1290 posts · Page 86 of 108. ← Blog
This lesson teaches how to embed a whole built workflow as a single executor inside a larger one — workflows compose.
Embed an entire built workflow as one executor with inproc.BindSubworkflowAsExecutor, composing a Prefix to SubWorkflow to PostProcess parent graph that runs fully offline.
The concepts of stream processing — events, time, windows, state, fault tolerance — are realized by a handful of systems, and choosing among them is one of the main practical decisions in building a streaming pipeline. This post maps the landscape: the engines, the models they embody, and how to think about which fits a problem. It's the bridge from understanding stream processing to actually running it.
The engines that implement stream processing: Flink (the heavyweight true stream processor), Spark Structured Streaming (micro-batch), Kafka Streams (library on Kafka), cloud-managed services, and the unifying Beam/Dataflow model. How to choose by state/correctness, ecosystem, ops capacity, and latency — plus the rise of streaming SQL.
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.
A stream-processing job runs forever, which means it will experience failures — nodes crash, networks partition, processes restart. The question isn't whether failure happens but what happens to your results when it does: do you lose events, double-count them, or get exactly the right answer anyway? Fault tolerance, and the famous "exactly-once" guarantee, are what separate a toy stream processor from one you can trust with money. This post is about surviving failure correctly.
A forever-running job will fail, so the delivery guarantee matters: at-most-once (may lose), at-least-once (may duplicate), or exactly-once (correct as if neither). How checkpointing and replayable sources achieve exactly-once semantics, and choosing the guarantee by need — with idempotency as the pragmatic route.
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.
Simple stream operations — filter this event, transform that one — need no memory. But the interesting computations — counts, aggregations, joins, windows, pattern detection — require remembering things across events, and that memory is state. Managing state correctly over an unbounded stream, at scale, with fault tolerance, is the hardest engineering problem in stream processing. This post is about state: why it's needed, what makes it hard, and how stream processors manage it.
Interesting stream computations — counts, aggregations, joins, windows, patterns — need to remember across events, and that memory is state. Why state is hard on an unbounded stream (bounding, speed, partitioning, surviving failure), and how engines provide managed keyed state.
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.