Abstract
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
far larger attack surface than a chatbot. Content moderation alone does not cover it.
This talk is the behind-the-scenes story of that launch. We will walk through the layered defense we built to protect the agent: fast, deterministic input sanitization that strips obfuscation and encoding tricks before any model sees the text; fine-tuned classifiers for
jailbreak, prompt-injection, and harmful-content detection on both what the user sends and what the agent drafts; and enforcement that recognizes repeat offenders rather than judging each request in isolation. We will show why multi-turn agents need a twist that
single-shot systems do not — removing a blocked message from history so it cannot quietly steer later replies. The safety stack we built for this one agent later grew into the foundation of a centralized AI risk platform now shared across the organization.
Confidence to launch came from measurement. We will cover how we score guardrails before they ever enforce: LLM-as-judge labeling of production traffic, tiered golden and holdout datasets, and shadow runs - including a configuration that shadow testing
showed would have been over-blocked legitimate user messages. And because attackers do not wait for your next model release, we will share our rapid-response ladder — from regex blocklists to zero-shot prompt hotfixes to trained classifiers — that ships mitigations as configuration rather than waiting on a retraining cycle, backed by continuous monitoring and real-time alerting.
You will leave with a concrete blueprint for the reliability work behind an AI agent launch: how to structure layered guardrails, how to earn launch confidence through evaluation and false-positive control, how to respond fast when a new attack appears, and the principles that carry over to any AI agent launch.
$2,835, Conference (3 days). Current pricing ends September 8th. All pass options.
QCon San Francisco 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
From the same track
Monday 16 November
10:35 Ballroom A Session Building Secure Runtime Sandboxes for Code-Generating Agents Details coming soon. 11:45 Ballroom A Session 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… 13:35 Ballroom A Session Why AI Cost Guardrails Are the Next Major Reliability Crisis Details coming soon. 14:45 Seacliff D Unconference Unconference: Guardrails & Safety Nets 15:55 Ballroom A Session Practical Concerns Running Agents in Production Amanda Walker Sr. Director, Engineering - Privacy, Safety and Security @Google Details coming soon. 17:05 Ballroom A Session Automated Interventions When Agents Drift Off-Course Details coming soon.