Observation #4: LLMs Get Purpose, Not Perfect Execution—Architecture and Orchestration Are the Core Capability
A good general LLM is better at nailing the purpose of your prompt than at doing the job perfectly. Most detailed work still needs skills—even with MCP—to finish. And the way you architect and orchestrate that work—trading off speed, cost, and performance—is the core capability for a company or team.
- Purpose over perfection. The model gets what you’re after: intent, scope, “why.” It’s less reliable at every detail, edge case, and format. Understanding the ask is its strength; flawless execution often isn’t. Closing the gap to “done and correct” takes human skill or well‑scoped tools; the LLM orients, you (or your stack) complete.
- Orchestration is the moat. Good AI architecture and agent orchestration—routing, when to use which agent or tool, batching, caching, fallbacks—let you trade off speed, cost, and performance deliberately. Anyone can call an API. Designing how agents and tools work together, and where to spend latency vs. cost vs. quality, is the skill that compounds. That’s what separates teams that “use AI” from teams that ship AI that works.
Use the LLM to align on purpose; plan on skills and MCP to finish the detailed work. Invest in architecture and orchestration—that’s where the edge lives.