Agent Engineering
Skills, slash commands, hooks, MCP, harnesses, and the multi-agent systems built on top of them — explained from first principles, by the Noddle Deck team.
Agent Engineering
Agent Skills, Explained: Teach Your AI Agent Like You'd Onboard an Engineer
Skills package expertise so an agent can load it only when the task calls for it. Here's the anatomy, the lifecycle, and the mistakes that make skills invisible to the agent that should be using them.
Agent Engineering
Slash Commands: Reusable Prompts You Can Ship
A slash command turns a prompt you'd otherwise retype into a versioned, shareable action — the difference between a good prompt and a piece of team tooling.
Agent Engineering
Hooks: Deterministic Guardrails for Non-Deterministic Agents
Hooks run outside the model's judgment entirely — deterministic checks that fire before or after a tool call so the same rule holds every single time, not just most of the time.
Agent Engineering
MCP: The USB Port for AI Agents
The Model Context Protocol gives agents one standard way to talk to any tool or data source, instead of a bespoke integration for every model-and-service pair.
Agent Engineering
The Agent Harness: Everything Around the Model
The model is one component. The harness — the loop, the tool router, the context manager, the permission system — is what actually turns it into a working agent.
Agent Engineering
Multi-Agent Systems: Orchestrating a Team of AIs
Splitting a task across a lead agent and several subagents can beat one big context window — if you get the handoffs and the shared state right.
Agent Engineering
Loop Engineering: Agents That Run While You Sleep
Long-running agent loops need their own discipline — checkpointing, budget limits, and exit conditions — or they either stall out or burn through your quota unattended.
Agent Engineering
Inside the Claude Opus System Prompt: an Architectural Teardown
A leaked 183 KB claude.ai system prompt, measured section by section: what's actually behavior versus plumbing, where the identity sentence sits, and what three model releases patched.
Agent Engineering
The Claude Sonnet System Prompt as a Control Loop
Sonnet's behavior isn't fixed by a document loaded once at the top of a chat. It's maintained turn by turn — a static base, per-turn injections, classifier-triggered payloads, and drift reminders that fire only once a conversation has run long enough to need them.
Agent Engineering
The Claude Fable 5 System Prompt: What a New Model Tier Looks Like on Paper
A leaked prompt repo shows what actually changes on paper when a vendor ships a new model tier — and the most interesting finding isn't what got added, it's what got shorter.
Agent Engineering
What an AI Architect Actually Does
Someone ends up owning model selection, the harness, the skill library, evals, cost budgets, and guardrails as a single system — whether or not their org ever writes the title down.
Agent Engineering
How to Build an AI Data Platform Your Business Applications Can Search
Most 'AI search' is a chatbot wired straight to a database, and it fails for a predictable reason: the model has nothing trustworthy to stand on. Here's the reference architecture that builds discovery before it builds the agent — and the adapter pattern that keeps business apps decoupled from it.
Build in Public
One canvas, three tools: how a crew of three ships Noddle
The Noddle family in plain terms — Board, draw, Deck, and Docs next. Why three people ship one product at a time, and where each tool honestly stands.
Data Engineering
Data Reconciliation, Explained: Proving Your Data Survived the Move
A successful copy job is not evidence. Here's how reconciliation actually works when data moves from zone A to zone B — the four levels of checks, the principles that keep comparisons honest, and the metrics that make it governable.