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Every step you take, every call you make: the reliable agent stack — Giselle van Dongen, Restate thumbnail

· 20:50

Every step you take, every call you make: the reliable agent stack — Giselle van Dongen, Restate

This talk introduces Restate, an open-source framework designed to provide a flexible, durable foundation for building resilient, long-running, and stateful agentic systems. Restate addresses the infrastructure gap in agent development by handling complex concerns like retry logic, recovery, session isolation, and process cancellation. It operates as a server proxying requests to the agent service, using an event journal to ensure that processes can survive crashes, redeploys, and long periods of suspension (e.g., waiting for human approval) without losing state or consuming serverless execution time.

Key takeaways

  1. Durable Execution and Resilience 5:40

    Restate enables durable execution, allowing an agent process that runs for extended periods (e.g., a week) to crash and restart exactly at the point of failure, rather than starting over. This is achieved by recording all events in a journal.

  2. Modeling State with Virtual Objects 14:00

    Instead of modeling agents as simple workflows, Restate uses 'virtual objects' to model persistent, stateful entities (like a session). These objects have unique IDs and isolated state, allowing multiple agents to interact with a single run without interfering with each other's state.

  3. Advanced Control and Interaction 16:40

    The framework supports advanced control primitives, allowing external processes to signal, inject state into, or completely cancel an already running agent loop. This capability is crucial for complex, multi-agent interactions.

  4. Low Latency via Push Model 19:20

    Unlike traditional workflow orchestrators that poll for new tasks (pull model), Restate uses an event-driven, push model for invocations. This design significantly lowers latency, achieving low latencies (e.g., 45ms p99) even for multi-step workflows.

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The Sound of Your Secrets: Teaching Your Model to Spy, So You Can Learn to Defend - David vonThenen thumbnail

· 51:32

The Sound of Your Secrets: Teaching Your Model to Spy, So You Can Learn to Defend - David vonThenen

This talk details acoustic keystroke logging—a method of intercepting typed information purely from sound rather than physical interception. The speaker outlines how deep learning models can be trained using spectrographic images derived from recorded key presses to classify specific letters (e.g., 'S'). While demonstrating the high accuracy of single-keyboard attacks (100%), the presentation shows that multi-keyboard logging is challenging but feasible, especially when combined with context prediction and spell-checking algorithms. The session concludes by emphasizing defensive measures, including two-factor authentication using physical keys and implementing strong, unique, offline password policies.

Key takeaways

  1. Acoustic Keystroke Logging Mechanism 16:22

    The attack relies on machine learning audio classification. Audio files (linear 16 format) are converted into spectrographic images (frequency over time, visualized as a heatmap), which serve as the input for training models like Convolutional Neural Networks (CNNs).

  2. Multi-Keyboard Attack Complexity 28:12

    While single-keyboard classification can achieve 100% accuracy, using multiple keyboards significantly lowers confidence scores. The problem is made solvable by decomposing the text based on space delimiters and employing spell-checking/context prediction (e.g., predicting 'hello people' from partial sound inputs).

  3. Defensive Strategies 40:50

    Defense requires layered security: use physical two-factor authentication keys (like YubiKey) instead of SMS; utilize unique, complex passwords that are not known to the user; and be aware of potential signal interference or noise.

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