Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

As models improve, we are starting to build long-running, asynchronous agents such as deep research agents and browser agents that can execute multi-step workflows autonomously. These systems unlock new use cases, but they fail in ways that short-lived agents do not.

The longer an agent runs, the more early mistakes compound, and the more token usage grows through extended reasoning, retries, and tool calls. Patterns that work for request-response agents often break down, leading to unreliable behaviour and unpredictable costs.

This talk is aimed at use case developers, with secondary relevance for platform engineers. It covers the most common failure modes in async agents and practical design patterns for reducing error compounding and keeping token costs bounded in production.

Edge-based visual AI promises low latency, privacy, and real-time decision-making, but many projects struggle to move beyond successful demos. This talk explores what deploying visual AI at the edge really involves, shifting the focus from models to complete, operational systems. We will discuss common pitfalls teams encounter when moving from lab to field. Attendees will leave with a practical mental model for approaching edge vision projects more effectively.

We generally accept that gold standard evaluation sets contain label noise, yet we rarely fix them because the engineering friction is too high. This talk presents a workflow to operationalize ground-truth auditing. We will demonstrate how to bridge the gap between algorithmic error detection and manual rectification. Specifically, we will show how to inspect discordant ground truth labels and correct them directly in-situ. The goal is to move to a fully trusted end-to-end evaluation pipeline.

Building AI agents that work in demos is easy. Building ones that people actually rely on every day takes a different set of patterns. This talk shares 10 practical lessons from 18 months of building and shipping a multi-agent system over structured enterprise data, used daily by thousands of business users. These aren't lessons about which model to pick or which framework to use. They're about the patterns between the model and the user: the things you should build into your agent harness, the conversational friction you can avoid, the security you can't leave to a prompt, and the personalization features that turn a capable tool into something people actually reach for. You'll leave with a set of proven patterns for building AI agents that earn trust at enterprise scale.