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Typing a sentence and watching a detailed, coherent, never-before-seen image appear is one of the most striking capabilities in modern AI — and the technique behind it is beautifully counterintuitive. Rather than paint an image stroke by stroke, these models start with pure noise and gradually remove it, step by step, sculpting a picture out of static, guided by your text. Understanding diffusion — and how text steers it — demystifies text-to-image generation and reveals one of the most important generative techniques in AI.
Typing a sentence and watching a detailed, never-before-seen image appear is one of the most striking capabilities in modern AI — and the technique behind it is beautifully counterintuitive. These models start with pure noise and gradually remove it, sculpting a picture out of static, guided by your text. That's diffusion.
This lesson teaches how to chain three role-specialised agents — researcher → fact_checker → reporter — into one sequential workflow and stream each stage.
A sequential workflow of three role-specialised Foundry agents — researcher, fact_checker, reporter — built with NewSequentialWorkflowBuilder and streamed stage by stage.
The multimodal capability people actually experience — uploading a photo and asking a chatbot what's in it, or having it read a screenshot, explain a diagram, or extract data from a chart — comes from vision-language models: LLMs that can see. The clever part is how it's done. Rather than build a seeing-and-reasoning model from scratch, you take a language model that already reasons brilliantly and give it eyes, by connecting a vision encoder to it. Understanding how that connection works explains the multimodal AI most people use.
The multimodal capability people actually experience — uploading a photo and asking a chatbot what's in it — comes from vision-language models: LLMs that can see. The clever part is how it's done: take a language model that already reasons brilliantly and give it eyes by connecting a vision encoder to it.
This lesson teaches that orchestration is a property of the workflow, not the agents — the same three agents drop into three different built-in graph shapes.
The same three agents dropped into three built-in agentworkflow builders — sequential, concurrent, and round-robin group chat — showing orchestration is a property of the graph, not the agents.
Agents return a clean 200 OK and still be wrong, unsafe, or expensive. Why agentic AI needs a new observability layer — LLM-as-judge, safety metrics, and the four lifecycle stages.
The magic trick at the heart of modern multimodal AI is deceptively simple: train an image encoder and a text encoder together so that a picture of a dog and the words "a photo of a dog" land at the same spot in a shared space. Once images and text live in one common representational space, a cascade of capabilities follows — searching images by text, classifying without task-specific training, and grounding language generation in vision. CLIP is the model that made this idea famous, and understanding it is understanding how modalities actually get connected.
The magic trick at the heart of modern multimodal AI is deceptively simple: train an image encoder and a text encoder together so a picture of a dog and the words 'a photo of a dog' land at the same spot in a shared space. Once images and text live in one common space, a cascade of capabilities follows. CLIP is the model that made this famous.
This lesson teaches how to host real `agent.Agent`s as workflow executors and chain them into a sequential pipeline.
Host three translation agents as workflow executors and chain them French to Spanish to English, using DisableForwardIncomingMessages and a TurnToken to drive a strict pipeline.
OWASP Agentic Top 10 coverage with YAML policy files, two API surfaces, and a metric bridge that shows policy denials in Grafana.
To a computer, an image is just a grid of numbers — millions of pixel values with no inherent meaning. Turning that raw grid into something a model can understand (this is a dog, that's a face, here's text on a sign) is the problem of computer vision, and the way it's solved has changed dramatically. The field moved from hand-crafted feature detectors, to convolutional networks that learn features, to — most recently — the surprising discovery that the transformer architecture behind language models works remarkably well for images too. Understanding how models see is the foundation of the vision side of multimodal AI.
To a computer, an image is just a grid of numbers with no inherent meaning. Turning that raw grid into understanding is computer vision, and the field moved from hand-crafted features, to convolutional networks, to the surprising discovery that the transformer architecture behind language models works remarkably well for images too.
This lesson teaches how to build a two-executor pipeline and watch it run as a live stream of typed events.
Your first Agent Framework Go workflow: two string executors wired by an edge, run with RunStreaming and watched as a stream of typed events, fully offline.
I built memory, communication, security, governance, and evals from scratch first. Then I deleted most of it and used the Microsoft Agent Framework-native packages. Here is the audit table and what survived.
For most of the deep-learning era, an AI model did one thing with one kind of data: this model classifies images, that one translates text, another transcribes speech. Multimodal AI breaks that separation. A single model can now look at an image and describe it, answer questions about a chart, generate a picture from a sentence, or transcribe and reason about audio — because it works across modalities rather than being confined to one. This shift, from single-modality specialists to models that bridge vision, language, audio, and more, is one of the most important developments in modern AI.
For most of deep learning, a model did one thing with one kind of data. Multimodal AI breaks that separation: a single model can look at an image and describe it, generate a picture from a sentence, or transcribe and reason about audio — working across modalities rather than being confined to one. It's one of the most important developments in modern AI.