Oct 02, 2026

The 5 Levels of Self-Driving Production — Eric Schwartz, Traversal

Eric Schwartz, a product manager at Traversal, argues that AI-generated code has moved engineering time from building to troubleshooting, and that observability dashboards show what broke but not why. He frames root cause analysis as a causal problem, lays out five levels of self-driving production, walks through alert intelligence at PepsiCo and incident response at American Express, and offers five questions for evaluating an AI SRE.

Jul 21, 2026

Causal Models Need Causal Data - Xaira’s X-Cell model (Bo Wang & Ci Chu)

Bo Wang and Ci Chu of Xaira Therapeutics introduce X-Cell, a 4.9-billion-parameter diffusion language model designed to predict cellular responses to genetic perturbations. Trained on PISCES, the largest genome-wide CRISPRi Perturb-seq dataset (25.6 million single cells across 16 contexts), X-Cell overcomes limitations of previous observational models. The discussion highlights the critical role of high-quality, causal data, X-Cell's architecture advantages, and its demonstrated ability to generalize to unseen cell types and primary cells, marking a significant step towards truly predictive virtual cell models in drug discovery.

Jul 16, 2026

Learning Genetic Perturbation Effects at Single-Cell Resolution for Virtual Cells

Jiaqi Zhang's talk focuses on advancing causal discovery and perturbational modeling for gene networks, crucial for therapeutic insights. She introduces theoretical guarantees for learning latent causal variables and presents MORPH, a VAE-based framework that predicts the effects of novel and combinatorial gene perturbations by integrating diverse prior biological knowledge and using distributional loss for training. The model has demonstrated success in guiding experimental design, including identifying novel T-cell regulators for cancer immunotherapy, highlighting its potential to accelerate biological discovery and drug development.

Jun 24, 2025

From DevOps ‘Heart Attacks’ to AI-Powered Diagnostics With Traversal’s AI Agents

Anish Agarwal and Raj Agrawal, co-founders of Traversal, discuss how their AI agents automate root cause analysis (RCA) for critical system failures. They detail their agent's architecture, which leverages causal inference and large-scale computation to systematically find the root cause in minutes, and argue that the rise of AI-generated code makes AI-powered debugging an essential capability for modern software engineering.