1046 posts · Page 75 of 88. ← Blog
How to borrow tools from a remote Model Context Protocol server and hand them to a Foundry agent as ordinary tools.
Connect to Microsoft Learn's public MCP server with mcptool.Connect and ListTools, then hand the borrowed tools to a Foundry agent as ordinary agent.Config.Tools.
How a single sprint of specialty-rule work — guided by a benchmark that wasn't afraid to print embarrassing numbers — turned a 'demo respiratory differential' into a five-condition rule-based diagnostic engine.
This is the grid's central act: with forecasts in hand, decide — continuously, in real time — exactly how much each resource should produce so that total supply matches total demand while respecting a web of physical and economic constraints. It's a colossal optimization problem, solved every few minutes, and it's getting harder as the grid grows more complex. Understanding how balancing works, and where AI helps, is understanding the operational heart of the grid.
This is the grid's central act: with forecasts in hand, decide continuously how much each resource should produce so supply matches demand while respecting a web of physical and economic constraints. It's a colossal optimization problem, solved every few minutes, and it's getting harder as the grid grows more complex.
How a shared state snapshot rides along with every AG-UI turn, so client and server-hosted agent stay in sync on evolving structured data.
Share a JSON state snapshot across AG-UI turns — server middleware emits a DataContent snapshot from the model's JSON and the client adopts it via toStateContent and extractState.
What HIPAA looks like when you express it as Go interfaces — governance policies, append-only audit at DB GRANTs, PHI redaction at the logger seam, and HITL as the §3060 CDS carve-out criterion 4.
If the grid must balance supply and demand every instant, and much of both is now uncertain, then everything depends on one thing: seeing the future as clearly as possible. How much electricity will people use in the next hour, the next day? How much will the wind and sun provide? These forecasts drive every operational decision, and improving them — which is fundamentally a machine-learning problem — is one of the highest-leverage places AI helps the grid. Better forecasts mean a grid that balances more efficiently, reliably, and cheaply.
If the grid must balance supply and demand every instant, and much of both is now uncertain, everything depends on seeing the future clearly. How much power will people use? How much will wind and sun provide? These forecasts drive every operational decision, and improving them — fundamentally a machine-learning problem — is one of the highest-leverage places AI helps.
How an approval-required tool makes the server pause a run and wait for a human on the client to say yes or no.
Gate a tool behind tool.ApprovalRequiredFunc so the AG-UI server pauses the run, then answer the approval from the client with a message round-trip loop.
What looked like an idiomatic BigQuery MERGE was scanning the full target table on every batch. The fix was syntactic, not architectural — and it was the single biggest contributor to a 57% data-warehouse cost reduction across the Tata Group engagement.
You can't apply AI to a system you don't understand — and the grid, for all its ubiquity, is genuinely unfamiliar territory for most engineers. It runs on physics that permit no delay and no buffer: electricity flows at the speed of light, can't be meaningfully stored at scale, and must be balanced instant by instant or the whole system destabilizes. Before exploring how AI helps, it's worth understanding how the grid actually works — because its physical constraints are exactly what make it such a demanding problem.
You can't apply AI to a system you don't understand — and the grid is unfamiliar territory for most engineers. It runs on physics that permit no delay and no buffer: electricity can't be meaningfully stored at scale and must be balanced instant by instant or the whole system destabilizes.
How the server-hosted agent can call a tool that actually runs on the client, and the one flag that makes it work.
Let a server-hosted agent call a client-side tool over AG-UI — the server sets DisableFuncAutoCall and forwards the call to the client, which runs the Go function locally.
Architecture decisions that delivered multi-million dollar savings at petabyte scale.
Architecture decisions that delivered 57% cost reduction on Tata Group's BigQuery data warehouse. The MERGE anti-pattern fix, partition strategy, capacity model, and the trust gap I'd handle differently next time.
The electrical grid is quietly becoming one of the most complex control problems humanity has ever attempted. For a century it was relatively simple: a few big, controllable power plants supplying predictable demand. Now it's millions of variable renewable sources, distributed generation, electric vehicles, and shifting demand — all of which must be balanced, second by second, or the lights go out. That explosion of complexity is turning grid operation into a data and optimization problem, and it's why AI is becoming essential to keeping the lights on in a decarbonizing world.
The electrical grid is quietly becoming one of the most complex control problems humanity has attempted. For a century it was simple: a few controllable plants supplying predictable demand. Now it's millions of variable renewable sources that must be balanced second by second, or the lights go out. That complexity is turning grid operation into an AI problem.