Architecture at QCon San Francisco 2026

Instant payment frameworks are transforming global finance, but few institutions have faced the infrastructure scale required by Brazil's Pix network.

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.

A voice agent looks like a chatbot with a microphone.

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.

As AI agents write more of the code Roblox engineers push, the hard part is no longer generating changes; it’s knowing which ones to trust.

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.

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.

AI agents have compressed business decisions from minutes to seconds. Data systems need to keep up.

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.

Apache Iceberg V3 adoption is growing and the community is already shaping what comes next. This talk walks through the emerging scope and priorities for the V4 table spec and progress of the proposals currently underway.

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.

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.

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".

As agents evolve, prompts get unruly. Edge cases are patched with one-line hotfixes, or with stacked instructions of escalating severity when an issue proves stubborn. Over time, those instructions pile up into a brittle artifact, overfit to an aging model.