How AI agents are actually used
Enough theory — this chapter is what real agents do, how they're built under the hood, and what happened when big companies shipped them. The headline: over 56% of developers now use AI coding tools daily. But the interesting part is that they're all the same handful of primitives from the last ten chapters, rearranged.
1 · Coding agents — win by self-verifying
Coding is where agents got real first. Claude Code lives in the terminal with file/shell/git access, navigates by grepping and reading files, and — crucially — runs the tests to verify its own work before committing. (Strikingly, only ~1.6% of its codebase is AI decision logic; ~98.4% is deterministic infrastructure.) Cursor builds a semantic index of the repo; Devin works autonomously in a sandbox and opens PRs (one example: 47 files migrated in ~20 min, tests passing). The superpower isn't writing code — it's checking it.
2 · Computer-use agents — how they "see and click"
Some agents operate software that has no API — they drive the actual screen. The loop is pure perception-action: get a screenshot, use vision to find the UI element, return pixel coordinates for a click, execute it in a sandboxed VM, get a fresh screenshot, repeat. Watch it run:
3 · The case studies, with real numbers
It's not a pure success story. By May 2025 Klarna walked back the AI-first approach to a hybrid human+AI model — the AI was strong on routine volume but weak on the long tail (fraud, disputes), hurting CSAT. Average-case metrics looked great while long-tail failures quietly damaged trust. Design for the long tail, not the demo.
4 · The pattern behind the patterns
Notice how few primitives are actually in play — the domain changes the tools, data, and risk controls, but the skeleton barely changes:
Next: a field map of 100+ real deployments across ten industries (Ch12), then the operations discipline — eval, observability, security, cost — that separates a demo from a product (Ch13).