In 2026, understanding the agentic loop is as fundamental as understanding HTTP was in 2005.
Here's the loop, stated plainly:
Goal → Plan → Execute → Evaluate → Adjust → Repeat
It doesn't stop after one response. It runs until the goal is met, or until it hits something it can't resolve and surfaces it to you.
Claude Code's own documentation puts it cleanly: "an agentic loop that plans and executes actions toward accomplishing a goal and can call tools, evaluate results, and continue until the task is done."
But here's what most engineers miss:
Each phase of the loop has its own failure mode.
Plan phase: This is where consequential errors originate. A bad plan produces a coherent sequence of well-executed steps that solve the wrong problem. Review the plan before execution. Always.
Execute phase: The agent takes real action in your environment — writes files, runs commands, commits code. This is where reversibility design matters. Git commits at checkpoints. Staging environments. Explicit rollback paths.
Evaluate phase: The agent assesses its own output. It's better than nothing. But it's limited by the same training distribution that shaped the output. It catches obvious errors. It misses subtle ones.
Adjust phase: A well-designed agent fails loudly. Silence from an agent that should be reporting progress is a signal worth investigating immediately.
One more thing you need to know:
The model's knowledge has a cutoff date. Everything it "knows" was baked into its weights during training. That's where RAG comes in; Retrieval-Augmented Generation. Instead of relying on training-data memory, the system retrieves real, current documents at query time and grounds its output in them.
It's the difference between asking a colleague who read your docs six months ago and one who just pulled them up before answering.
Claude Opus 4.6's context window: 1 million tokens. Your entire production codebase, in context, simultaneously. That's what makes repository-level reasoning possible in 2026.
Tomorrow: the part of the book that made engineers uncomfortable when they read it in beta. The real data on what AI is doing to engineering employment.
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