Agentic Coding: The Real Daily Grind—Specs, Evals, and Sane Loops

Let’s get honest: the golden promise of AI coding agents is mostly measured in the mess of real workflows, not glossy demos. Today, actual engineers are living inside a thicket of agentic coding loops. We’re not just tossing a prompt at Claude Code or Gemini and hoping for magic—every serious shop I know is running tight plan→act→verify→fix iteration loops, enforced by MCP tool servers and spec-driven prompts.

Why the obsession with spec-driven prompting? Because AI is still a pro at hallucinating. If you want that agent to refactor your backend service or add a GraphQL endpoint, you’d better spell out the spec and the side effects you care about. Hardcore teams are templating these specs and feeding them into prompts, then running eval-driven development where every agent move is checked by custom test suites and auto-linters. Your agent doesn’t pass eval? Back to the plan.

It’s not just about code completion. Teams are chaining loops: one agent plans, another writes, a third verifies, and a fourth fixes. MCP tool servers (basically agent traffic control with tight permissioning) make sure one runaway agent doesn’t nuke your repo. It’s a wild west, but the best folks are writing YAML or TypeScript specs that serve as both the contract and the prompt skeleton—forcing AI into the right shape.

Why does all this matter? Because the old way—"let’s see what the LLM spits out"—is deadweight in production. Reliable AI coding is about grind: looping, verifying, and turning vague asks into concrete specs the model can’t ignore. If you want to ship with AI, your job is to build the rails the agent rides on, not just marvel at its speed.