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Build a local C# approval workflow where risky AI-proposed actions create explicit approval requests, consume scoped expiring tokens, and leave behind auditable decision records.

Treat long-running agent context as a controlled system. Pin required facts, compact old history, expire stale notes, and carry forward only what the next turn is allowed to rely on.

Treat live MCP servers as versioned tool contracts. Discover live schemas, replay frozen probes, and block risky drift before promotion.

Treat model swaps as controlled experiments: frozen evals, shadow replay, deterministic scoring, and explicit promotion gates before an AI system changes models.

Repository instruction files are becoming the control plane for AI-assisted coding: a short AGENTS.md map, deep docs as the system of record, and task-specific specs for execution.

A gateway that discovers, validates, and delegates to a remote specialist over A2A. Both agents run on Microsoft Foundry and deploy to Azure Container Apps from one image.

Build a support agent workflow in C# with trusted vs untrusted content labels, deterministic tool authorization, approval-gated sensitive actions, and redacted audit storage.

Build a durable local-first agent workflow in C# with narrow roles, deterministic policy-bounded execution, live runtime logs, and restart-safe state, using home repair as the concrete example.

Build a grounded local-first multi-agent research workflow in C# with deterministic retrieval, narrow agent roles, structured JSON contracts, and final citation validation.

Build a local-first inference gateway with exact cache keys, per-tenant budget reservation, sync-vs-batch routing, deferred execution, and measurable cost controls in C#.