1290 posts · Page 95 of 108. ← Blog
This lesson swaps the LLM behind an agent for another agent, reached over the A2A protocol instead of a model API.
Back a Go agent with another agent over the A2A protocol: resolve the remote card, open a gRPC client, and wrap it with a2aprovider.NewAgent.
The 21st Century Cures Act §3060 CDS carve-out criterion 4 expressed as a code-level queue, lossless on reject, with audit-recorded reviewer rationale. Build it once, satisfy GDPR Article 22 for free.
A grid of cells, each either on or off, each following the same trivial rule based only on its neighbors — and from that almost-nothing emerges breathtaking complexity: gliders that move, patterns that compute, structures that reproduce. Cellular automata are among the most profound ideas in computer science, a vivid demonstration that complex behavior can arise from simple local rules. This series builds them from first principles, starting with what they are and why they matter.
A grid of cells, a few states, a neighborhood, one local rule applied in lockstep — and from that almost-nothing emerges breathtaking complexity. What a cellular automaton is, its four ingredients, and the astonishing core idea of emergence: complex global behavior from simple local rules, with no designer.
Renewables are the solution to decarbonizing electricity and the source of the grid's hardest new problem — the same fact viewed two ways. Wind and solar are clean and increasingly cheap, but they are variable (they produce when the weather allows, not when you need it) and uncontrollable (you can't turn up the sun). Integrating large amounts of this variable, uncontrollable generation into a grid that must balance every instant is the central technical challenge of the energy transition, and it's where AI's value to the grid concentrates.
Renewables are the solution to decarbonizing electricity and the source of the grid's hardest new problem — the same fact viewed two ways. Wind and solar are clean but variable (they produce when the weather allows) and uncontrollable. Integrating large amounts of this into a grid that must balance every instant is the central technical challenge of the energy transition.
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.