Weekly deep dives on context engineering, agent architecture, and building software that builds itself.
Senior Software Engineer @ Microsoft (GitHub · Copilot Ecosystem)
I build agentic systems — software that doesn't just assist developers, it collaborates with them. My work lives at the intersection of context engineering, agent governance, and platform architecture.
By day, I work on the GitHub Copilot ecosystem at Microsoft. By night, I run a 43-agent AI home assistant that manages my family's entire life — calendars, meals, finances, health tracking, content creation, and more — all powered by GitHub Copilot CLI.
My core thesis: Make the right thing to do the easy thing to do — for humans AND agents.
I write about what I learn, ship the tools I build, and teach the patterns that actually work. If you're building with AI agents, you're in the right place.
I create content at the frontier of agentic software development. Here's what I write and teach about:
| Pillar | What It Covers |
|---|---|
| 🧠 Context Engineering | Prompt architecture, memory systems, skill files, and making AI agents actually useful |
| 🤖 Agent Skills & Architecture | Multi-agent orchestration, delegation patterns, agent-to-agent communication |
| 🔌 MCP Ecosystem | Model Context Protocol servers, tools, and integrations — including turning your phone into one |
| 🪝 Extension Architecture | Copilot hooks, extensions, governance layers, and guardrails for safe AI coding |
| 🔒 AI Governance | Safe agentic development, content signing, approval workflows, human-in-the-loop patterns |
| ⚙️ Platform Engineering | GitOps for AI, infrastructure-as-code for agent systems, CI/CD with AI in the loop |
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- [Per-Turn Evaluation: Dynamic Governance for AI Agents](https://htek.dev/articles/per-turn-evaluation-dynamic-governance-ai-agents/) — Per-turn evaluation gives AI agents dynamic governance by re-evaluating rules, tools, and context from live state instead of startup config.- [The Functional Options Pattern for AI Agent Composition](https://htek.dev/articles/functional-options-pattern-ai-agent-composition/) — The Go functional options pattern is a clean way to compose tools, guardrails, memory, and middleware into production-ready AI agents.- [What Is Harness as Code? The DevOps of AI Agents](https://htek.dev/articles/what-is-harness-as-code/) — Harness as Code applies Infrastructure as Code principles to AI agents: declarative governance, reproducible behavior, and auditable context.- [Copilot Plugins: Building Domain-Expert AI Teammates](https://htek.dev/articles/copilot-plugins-domain-expert-ai-teammates/) — Build Copilot plugins with domain knowledge, MCP tools, and custom skills so Copilot acts like a specialist teammate, not just autocomplete.- [Custom Copilot Agents: Building Domain-Expert AI Teammates with Skills, MCP Tools, and Custom Knowledge](https://htek.dev/articles/custom-copilot-agents-building-domain-expert-ai-teammates/) — Most teams stop at autocomplete. The real unlock is building custom Copilot agents that know your codebase, workflow, and tools.
Languages
AI & Agents
Cloud & DevOps
Frontend & Frameworks
