1290 posts · Page 90 of 108. ← Blog
The patterns that worked, the traps we fell into, and what we'd do differently.
What worked, what was hard, and what we'd do differently. Real numbers: 18 agents, 90 days, 5 governance policies, 4 provider swaps.
The most dangerous failure in data engineering is the one that doesn't announce itself. A crashed pipeline gets noticed; silently wrong data flows straight into dashboards and models, where people trust it and make decisions on it — wrong decisions, confidently made. Data quality and governance are the disciplines that guard against this: ensuring data is correct, trustworthy, and well-managed. They're the least glamorous and most important part of production data engineering, because without them, all the pipelines and warehouses just deliver garbage efficiently.
The most dangerous failure in data engineering is the one that doesn't announce itself. A crashed pipeline gets noticed; silently wrong data flows straight into dashboards and models, where people trust it and make wrong decisions confidently. Data quality and governance are the disciplines that guard against this.
This lesson teaches a middleware chain where a guardrail can block a run before the model is ever called, chained ahead of a logger.
Chain a guardrail middleware that yields its own refusal and skips the model ahead of a logger — short-circuiting a harmful request offline before Foundry is called.
Running agents on Cloud Run, exposing via A2A, and wiring into production systems.
Deploy Microsoft Agent Framework multi-agent systems on Cloud Run with A2A agent-to-agent communication, autoscaling, load balancing, and production observability dashboards.
A decade ago, building a data platform meant heavy, monolithic, on-premises systems and long projects. Today it's assembled from specialized cloud tools that snap together around a central cloud data warehouse — ingest here, transform there, visualize over there — each best-in-class at one job. This "modern data stack" is less a specific set of products than an architecture and a philosophy, and understanding its shape (and what drove it) is understanding how data platforms are actually built now.
A decade ago, a data platform meant heavy, monolithic, on-premises systems. Today it's assembled from specialized cloud tools that snap together around a central cloud data warehouse — each best-in-class at one job. This 'modern data stack' is an architecture and a philosophy, and understanding its shape is understanding how data platforms are built now.
This lesson teaches the simplest form of multi-agent delegation: wrapping one agent as a tool another agent can call.
Wrap a specialist WeatherAgent with agenttool.New into a tool.Tool and hand it to a French TravelAgent — one model orchestrating another with no workflow engine.
How to instrument agents for observability, error handling, and audit logging.
Migrate ADK callbacks to Microsoft Agent Framework composable middleware: decorators for audit logging, retry with backoff, token budget enforcement, and OpenTelemetry tracing.
How fresh does the data need to be? That one question splits data engineering into two paradigms. Batch processing handles data in large chunks on a schedule — simpler, cheaper, and fine when yesterday's data is good enough. Stream processing handles data continuously as it arrives — more complex and costly, but necessary when you need to know now. Choosing between them (and knowing when each fits) is one of the most consequential architectural decisions in a data platform, and it's driven by real requirements, not by which sounds more impressive.
How fresh does the data need to be? That one question splits data engineering into two paradigms. Batch processing handles data in chunks on a schedule — simpler and cheaper. Stream processing handles data continuously as it arrives — more complex, but necessary when you need to know now.
This lesson teaches how to send a question and an image together in one message to a vision-capable Foundry agent.
Put a TextContent and a base64 DataContent JPEG in one message and hand it to a vision agent — multimodal input with no upload step, embedded at compile time.
How to port ADK's model hard-codes to Microsoft Agent Framework's provider factory pattern.
Zero-code LLM provider swaps across environments: Ollama for dev, OpenAI for staging, Azure Foundry for prod. Same agents, different models.
The same data can be organized in ways that make analytical queries fast, intuitive, and cheap — or slow, confusing, and expensive. Data modeling is the craft of that organization, and it's where a counterintuitive truth lives: the careful normalization that's correct for application databases is often exactly wrong for analytics. Analytical data wants to be shaped differently, around how questions are asked rather than how data is written, and understanding dimensional modeling is understanding how to make a warehouse actually usable.
The same data, organized differently, makes analytical queries fast and intuitive — or slow and confusing. Data modeling is the craft of that organization, and it holds a counterintuitive truth: the normalization that's correct for application databases is often exactly wrong for analytics.
This lesson teaches how a Foundry agent borrows tools from a remote MCP server and calls them as if they were local functions.
Connect a Foundry agent to Microsoft Learn's public MCP endpoint over streamable HTTP, list its tools, and hand the whole slice to the agent as proxied tools.