Archive

"Enough to Reconstruct, Never Enough to Leak": The HIPAA...

Enough to reconstruct, never enough to leak. The audit event schema problem under §164.312(b), and how to solve it without conflating the audit sink with the PHI sink.

The Future and Responsible AI in Energy

AI's potential to help operate a clean, complex grid is enormous — and precisely because the stakes are so high, this is exactly the domain where AI must be applied most carefully. A wrong forecast is one thing; an AI decision that destabilizes critical infrastructure serving millions is another entirely. The future of AI in energy is not "hand the grid to an AI" but something more disciplined and more valuable: AI as trustworthy decision-support that helps human operators run a decarbonized grid safely. This closing post is about that future, and the responsibility it demands.

AI's potential to help operate a clean, complex grid is enormous — and precisely because the stakes are so high, this is exactly where AI must be applied most carefully. The future is not 'hand the grid to an AI' but something more disciplined: AI as trustworthy decision-support that helps human operators run a decarbonized grid safely.

providers/azure · Azure AI Project

Point the same agent at an Azure AI Foundry project endpoint using the project Responses API mode.

Run an agent against an Azure AI Foundry project: foundryprovider.NewAgent plus ModelDeployment selects project Responses API mode from the project endpoint.

The Fallback Is the Contract: Reliability Patterns for...

Every LLM-backed agent in this platform has a deterministic rule-based fallback. The case always finalises. The fallback isn't a workaround — it's the contract.

Multi-Agent Systems in 5 Interfaces: A Microsoft Agent...

Five interfaces hold the whole platform together. The 30-line orchestrator closure that makes the rest of the architecture testable, auditable, and safe to evolve.

Grid Reliability and Assets

Balancing supply and demand keeps the grid running moment to moment — but the grid also runs on millions of physical assets (transformers, lines, substations) that age, wear, and occasionally fail, sometimes catastrophically. Keeping the grid reliable over time means anticipating and preventing those failures, not just reacting to them. This is a data problem — reams of sensor readings hinting at trouble before it happens — and it's where AI helps the grid stay reliable: predicting failures, spotting anomalies, and monitoring the vast physical system.

Balancing keeps the grid running moment to moment — but the grid also runs on millions of physical assets that age, wear, and occasionally fail catastrophically. Keeping the grid reliable means anticipating failures, not just reacting. That's a data problem, and it's where AI helps: predicting failures, spotting anomalies, and monitoring the vast physical system.

providers · Anthropic (Claude)

The same agent primitive as the Foundry lessons, backed by the Anthropic (Claude) provider instead of Azure AI Foundry.

A provider swap onto Anthropic Claude: build an anthropic.Client, pass it to anthropicprovider.NewAgent, and run the identical agent surface.

PostgreSQL Row-Level Security Is HIPAA Defense in Depth

PostgreSQL row-level security as HIPAA defence in depth. Why fail-open application filtering isn't enough, and how 'append-only at DB GRANTs' carries more of the §164.312(b) burden than people realise.

Demand-Side Flexibility

For a century, grid operation had one basic move: adjust supply to follow demand. The renewable era adds a second, transformative move — adjust demand to follow supply. If you can shift when electricity is used to when clean power is abundant, you turn demand from a fixed constraint into a flexible resource that helps balance the grid. Orchestrating that flexibility across millions of devices and distributed resources is a massive coordination problem, and it's one of the most exciting frontiers for AI in energy.

For a century, grid operation had one basic move: adjust supply to follow demand. The renewable era adds a transformative second move — adjust demand to follow supply. If you can shift when electricity is used to when clean power is abundant, demand becomes a flexible resource that helps balance the grid. Orchestrating that across millions of devices is a massive AI coordination problem.

providers · A2A (Agent2Agent)

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, Expressed in Go

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.

The Renewable Integration Challenge

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.