1290 posts · Page 91 of 108. ← Blog
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.
How to make the model return a typed Go struct instead of prose — hand RunText a pointer, get back populated fields.
Pass RunText a pointer to a Go struct via WithStructuredOutput and the framework asks the model for matching JSON, then unmarshals it straight into your fields.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months. Provider abstraction as the foundation for portable agents.
Caching giveth performance and taketh away your sanity. The same technique that makes systems fast introduces a whole category of subtle, intermittent, hard-to-debug problems — stale data that appears randomly, a cache that collapses under load at the worst moment, bugs that only happen when the cache is cold or full. This closing post catalogs the pitfalls that bite real systems, and distills the practical wisdom of the series: cache deliberately, expect the failure modes, and remember that the two genuinely hard things are still hard.
Caching giveth performance and taketh away your sanity. The same technique that makes systems fast introduces subtle, intermittent, hard-to-debug problems — stale data appearing randomly, a cache collapsing under load, bugs that only happen when the cache is cold. This catalogs the pitfalls and the practical wisdom.
How to gate a sensitive tool behind human consent: the run pauses, hands you an approval request, and only calls the tool once you approve.
Wrap a tool with ApprovalRequiredFunc and the run pauses with a ToolApprovalRequestContent; approve it, feed the decision back on the same session, and the tool fires.