A close reading of thirteen months of real, day-to-day coding-agent sessions, turned into practical guidance for both the human and the agent.
| File | What it is | Audience |
|---|---|---|
index.html |
The write-up / blog post. The story, the data, the six-move loop, the autonomy question, with diagrams. Start here. | Anyone |
human-playbook.html |
A one-page field guide: the loop, three checklists, a phrase bank, an "earn more autonomy" section, and anti-patterns. Built to print. | The human |
agent-instructions.md |
A tool-agnostic instruction set to drop into any agent's standing-instructions file (AGENTS.md, a rules file, a system prompt). |
The agent |
Open the two .html files in any browser. They're self-contained (no internet, no
assets, no tracking) and safe to share.
- 211 locally-stored agent sessions (154 with real back-and-forth), 1,436 human turns in total.
- Spanning April 2025 – June 2026 (~13 months) across 19 codebases — a mobile + embedded-device app and SDK, a regulated reporting tool, a data/metrics pipeline, a server framework, even coursework and sessions reading the agent's own source.
- Plus 17 deep 2026 "agent-mode" transcripts capturing ~3,400 tool executions, used for the tool-usage and autonomy-depth figures.
- Combined two storage sources so the timeline is complete: exported session logs (
.jsonl, the 2026 sessions) and the live workspace databases (.jsonsessions, reaching back into 2025). They were deduplicated by session id — there was no overlap, so the union is the full record. - Parsed every session, reconstructing each conversation's turns (handling both whole-session snapshots and the streamed, incremental save format).
- Profiled the corpus quantitatively — models, modes, turn counts, tool usage, the 2025→2026 shift in agent depth, and the share of turns matching a dozen behavioral patterns.
- Read the conversations — the deepest sessions in full, plus a categorized harvest of exemplar quotes across all turns, spanning every domain and both years.
- Synthesized the recurring behaviors into a six-move loop, a human playbook, a mirror set of agent principles, and an evidence-based answer to the autonomy question.
- Verified every load-bearing quote against the source transcripts and scrubbed all product names, identifiers, paths, and people for public sharing.
- The stance is constant; the leash grew. Agent depth rose from ~3.3 to ~9.5 tool-actions per human turn between 2025 and 2026, yet the human's grounding-and-verifying style is identical in the first session and the last.
- Autonomy is earned through verifiability. The way to let the agent do more on its own isn't to instruct it to be bolder — it's to make more of the work cheap to check (tests, golden files, green builds, standing rules) and keep a hard stop at anything irreversible.
Treat the agent as a fast, capable partner that has no idea what actually matters. Give it the missing context generously; verify its claims relentlessly. Hand it the fast part of the loop — keep the judgment for yourself, and make the judgment cheaper to check over time so the fast part can grow.
All examples are anonymized; quotes are lightly edited for typos only. Independent of any other analysis in adjacent folders.