Most access strategies weren’t designed for non-human identities. Traditional PAM tracks admin sessions and VPNs grant broad network entry. Zero trust frameworks assume you can verify identity upfront, which gets complicated fast when your principal is a CI/CD job, a service account, or an AI agent making tool calls across your infrastructure.

In this session, Tailscale’s Solutions Engineer Kartik Bharath and Product Manager XingLu Wang worked through what a unified access architecture actually covers today: human developers, contractors, pipelines, and AI agents, under one consistent policy model, without adding IT overhead or slowing down engineering teams. They illustrated Tailscale’s stack, including Border0+Tailscale for privileged access and Aperture for AI agent controls, and walked through real architectural decisions and the tradeoffs involved.

SecOps teams are caught in a never-ending alert-fatigue cycle. More detection rules generate more alerts, more alerts overwhelm analysts, and overwhelmed analysts miss the threats that matter. Meanwhile, the pressure to expand data collection keeps growing; from compliance requirements, from AI tools that need telemetry to function, and from leadership that equates coverage with visibility.

Breaking that cycle means rethinking three things at once: how you model detection, how AI fits into the actual workflow, and how you manage the data underneath it all.

In this session, Chas Clawson, VP of Security Strategy at Sumo Logic, shared a practical framework for SecOps teams working to get control of their signal-to-noise problem with specific guidance on where AI makes the biggest operational difference.

Distributed systems observability is a solved problem until AI agents enter the picture. They degrade, hallucinate, over-consume, and make tool calls in sequences you didn’t anticipate, none of which maps neatly to the log-metric-trace model your stack was built around. What most teams are missing isn’t more tooling; it’s a unified architecture that treats AI agent observability as a first-class problem, and OpenSearch combined with OpenTelemetry is increasingly where that architecture lives. In this session, Dotan Horovits (AWS & OpenSearch Ambassador) and Rekha Thottan (AWS) walks through full-stack observability across both layers: a live troubleshooting simulation using correlated logs, metrics, and traces, followed by how agent traces flow through the same OTel pipelines as your standard services and how Agent Health, an open-source evaluation framework, gives you a structured pre-production benchmark before rogue agent behavior becomes a production incident.

Most platform teams had a working theory about Kubernetes rightsizing before AI workloads arrived and raised the cost of deferring: recommendations are useful, humans should review them, and automation can wait until teams are more confident. That position becomes harder to defend when workloads include inference endpoints running at peak load, bursty ML training jobs, and GPU-adjacent containers that cost real money every hour they’re overprovisioned. While 89% of engineering teams consider rightsizing automation mission-critical, 71% still require human review for every change, and only 17% have reached continuous automated optimization. The gap isn’t technical, it’s a trust problem. Join CloudBolt’s Yasmin Rajabi and Reid Vandewiele as they share the mechanics that let teams move from advisory recommendations to conditional autonomy to continuous optimization for Kubernetes and AI workloads, without the all-or-nothing leap that keeps many teams stuck waiting longer.

Vector embeddings transformed how we build search and retrieval systems, and if you’ve shipped production applications on top of them, you already know what they can do—and may also be starting to discover what they can’t. Vectors are powerful, but they represent a single point in space, while complex search problems involving multiple signals, multimodal data, or nuanced relevance ranking require something more expressive. Tensors extend what’s possible, enabling richer representations, more sophisticated scoring, and retrieval that can reason across dimensions that vector search simply wasn’t built to handle. In this session, Vespa.ai’s Bonnie Chase, Director of Product Marketing, and Zohar Nissare-Houssen, Strategic Presales Lead Engineer, offer a practical primer on what tensors are, why they matter, and what they make possible in real-world applications, along with concrete use cases across retail, life sciences, and financial services.

AI agents are changing not just how software gets written, but who is responsible for it, how teams are structured, and what it means to own a system when an agent can modify thousands of components at once. Most organizations aren’t ready. The ones that are built rigorous platform foundations. Spotify is one of the few operating at that level. In this online event, TNS host Jennifer Riggins sat down with Spotify’s Stefan Särne and Sanjana Seetharam to explore what agentic-first development looks like at scale, what changed, what broke, and which platform principles made it work.