In every great kitchen, speed and consistency don't come from more gadgets. They come from mise en place — the small, disciplined set of knives, pans, and staples laid out the same way, every time. I learned this watching a chef glide through a Friday dinner rush with the grace of a violinist. Her secret wasn't molecular gastronomy equipment; it was a ruthless commitment to the few tools she trusted and the rituals that made them predictable.

Most AI teams are trying to cook a grilled cheese with an immersion circulator and liquid nitrogen. They wrap their models in agents, their agents in orchestrators, their orchestrators in monitoring, and their monitoring in more dashboards with sprawling rule files. The result isn't better food - it's longer prep times, more points of failure, regression defect tickets, technical debt, and confused line cooks. The complexity tax doesn't disappear; it lands squarely on your people.

Your AI kitchen doesn't need more stations. It needs a mise en place.

Choose the Knife, Not the Kitchen Remodel

A simplicity-first AI workflow should look like a chef's tool roll: a small set of robust, purpose-built tools with conversational prompting as the default UX, and light guardrails for structure and safety. Pair this with some internal training and practice, and you have a recipe for reduced operational drag and increased developer output.

This is not Luddism; it's throughput. The evidence is clear: minimal stacks avoid heavy abstractions, trim maintenance, and let teams iterate faster on real product problems instead of fighting orchestration glue code. You can see this ethos in the rise of terminal-native tools like Aider, Claude Code, and Warp, which make AI pair programming productive without layers of framework ceremony. The best tech stacks privilege simplicity and maintainability over breadth of tools.

Light guardrails are your recipe card, not a second chef, or a set of expensive, hard-to-maintain tools. Use structured output with JSON Schema or Pydantic models to get reliable shapes from conversational prompting. Keep rules situational. Engineers in the trenches are documenting approaches that lean on small, explicit constraints rather than sprawling instruction documents that models won't consistently honor. Think of it as a plating ring, not a sous-vide rig for toast.

Why This Matters to Your People

The human cost of over-orchestration is subtle and corrosive. You get more onboarding time, more brittle assumptions, and a constant hum of "how does this thing actually work?" The pathologies are familiar: dashboards nobody trusts, playbooks no one reads, and incident write-ups that end with "framework edge case." Engineers who wanted to build product now spend their mornings deciphering an agent graph. That's culture drift.

In other words, agent frameworks solve a class of problems. But adopting them too early is like installing a salamander broiler to toast your bread: impressive, expensive, and unnecessary.

What to Standardize Tomorrow

The tool roll: Pick two or three CLI-first interfaces (e.g., Claude Code or Cursor) and make them the default workflow. Document a 10-minute quickstart. Show how CLI-first, minimal stacks drive speed and reduce overhead.

The house prompt: Standardize a short, single-page prompting style guide and a few tested templates. No 20-page rule tomes. Think index card, not encyclopedia.

The guardrail: Enforce structured output, plus a tiny set of situational rules for safety and correctness. Lightweight, structured guardrails deliver reliability without orchestration bloat.

The escalation: Create one "when to reach for agents/graphs" checklist. Require a written justification tied to product complexity and expected ROI. Frameworks add power and overhead—use them when the problem demands it, not before.

Jiro Dreams of Sushi is not a movie about fish. It's about the compounding returns of mastering a small number of moves. AI development is heading the same way. Your team doesn't need more stations; it needs a sharper knife and the discipline to put it in the same place, every day.

Build your AI mise en place: small, robust CLI tools, conversational prompting, and light guardrails. Stop shifting the complexity tax onto your team. The fastest kitchen in town is the one that knows where everything goes.