Talk · Editorial classification · Date to be announced
We spent years optimizing cloud infrastructure, right-sizing VMs, tuning pipelines, and fighting surprise cloud bills. Now a new cost center has entered the chat: AI. Every prompt, every agent interaction, every repository scan, and every generated line of code consumes tokens—and those tokens add up quickly. AI coding assistants and agents like GitHub Copilot, Claude Code, and OpenAI Codex are transforming how teams build software. But scaling AI adoption without understanding consumption patterns is a bit like giving everyone an unlimited cloud subscription and hoping for the best. (We all know how that story ends.) In this session, we'll explore AI FinOps practices for enterprise engineering teams: how to measure AI consumption, optimize token usage, improve agent workflows, manage context efficiently, choose the right models, and get more value from every AI interaction. Through practical examples with GitHub Copilot and modern AI coding agents, we'll apply familiar DevOps principles—visibility, optimization, governance, and continuous improvement—to the next generation of software delivery. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 14:00–14:30. Timezone requires verification.
Talk · Editorial classification · Date to be announced
We ship an agent that builds .NET apps; the agent itself is .NET and runs in your browser as WebAssembly. This talk is a from-the-trenches tour of the harness behind Uno Platform Studio, built on Microsoft.Extensions.AI and Microsoft Agent Framework. The decisions that survived production, with the numbers that drove them. The centrepiece is a migration story. App generation began as one conversational agent with phase ordering written in prose, enforced by hope, and became a network of typed workflow graphs. You'll see why the LLM router we designed never shipped. Why scaffolding the app, applying themes, and the phase orchestration all became plain code. How that refactor happened, and how that meant a second production pipeline cost us a constructor rather than a new architecture. Build, Measure, Delete: The skills system we engineered onto a strict context budget, the framework feature that came too late, and the day we reversed it all. Deleting our own machinery for the framework's native Agent Skills, watching the old failure return on cue, and settling it with one routing line. We'll finish with questions that we still need to explore, how we might scale out further inside of our agent, and perhaps some final reflections on why LLM codegen is harder than it looks. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 15:30–16:00. Timezone requires verification.
Talk · Editorial classification · Date to be announced
You cannot manage what you cannot measure. For AI agents, evaluation is the same discipline as testing for traditional softwares. It is about how teams manage risk and business outcomes—not just pass a release check—and optimize for safety and quality in a systematic way. In this two-hour, demo-driven session, we will turn business intent into a repeatable eval-fix loop. Using open-source ASSERT, Agent Control Specification (ACS), we will generate targeted tests, trace agent trajectories in OpenTelemetry, ground LLM judge rubrics scoring in your scenario requirements, and choose improvements across deterministic and model-based controls, beyond prompt tuning. We will explore knowledge bases and how retrieval evidence and tool-call traces reveal failures that input/output scoring misses. Then we will hill-climb: compare changes on frozen cases, measure both unsafe behavior and legitimate work preserved, and include cost and latency in the decision. Use Rego for hard rules, classifiers for nuance, and LLM judges for complex reasoning—within a broader governance model of identity, authorization, containment, and audit evidence. Leave with an intent → evaluate → diagnose → fix → remeasure workflow, plus a path from production observations to reviewed regression tests and CI/CD gates as well as promoting that pre-deployment judges to monitoring to detect drifts. The goal: continuously improve the agent while maintaining control over what it is allowed to do. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 10:30–12:30. Timezone requires verification.
Workshop · Editorial classification · Date to be announced
Building reliable AI agents in this fast-moving landscape requests observability and continuous optimization. In this 90-minute workshop, we'll take you on a hill climbing journey from model selection to agent optimization using Microsoft Foundry. Start by understanding model capabilities for various tasks independently. Then create an agent and walk through the Agent DevOps loop to understand observability features. Finally, we'll explore hill climbing in action using two levers - model router and agent optimizer - and understand how to build more effective agents with Microsoft Foundry models. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 13:15–16:00. Timezone requires verification.
Talk · Editorial classification · Date to be announced
Enterprise AI moves beyond experimentation when systems can operate reliably under complex business constraints. Corporate tax compliance presents a compelling example, where rules evolve continuously, calculations span interconnected schedules and jurisdictions, and every automated decision must remain explainable and verifiable. This session explores practical principles for designing multi-agent AI systems that can move from prototype to production in high-stakes enterprise environments. Drawing from experience architecting a production corporate tax preparation system, the session examines how specialized AI agents can divide complex workflows across data intake, tax determination, validation, and return assembly. It demonstrates why explicit agent responsibilities, contract-driven communication, bounded autonomy, and failure containment are essential when building intelligent systems that cannot rely solely on a general-purpose model. The session also explores practical approaches to coordination, evaluation, observability, and auditability, showing how these capabilities can be embedded into an AI architecture rather than added after deployment. Attendees will gain reusable architectural patterns for building trustworthy multi-agent applications, understand common failure modes when moving agentic systems into production, and learn how to design AI solutions that balance autonomy, reliability, transparency, and human accountability in enterprise scenarios where correctness matters. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 12:00–12:30. Timezone requires verification.
Talk · Editorial classification · Date to be announced
Your agent demo works. It's fast, it's cheap, it's magic in the sandbox. Then you deploy it and everything falls apart. No one agrees on how to test it, monitor it, or improve it, and the token bill doesn't care that it's "still just a pilot." We solved this exact problem for code decades ago with the SDLC. We solved it again for APIs once the sprawl got out of hand. Now it's agentic AI's turn, and teams are trying to skip the step where you learn from history. In this talk, I'll break down the Agent Development Lifecycle (ADLC): what it borrows from software and API management, and what to actually consider when designing an agentic system: cost, governance, security, and repeatability. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 10:30–11:00. Timezone requires verification.
Talk · Editorial classification · Date to be announced
Generative AI is moving beyond text and images into simulating entire environments. This session breaks down "world models," AI systems that learn to represent scenes, predict how situations evolve, and generate realistic synthetic data. We'll cover the core architectural building blocks behind leading approaches and recent published results showing double digit gains in accuracy and output quality. The concepts apply directly to synthetic data generation and scenario testing in any AI heavy domain. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 14:45–15:15. Timezone requires verification.
Talk · Editorial classification · Date to be announced
An AI agent can work perfectly in a demo and start acting strange in production. Data changes, retrieval gets stale, tools evolve, permissions shift, models update, workflows move, and users discover edge cases nobody tested. Suddenly the helpful assistant is confidently wrong, calling the wrong API, or giving answers nobody trusts. This session shows developers and architects how to detect and prevent agent weirdness before it becomes operational pain. Samantha will cover practical patterns for testing, evaluation, observability, lifecycle ownership, escalation, governance hooks, and retirement so production agents remain useful, secure, maintainable, and aligned as enterprise reality changes daily. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 13:15–13:45. Timezone requires verification.
Talk · Editorial classification · Date to be announced
hamba.nl is a live Dutch travel magazine built with the GitHub Copilot App and run by seven Foundry-hosted agents, orchestrated with Agent Framework, covering the full editorial pipeline from draft to published article. In this session I'll take you under the hood: how the agents are scoped and orchestrated, how they connect to tools and data through MCP, how content, moderation, and fact-checking roles hand off to each other, and how a human-in-the-loop stays in control of final approval. This is a working system with real failure modes, and you'll leave with a concrete reference architecture for agent teams that run a function, not just generate code. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 11:15–11:45. Timezone requires verification.
partial: All verifiable upcoming-edition names on the inspected page were extracted. This is not a claim that the final event roster is complete. Reviewed 2026-09-11T16:44:36.020Z.
sponsors
not published: Upcoming-edition details were not published in the inspected organizer source. Reviewed 2026-09-11T16:44:36.020Z.
exhibitors
not extracted: Not yet extracted from an edition-specific public source. Editorial review date unavailable.
attendees
not extracted: A public named attendee roster has not been extracted. Speakers and company employment do not imply named attendance. Editorial review date unavailable.
agenda
partial: All verifiable upcoming-edition names on the inspected page were extracted. This is not a claim that the final event roster is complete. Reviewed 2026-09-11T16:44:36.020Z.
Sources
Showing 5 of 5 source records. A source check is not a claim of complete extraction.
Organizer lists Central Time for Toronto and event header ends16:00 while agenda extends17:00. Preserve date; time/timezone require resolution. Full published speakers/agenda retained in source.