1046 posts · Page 71 of 88. ← Blog
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.
How to port tools, add policy enforcement, and integrate OPA.
Migrate ADK functions to Microsoft Agent Framework governed tools with policy enforcement, DLP scanning, approval gates, and OPA integration for production agent systems.
"Just put it in a database" stops working the moment you're dealing with analytics at scale — because the database that runs your application is optimized for exactly the wrong thing. Analytical data needs different storage: systems built to scan and aggregate huge volumes, not to serve fast individual transactions. The landscape of analytical storage — data warehouses, data lakes, and the newer lakehouses — is where data engineering decisions about where data lives get made, and understanding the differences (starting with OLTP vs OLAP) is essential.
'Just put it in a database' stops working the moment you're doing analytics at scale — the database that runs your app is optimized for exactly the wrong thing. Analytical data needs different storage: warehouses, lakes, and lakehouses. Understanding the differences (starting with OLTP vs OLAP) is essential.
How one middleware wraps every agent run in an OpenTelemetry span tagged with the gen_ai semantic attributes — the same one-line hook you use for logging.
otelprovider.NewMiddleware wraps every run in an OpenTelemetry span tagged with gen_ai attributes — the same one-line middleware hook you use for logging.
How conversation threads replace session state; how to track token usage across agent chains.
Sessions to threads: porting multi-turn state from ADK to Microsoft Agent Framework. Token budgeting, long-term memory, and conversation audit trails.
The core artifact of data engineering is the pipeline: an automated flow that pulls data from somewhere, reshapes it, and lands it somewhere useful. And the single most consequential shift in modern data engineering is captured in three reordered letters — ETL became ELT — a change driven by cheap, powerful cloud data warehouses that flipped when and where transformation happens. Understanding pipelines, and the ETL-to-ELT shift, is understanding how data actually moves and gets made usable.
The core artifact of data engineering is the pipeline: an automated flow that pulls data from somewhere, reshapes it, and lands it somewhere useful. And the single most consequential modern shift is captured in three reordered letters — ETL became ELT — driven by cheap, powerful cloud data warehouses that flipped when and where transformation happens.
How to serialize an agent.Session to disk and resume it later, so a follow-up prompt still remembers earlier turns across a process restart.
An agent.Session serializes with encoding/json, so you can marshal it to disk or a database and resume it later with the model still remembering earlier turns.
How to port ADK's orchestration callbacks to Microsoft Agent Framework builders without losing control.
How to port ADK's orchestration callbacks to Microsoft Agent Framework builders without losing control. The executor pattern: you own the loop.
Every dashboard, every analytics query, every machine-learning model, and every "data-driven decision" rests on an invisible foundation: someone built the pipelines that collect, move, clean, and organize the data so it's actually usable. That someone is a data engineer, and their work is the unglamorous, essential plumbing beneath everything data. When it works, no one notices; when it breaks, every downstream report and model breaks with it. Understanding data engineering is understanding how raw data becomes something a business can actually use.
Every dashboard, analytics query, and ML model rests on an invisible foundation: someone built the pipelines that collect, move, clean, and organize the data. That someone is a data engineer, and their work is the essential plumbing beneath everything data. When it works, no one notices; when it breaks, every downstream report and model breaks with it.