Predavanja

Agent 365 isn’t just another AI announcement — it’s Microsoft’s vision for how companies will manage an entire workforce of AI agents. Fresh out of preview and now generally available, Agent 365 introduces a new “control plane” for AI inside Microsoft 365, giving organizations the ability to discover, govern, secure, and scale autonomous agents across Teams, Outlook, SharePoint, Windows, and even third-party platforms.

In this session, we’ll unpack why Microsoft believes the future of work will involve billions of AI agents acting as digital teammates — and why governance, security, and visibility suddenly matter more than ever. From “shadow AI” running on employee devices to intelligent agents capable of taking action across enterprise systems, Agent 365 is redefining how organizations think about productivity, automation, and trust. Expect real-world scenarios, launch insights, and a practical look at what happens when AI moves beyond copilots and becomes part of the operational fabric of the workplace.

AI agenti više ne samo odgovaraju na pitanja, sada i djeluju: izdaju račune, čitaju knjige, šalju podatke dalje. Model Context Protocol (MCP) omogućava da ih spojimo na vlastiti sustav. Ali… Jesmo li se zapitali što bi se dogodilo kada bi agent dobio pristup financijskim podacima tisuća tvrtki? Tko je zapravo autenticiran, korisnik ili agent? Kako agentu dati pristup samo jednoj tvrtki, kada isti korisnik radi za njih deset? I što ako agent odluči fiskalizirati račun koji se više ne može povući? Kroz stvarni primjer MCP servera za Parra platformu proći ćemo OAuth autorizaciju za MCP, multi-tenant model ovlasti, dizajn alata koji razlikuje sigurne od nepovratnih akcija. Dođite na ovo predavanje i saznajte kako AI agenta pustiti u produkciju, a da ne izgubite san. Vidimo se!

We will walk through a case using an AWS Cognito-based authentication system implemented for MG Motors, a UK-based car manufacturer.

We will showcase a setup using Cognito User Pool groups and resource servers to provide a highly secure authentication setup that covers both human users, by implementing cross-environment Single Sign-On, and external integrators and MCP/Agentic access to query and interact with data, using granular access scoped to tenants in 30+ markets across Europe.

Building an app is now ridiculously fast. Getting someone to actually use it hasn't sped up nearly as much.
Using Cjenici.parra.hr, we walk through the whole path: the need, the prompt, actually building it and all the unexpected legal, marketing and support steps that it took to get to the real users in only one week.

We will talk about FIDO2 and various options for authentication, and then focus on newly released Passkey option with various examples and use cases, including benefits from practical and security point of view, and some limitations of course. How to use it for various systems including Microsoft Entra ID

How do you know where several million items, across a few hundred item types, sit in their lifecycle, when you cannot go and inspect them, and when that lifecycle shifts with both time and external events?
We started where most teams start: one very large table, cron jobs and triggers, then an LLM to keep it up to date. The lesson came early: we first needed a formal description of the item type and of the item itself, and then a lifecycle we could observe and control.
Our first attempt modelled both as state-machine workflows. The lifecycle rules turned out to be a poor fit for AI, and workflows brought their own problems at this data volume. The alternative, running models straight against the big table, was no better.
Digital twins gave us the model we were missing. We moved through several implementations, from fixed lifecycle rules to an AI-driven lifecycle, and now run on Fabric Digital Twins with AI agents driving item lifecycle - a ship-of-Theseus migration where the system keeps running while every part is replaced.
This session is the data and AI view of that journey, with a live demo of what we run today - and a chocolate-sweet finish, as always.
Key takeaways:
Where a “big table plus cron plus LLM” approach breaks, and the signals that tell you before it does
Why rule-based state machines and AI-driven lifecycle prediction pull in opposite directions
How to split responsibilities between the twin model and the agents acting on it

Traditional computer vision models are great at answering one question: “Is something wrong with this image?” But detecting an anomaly is only the beginning of an operational process.

In this session, we will explore an end-to-end architecture that combines Azure Machine Learning, specialized computer vision models such as YOLO and PatchCore, multimodal LLMs, and AI agents built with Microsoft Foundry.

A vision model first detects or scores potential anomalies in images. Instead of sending raw detections directly to operators, a multimodal model analyzes the flagged image together with the detection results, describes what happened, adds operational context, and produces structured information that another AI agent can act upon. The operational agent then decides how to handle the case using available tools and workflows, while a separate supervisory model monitors and evaluates its actions.

We will discuss the architecture, responsibilities of each model, information flow between deterministic ML and generative AI components, and why combining specialized models with multimodal LLMs and supervised agents can be more effective than trying to solve the entire problem with a single model.

There are multiple ways to implement RAG (Retrieval-Augmented Generation), a system that connects an LLM to an external knowledge base. But which RAG architecture should we choose for our specific use case? In this talk we'll explore different RAG architectures and compare their strengths and weaknesses. In the traditional RAG approach we use a vector database to store embeddings that were generated from our data. For some scenarios a better option is Graph RAG which uses a knowledge graph to map relationships between entities. We can also choose Agentic RAG, which uses autonomous AI agents. There's also Multi-Agent RAG. And the list of RAG architectures does not stop there.

The Active Directory tiering model is designed to protect privileged identities and reduce the risk of lateral movement—but real-world administration often introduces challenges that aren't covered in the documentation.

In this session, we'll walk through practical scenarios that administrators encounter when managing Microsoft Failover Clusters while maintaining a secure tiering model. You'll learn how to perform day-to-day cluster management without violating administrative boundaries, how to recover and repair a failover cluster without breaking tiering, and which common operational practices can unintentionally weaken your security posture.

Through real-world examples, we'll explore common pitfalls, explain why they happen, and demonstrate practical solutions that allow you to keep both your infrastructure highly available and your privileged access model intact. Whether you're responsible for Active Directory, Windows Server, or clustered workloads, you'll leave with actionable guidance that can be applied immediately in your environment.

We have been hearing about artificial intelligence for a while now. The advantages of AI, new technologies, way to utilise them… But it usually starts going in a different direction: Can something go wrong with AI? A very interesting question that always veers into the scenario from a certain cheesy 1984. SF movie starring Arnold Schwarzenegger.
No, ChatGPT will not destroy the world because we made it fix our ugly React code, but there are many scenarios where irresponsible use of AI can lead to unforeseen consequences. You may have already experienced some of them without even knowing.
This talk intends to educate the audience on a different way to talk about AI and its potential dangers. We will go over some lesser-known, but still very interesting AI scenarios and thought experiments and see what kind of future and (potential) dangers of AI they predict, how to recognise these dangers in our own products, see real-life examples of misuse of AI in software development and, most importantly, how to prevent the worst from happening.

AI can now write code, docs and reviews, and the line between engineers and designers is fading. We're all builders now. What still sets people apart is judgment, taste and product sense.

My talk covers five practices engineers can start making today: question first, remember you're not the user, treat information architecture as a deal breaker, understand the context, and say no to decorative elements, to build a better product. Each comes with a real product story, plus tools to put it into practice.

Session donosi potpuno novi pristup aktualnoj temi o sidrenim cijenama i digitalnim cjenicima, sada s osvrtom na TDM (Opt-out) iznimku i da li Odluka Vlade RH o obaveznoj objavi digitalnim cjenika u strojno čitljivoj formi onemogućava i u kojem dijelu poduzetnike od korištenja te iznimke. Predlaže se kao mogućnost panel sa web developerom koji ima iskustva u primjeni anti-scrapping mjera na stranicama i da li je ovo kraj poruke " Ovo web-mjesto upotrebljava sigurnosnu uslugu u svrhu zaštite protiv zlonamjernih botova. Ova se stranica prikazuje dok web-mjesto provjerava niste li bot."

Predavač: Anamarija Jengić Bujan, Intellectual property specialist

Join this session to learn how to properly manage and secure Microsoft’s Identity as a Service, whether your organization uses it in a hybrid setup with Active Directory or as a cloud-only solution.

Learn from Tomislav's experience with many large organizations managing Entra, as he optimized the platform in terms of authentication methods, guest, application and agent access, synchronization, and password expiration and resets.

This is a must attend session for Entra admins!

AI coding assistants and autonomous agents produce changes faster than teams can inspect every line. The bottleneck is no longer writing code; it's reviewing it. How do we verify behavior and release safely at AI speed? How do you stay accountable when nobody can read every line that ships?

We present one delivery control system consisting of four automated layers: BDD scenarios define behavior, guardrails as code enforce policy, independent agents review every change, and observability with progressive deployment rings tests assumptions against production signals. Each catches a different class of failure and emits evidence, and every escape becomes a new rule. Humans operate above the system, analyze evidence, and act when necessary.

You will leave with a system where humans own intent, exceptions, and release decisions. Review can be automated. Accountability cannot.

AI now writes an application that works. The code passes its tests and can go to production.

The problem is that we have spent years talking about software mostly through code. Projects rarely fail on code. They fail because nobody understood the process, the rules and the exceptions, or because people never actually used it.

The talk walks through what sits between "works on my machine" and a system somebody actually uses: the domain and the people, business rules and exceptions, non-functional requirements, data migration, rollout and change management. Three examples from practice, invoice approval as the process everyone knows, and sixty years of programming history where the same pattern keeps repeating.

The second half is about why the domain stays with us. If you don't know the field, you can't write a good prompt or judge the answer you get back. That holds for any profession, not just software.

For engineers, managers, and anyone who commissions or delivers business software.

Being an Offensive Security Engineer is an interesting job. You have clients who understand the meaning of a time-restricted assessment and whitelist everything for you. You have others that don't really know what they want, just expect results, but won't actually make it easy for you, so you have to fight your way through their defenses. And then, there is the holy grail of clients...the one that on a beautiful Monday morning will send you an email and request an adversary simulation using malware, and in specific, a Wiper!

Challenge Accepted!...I hear you say, and so did my team. As a result, we researched it to the bone. Not only the instance we were gonna execute, but also the whole story behind Wipers. When was the first appearance of this kind of malware? How did it evolve through the years? What mechanisms or tactics do adversaries use to deliver it to the victims? Why did it resurface in recent years? And most importantly....did we delete all the client's data?

These questions and many more will be answered in this talk. In this session, I'll present the evolution of the "Wiper" malware family through the lense of an offensive security engagement. Furthermore, I'll explain in depth the technical aspect, analyzing their functionality and you will witness how this malware can be integrated in offensive operations. Finally, we'll explore the way Wipers are used today, the role they play in hybrid warfare, and what the future holds for malware in general.

Many organizations nowadays invest in data and AI while overlooking the human side of it all. The result: users end up like Alice in Wonderland, dropped into an unfamiliar world full of possibility, with no idea which way to go. We design experiences that feel powerful in demos but confusing in real life.

In this session, we'll explore how decades of Human Computer Interaction research can help us design better AI agents in Copilot Studio. We'll draw on principles like the Blank Canvas Paradox, cognitive load, and mental models to understand what makes an interaction feel effortless rather than exhausting.

Using Microsoft's Human AI Experience (HAX) guidelines as a foundation, we'll translate psychological UX principles into practical Copilot Studio design patterns that can be applied immediately. You'll walk away with a practical toolkit for designing agents in Copilot Studio that are user friendly and effective.

Whether you're a maker, consultant, designer, or architect, this session will give you a practical framework for designing Copilot Studio agents people actually understand how to use. Let's give Alice some directions.

CI pipelines are among the most privileged and least watched identities in a cloud account. This talk walks through replacing stored access keys with GitHub OIDC federation, then tightening in three layers:

Who can assume the role. Pin the trust to an exact repo, branch, environment, and workflow, keyed on immutable repo IDs rather than names.

What the role can do. CI only starts an orchestrator and writes build artefacts under one storage prefix. It never runs containers or passes roles directly.

Guardrails. Add explicit denies and immutable artefacts.