer Experience Is Wrong for Agents
Most of what we know about developer experience we learned from humans. Agents are a different user, and some of our best practices quietly work against them.
Track:
Resilience Engineering: Everything Fails All the Time
Adapt or Drift: Resilience Engineering When AI Moves the Operating Point
Resilient systems do not stay resilient by standing still. They survive by adapting. But adaptation comes with risk: under sustained pressure, organizations and systems can slowly drift toward failure while still appearing to operate normally.
Lunch & Lunch topic discussions on Atrium level.
Women & Allies in Tech Lunch Session in Waterfront AB, Atrium level (Sign-up coming soon).
Track:
Architectures You've Always Wondered About
How to Build a Real-Time Voice Agent
A voice agent looks like a chatbot with a microphone.
Track:
Guardrails & Safety Nets (Evals) for the New Landscape
LLM Flip-Flops: From Reliability Risk to a Hill-Climbing Signal
Run the same task through the same LLM twice, and you may get two materially different answers. This behavior is often dismissed as the stochastic nature of LLMs, but it creates a serious product risk.
Track:
Modern API Design for Humans and Agents
The Schema Was Never Enough: The World in Protocols
Every reliable process in the world is a protocol: auctions, double-entry bookkeeping, brokerage account transfers, or APIs. Protocols reduce ambiguity but the world remains messy.
Track:
Resilience Engineering: Everything Fails All the Time
Unconference: Resilience Engineering
Seacliff D
Track:
Sponsored Solution Track I
Your Agents Lack Context: Here's How to Fix 'You're absolutely right!"'
Every AI coding tool can generate code. Very few can generate the right code for your organization, because they’re missing context.
Break
Track:
Architectures You've Always Wondered About
Inside the Disaggregated Architecture Serving LLM Inference
Every LLM request is really two very different jobs. Reading your prompt (prefill) is compute-intensive, while generating the response token by token (decode) is dominated by memory bandwidth. For years, the industry ran both phases on the same hardware and accepted the compromise.
Track:
Guardrails & Safety Nets (Evals) for the New Landscape
Unconference: Guardrails & Safety Nets
Seacliff D
Track:
Modern API Design for Humans and Agents
No New Backend: The API Layer Behind 800+ Agent-Built Apps at Duolingo
At Duolingo, anyone (engineer or not) can describe an internal tool and get a working app back, deployed against real company systems. That's BuildingHub, and 350 Duolingo employees have created at least one app with it.
Track:
Resilience Engineering: Everything Fails All the Time
Saturation: How Your Software Will Fail at Scale
Even in the ethereal world of software, everything has a limit. And, once that limit is reached, very bad things can happen. In this talk, we will explore the problem of saturation: when a software system runs into one of these limits.
Track:
Sponsored Solution Track I
Wait, You're Still Doing PRs?
PRs were built for open source. A non-maintainer wanted to contribute, a maintainer needed a gate, and you opened the PR early so the two of you could work through the change together while it was still forming. That was the point. The PR was where the thinking happened.
Track:
Sponsored Solution Track II
The Context Gap: Why Correct Agents Fail
Two agents edit different files. Git merges clean. The program is broken. No conflict was thrown, because the collision was in meaning, not in text — and business decisions are almost all semantic.
Coffee Break & Snacks - Pacific Level
Track:
Architectures You've Always Wondered About
Architecting Real-Time Timeline Engines for 100 Million Active Users
Details coming soon.
Ballroom BC
Track:
Guardrails & Safety Nets (Evals) for the New Landscape
Practical Concerns Running Agents in Production
Details coming soon.
Track:
Resilience Engineering: Everything Fails All the Time
Engineering Boundaries for Outages You Can't Prevent
Details coming soon.
Break
Track:
Architectures You've Always Wondered About
Unconference: Architectures You've Always Wondered About
Seacliff D
Track:
Guardrails & Safety Nets (Evals) for the New Landscape
Building Secure Runtime Sandboxes for Code-Generating Agents
Details coming soon.
Ballroom A
Track:
Modern API Design for Humans and Agents
Stop Fighting the Remodel: API Design for Models That Outgrew Their Boundaries
Every domain model has a breaking point. At Netflix, ours arrived all at once—Ads, Games, Live events, and entirely new kinds of content. Each pulled the core entities in different, sometimes contradictory directions.
Track:
Resilience Engineering: Everything Fails All the Time
It's Never the Thing That Broke: Learning From Incidents at (AI) Speed
When something breaks, our first instinct is to focus on the thing that failed. But in complex systems, the failure is usually the result of many things going right, changing, and interacting in ways we didn't anticipate.
Conference Social - Dry Snacks & Drinks - Ballroom Foyer & Pacific Level - Ends at 7pm
Tuesday, November 17th, 2026
Continental Breakfast - Pacific Level
Break
Track:
Distributed Systems in Production
Orderly Keys, Wild Values: Adaptive Compression for Distributed Key-Value Storage
At Netflix scale - billions of requests per day and petabytes of key-value data - even small inefficiencies in storage and network paths become expensive.
Track:
Engineering AI Systems
Skills, Memory, or Fine-Tuning? The Engineering Loop Behind Self-Improving Agents
As agents become mainstream, everyone wants to improve theirs either by making fewer mistakes on existing tasks or by taking on harder ones. This usually happens once an agent is already deployed in production.
Track:
Staff+ Engineering Skills
Nobody Knows Everything (and That's OK): Building Alignment in Complex, Rapidly Changing Environments
Successful Staff+ engineers build alignment across organizational and technical boundaries, including domains where they aren't the deepest expert.
Track:
Data Platforms Reimagined
Breaking Down the Walls to Python & Big Data + AI Performance with Transpilation and LLMs
Many of us have a love hate relationship with Apache Spark, especially when it comes to PySpark. Especially in Python, the UDF performance can be a dumpster fire.
Break
Track:
Distributed Systems in Production
How to Build Online Systems with Object Storage
“Diskless” systems that delegate durability to object storage are everywhere, and for three good reasons:
Track:
Engineering AI Systems
Inside Asimov: BlackRock’s Platform Architecture for Applied AI at Scale
BlackRock’s Portfolio Management Group is transforming how investment teams conduct research by combining deep investment expertise with shared AI capabilities.
Track:
Staff+ Engineering Skills
Use AI to Multiply Your Team as a Staff Engineer
Most "AI for engineers" content is about typing faster. That's IC-level work. Staff engineers have a different question to answer. When AI raises the floor on what every engineer can ship, what is the staff engineer's job, and where does the leverage actually come from now?
Track:
Data Platforms Reimagined
Building a Data Platform with AI
Data platform sits at the intersection of infrastructure, governance, operations, and user experience.
Lunch & Lunch Topic Discussions on Atrium Level
Track:
Engineering AI Systems
The Revenge of the Data Scientist: Why Reliable AI Needs Evals, Traces, and Metrics
Most teams can now ship an AI prototype by calling a foundation-model API. The hard part is knowing whether that system works when real users, messy data, and business consequences arrive.
Track:
Staff+ Engineering Skills
Transitioning From a Startup to a Mature Platform
What you need to succeed in a startup with zero customers is different from what you need when you have a few thousand, and this changes many times as the company grows. Some of these changes are code and architectural changes, some are organizational changes, and some are mindset shifts.
Track:
Data Platforms Reimagined
One Does Not Simply Add Synchronous Writes - Bridging Batch and Real Time with Apache Iceberg
What happens when a batch ingestion platform needs to behave like an operational database? Datadog’s Reference Tables product was designed for asynchronous uploads of large datasets, but customers increasingly needed synchronous row-level edits, without sacrificing batch scale.
Break
Track:
Distributed Systems in Production
Adaptive Systems in Production: What Recommendation Systems Can Teach Us About Agents
As organizations race to build AI agents, many teams are encountering challenges that feel new: evaluation uncertainty, feedback loops, behavioral drift, exploration versus exploitation, and maintaining user trust in systems that continuously adapt.But these challenges are not new.
Track:
Engineering AI Systems
Lessons from Building a $100M Product in Six Weeks at OpenAI
Engineering is shifting from synchronous, line-by-line implementation to an asynchronous model in which agents execute, verify, and retry while humans own intent, architecture, constraints, and release decisions.
Track:
Data Platforms Reimagined
Controlling Costs in the Era of Infinite Compute
Details coming soon.
Seacliff ABC
Track:
Sponsored Solution Track III
Trust, but Verify: Delivering Quality with Agentic Workflows
In spite of all the recent panic, nothing has really changed about software development. We’re worried about agents being unreliable. Newsflash: human devs are too. We’re worried about agents producing more code than we can read, but did you ever really do all your reviews within 24 hours?
Coffee Break & Snacks - Pacific Level
Track:
Distributed Systems in Production
When Your Users Are Agents: Lessons from a Distributed Postgres Platform
Distributed systems are built around assumptions about workload behavior: connections have reasonable lifetimes, retries eventually stop, traffic spikes have recognizable causes, and application code produces somewhat predictable query patterns.
Track:
Engineering AI Systems
Progressive Failure Modes of Modern AI Serving Systems
Details coming soon.
Ballroom A
Track:
Staff+ Engineering Skills
Learning What’s Already There: A Breadth-Based Approach To Onboarding
When onboarding to a new organization, it's vital to bootstrap your network and context quickly.
Track:
Sponsored Solution Track III
Your Agents Have Amnesia: Lessons from Building a Memory Layer for Multi-Agent Systems
Multi-agent systems have a data problem nobody warns you about. Agent B sees Agent A's final output and nothing else; the reasoning, the dead ends, and whatever Agent A figured out three steps in all get thrown away.
Break
Track:
Distributed Systems in Production
Autoscaling 800 Valkey Clusters: Distributed Systems Lessons from Scaling Stateful Fleets
Autoscaling stateless services is a solved problem. In a sharded datastore, a resize is not a capacity change — it's moving ownership of key ranges between nodes while the cluster serves live traffic, and rebalancing takes long enough that reacting to a traffic peak is already too late.
Track:
Staff+ Engineering Skills
Scaling Tech by Aligning Humans
Scaling a complex technology can get messy without a clear north star or a shared technical strategy. Product strategies from different functions can each deliver impact but may lead to disconnected solutions or inadvertently introduce various forms of technical debt.
Track:
Data Platforms Reimagined
Handling High-Velocity Ingestion Outside the Cloud
Details coming soon.
Seacliff ABC
Reception - Dry Snacks & Drinks - Ballroom Foyer & Pacific Level - Ends at 7:00pm
Wednesday, November 18th, 2026
Continental Breakfast - Pacific Level
Break
Track:
Architecting for Agents: Beneath the Loop
Platform Engineering for Agents
Agentic tools amplify what’s already in the codebase. The agent works through your code, your architecture decisions, and the documentation around them. It follows what it can read and guesses at the rest.
Track:
Real World Platform Engineering
Why Most Platform Teams Fail: The Adoption Problem Nobody Wants to Own
We have all seen the moment: the platform goes live, the launch deck looks sharp, the portal is polished, the golden paths are documented, and yet teams quietly continue doing things the old way. Not always because the platform is bad, but because adoption was assumed, not owned.
Break
Track:
Architecting for Agents: Beneath the Loop
Making Production Legible to Agents: Lessons From Building an Observability MCP
An agent that can query production telemetry can test its own hypotheses against reality, rather than hallucinate fictions. Observability tooling allows our agents to close the loop and benefit from production wisdom.
Track:
Engineering the Developer Experience
No More Small Systems, How AI May Bring Large System Problems to Everyone
Once you figure out how to use AI to make your organization productive, you will soon find you have a whole new set of problems. While some of those problems will be novel thanks to LLMs, many of them are just the result of there being "more stuff".
Track:
Real World Platform Engineering
Beyond the Kubernetes API: Building LinkedIn’s Compute Platform
LinkedIn’s Kubernetes-based compute platform spans more than 500k bare-metal machines, 5M pods, with thousands of developers doing more than 110k deploys a week. We don’t expose raw Kubernetes to developers and instead, provide a curated set of platform offerings.
Track:
Code Beyond Engineers & Engineering Teams
Legacy as Leverage: How Brownfield Work Builds Technical Judgment
Legacy systems are often treated as a necessary evil: work to be endured before moving on to more “real, strategic” projects.
Track:
Sponsored Solution Track IV
The Men in Black Know What's in Your JavaScript and How to Fix it!
Ever wonder what’s lurking in the dark corners of your node_modules, like unidentified flying dependencies sneaking aboard your project? In an age of escalating software supply chain attacks, knowing exactly what you're shipping is as vital as tracking strange lights in the sky.
Lunch & Lunch Topic Discussions on Atrium Level
Track:
Architecting for Agents: Beneath the Loop
Efficient GPU Agents: Infra for Autonomous LLM Perf Optimization
Parasail serves open-weight LLMs on distributed clusters of GPUs managed by Kubernetes. The typical performance optimization loop is to reproduce a problem or measure a regression, iterate on code or config, set up and run benchmarks, and decide whether the change is worth shipping.
Track:
Engineering the Developer Experience
Navigating AI Panic in Software Engineering
Details coming soon.
Ballroom A
Track:
Real World Platform Engineering
Decoupling Local Development from Remote Infrastructure
Details coming soon.
Pacific DEKJ
Track:
Code Beyond Engineers & Engineering Teams
The Death of the Code Review
Code review was built for a world where humans wrote all the code.
Break
Track:
Architecting for Agents: Beneath the Loop
Dreams on Paper: How Agents Learn from Experience
Agents produce high-fidelity traces of everything they do. But traces alone don't produce memory. This talk introduces three primitives for turning session history into something actionable:
Track:
Engineering the Developer Experience
Understanding as a Deliverable: How AI Changes What Healthy Software Looks Like
AI is changing software development far faster than our engineering practices are evolving, and many teams are struggling to adapt their processes and tools to keep up.
Track:
Real World Platform Engineering
Platform Engineering’s Second Act: From Vending Machine to Passport Control
Three years ago on the QCon SF stage, I made the case for “Acceleration, Autonomy, and Accountability” as the pillars of a successful platform. Those pillars haven't moved. AI has just rewritten what each one requires, and the platform team's job along with it.
Track:
Code Beyond Engineers & Engineering Teams
You Can't Read the Code Anymore - Verifying Software in the Age of AI
AI writes most of the code now, so the hard part of engineering has moved from authoring to the part we’re worse at - verifying. With the incentives all pushing toward shipping faster and faster - the rational move is to skim the diff, click through a little manual QA, and ship on vibes.
Coffee Break & Snacks - Pacific Level
Track:
Architecting for Agents: Beneath the Loop
Avoiding Prompt Debt: Building Resiliant AI Apps that Grow With the Ecosystem
Details coming soon.
Track:
Engineering the Developer Experience
Building a Culture of Engineering Excellence in the AI Era
Details coming soon.
Ballroom A
Track:
Real World Platform Engineering
Building a Migration Platform: Moving 100+ Netflix RDBMS Workloads to Aurora PostgreSQL
In late 2024, Netflix made a bet: consolidate the vast majority of our relational database use cases onto a single engine: Amazon Aurora PostgreSQL.
Track:
Code Beyond Engineers & Engineering Teams
When Everyone Can Code, Who Owns Production?
AI is making it dramatically easier to create and change software, but writing code is only one part of the software lifecycle. Every change still needs to be deployed, observed, operated, diagnosed, and kept reliable in production.
QCon Closing Reception - Dry Snacks & Drinks - Ballroom Foyer
Thursday, November 19th, 2026
Continental Breakfast - Seacliff Foyer
Certification
09:00AM PST - 12:00PM PST (3 hours)
The InfoQ Certified Architect Program at QCon San Francisco
Garden A
Training
HALF DAY09:00AM PST - 12:00PM PST (3 hours)
Building AI Agents: A Practical Framework for Choosing the Right SDK
Seacliff B
Training
HALF DAY09:00AM PST - 12:00PM PST (3 hours)
The Human Architecture Problem: Designing Teams That Scale With AI [Half-day version]
Charlotte de Jong Schouwenburg - Bravely
Seacliff D
Training
HALF DAY09:00AM PST - 12:00PM PST (3 hours)
AI for Java Developers
Dan Vega - Broadcom
Nate Schutta - Thoughtworks
Marina
Training
HALF DAY09:00AM PST - 12:00PM PST (3 hours)
Measuring What Matters: Building a Developer Productivity Program Engineers Can Trust
Erin Doyle - Quotient
Seacliff A
Training
HALF DAY09:00AM PST - 12:00PM PST (3 hours)
Supercharge Your Work With Claude Code and Codex
Khawaja Shams - Momento
Seacliff C
Lunch - Atrium Level
Training
HALF DAY01:00PM PST - 04:00PM PST (3 hours)
How To Write Skills That Actually Work
Seacliff C
Training
HALF DAY01:00PM PST - 04:00PM PST (3 hours)
Psychological Safety as Infrastructure: Building Teams That Fail Fast With AI
Charlotte de Jong Schouwenburg - Bravely
Seacliff D
Training
HALF DAY01:00PM PST - 04:00PM PST (3 hours)
AI-Assisted Coding Master Class: Build Your Mission Control
Sepehr Khosravi - Coinbase
Seacliff B
Training
HALF DAY01:00PM PST - 04:00PM PST (3 hours)
The Fundamentals of Software Engineering in the Age of AI
Nate Schutta - Thoughtworks
Dan Vega - Broadcom
Marina
Training
HALF DAY01:00PM PST - 04:00PM PST (3 hours)
Cracking the Black Box: Forensic Debugging
Ryan Scott Brown - Crunchyroll
Seacliff A
Friday, November 20th, 2026
Continental Breakfast - Seacliff Foyer
Training
FULL DAY09:00AM PST - 04:00PM PST (7 hours)
The Incident Lab: Responding to and Learning From Production Failures
Sarah Butt - Salesforce
Eric Dobbs - Oracle
Hamed Silatani - Uptime Labs
Alex Elman - Slack
Seacliff A
Training
FULL DAY09:00AM PST - 04:00PM PST (7 hours)
The Human Architecture Problem: Designing Teams That Scale With AI [Full-Day Version]
Charlotte de Jong Schouwenburg - Bravely
Seacliff B
Training
FULL DAY09:00AM PST - 04:00PM PST (7 hours)
Effective AI-Native Development with Harness Engineering
Zichuan Xiong - Thoughtworks
Premanand Chandrasekaran - Thoughtworks
Seacliff C
Training
FULL DAY09:00AM PST - 04:00PM PST (7 hours)
Agent MCP Workshop: Building Your Own Java-Based MCP Server
David Parry - Qodo
Seacliff D
Lunch - Atrium Level