Agentic Coding in the Real World: Spec Loops, Claude Code, and The MCP Pattern

I spent this week elbows-deep in agentic coding—where LLMs do more than autocomplete a line; they plan, act, verify, and self-correct in a loop. The difference is night and day compared to last year's prompt engineering party tricks. With tools like Claude Code, we're watching AI not just write code but reason through specs, run local eval suites, and call out to tool servers with standard MCP (Modular Command Protocol) interfaces.

Why Spec-Driven Prompts Beat Ad Hoc Chat

The big leap is spec-driven prompting—feeding the model structured requirements (YAML, Protobuf, or plain English, but disciplined), not hand-wavy chat. This matters: you get fewer hallucinated APIs, more robust test coverage, and plausible refactoring. If you’re still priming with “act like an expert Python dev,” you’re missing the point. Spec-driven is verifiable and lets the LLM work incrementally, not in a single risky leap.

Eval-Driven Loops: Real Feedback, Not Vibes

Eval-driven dev is the secret sauce. Run the code, parse the results, and let the LLM see the outcomes. It’s not just TDD—it's continuous self-audit. My day: Claude Code reads my YAML spec, writes the first pass, hits a mock API via MCP, runs tests, gets stack traces, and revises. I only jump in for edge cases. This cuts shallow bugs and forces deeper learning into the loop.

AI Pair Programming—But Actually Useful

Forget the autocomplete hype. With agentic loops, the LLM is a junior who reads specs, debugs, and learns. It's slow for greenfield but unbeatable for tedious migrations and refactoring. Yes, there’s overhead in writing specs, but it’s a trade worth making if you care about correctness and speed both.