Capstone 01 — Terminal-Native Coding Agent
By 2026 the shape of a coding agent is settled. A TUI harness, a stateful plan, a sandboxed tool surface, a loop that plans, acts, observes, recovers. Claude Code, Cursor 3, and OpenCode all look the same from 50 feet. This capstone asks you to build one end to end — CLI in, pull request out — and measure it against mini-swe-agent and Live-SWE-agent on SWE-bench Pro. You will learn why the hard part is not the model call but the tool loop, the sandbox, and the cost ceiling on a 50-turn run. Phases exercised: P0 · P5 · P7 · P10 · P11 · P13 · P14 · P15 · P17 · P18 Coding agents became the dominant AI application category in 2026. Claude Code (Anthropic), Cursor 3 with Composer 2 and Agent Tabs (Cursor), Amp (Sourcegraph), OpenCode (112k stars), Factory Droids, and Google Jules all ship variations of the same architecture: a terminal harness, a permissioned tool surface, a sandbox, and a plan-act-observe loop built around a frontier model. The frontier is narrow — Live-SWE-agent reached 79.2% on SWE-bench Verified with Opus 4.5 — but the engineering craft is wide. Most failure modes are not model mistakes. They are tool-loop instability, context poisoning, runaway token cost, and destructive filesystem operations. You cannot reason about these agents from the…
Capstone 01 — Terminal-Native Coding Agent: By 2026 the shape of a coding agent is settled. A TUI harness, a stateful plan, a sandboxed tool surface, a loop…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.