DevOps discipline for AI coding agents: shape the work, track it as a graph, and get each change judged by a context that didn't write it.
README
Coding agents don't do their own bookkeeping. AgentOps does. It sits on top of the agent you already use (Claude Code, Codex, Cursor, OpenCode) and adds the parts an engineering team would notice missing: a record of what was tried, gates between phases, and a corpus of learnings that survives the next session. Plain markdown in .agents/ next to your code; mix any model per phase.
<sub><code>/discovery</code> → bead graph · <code>/crank</code> → sub-agents in waves · <code>/validate --mixed</code> → real Claude + Codex verdict. Live sessions. <a href="docs/assets/hero.mp4">MP4</a></sub>
</div>AgentOps breaks intent into bounded slices, gives each a failing test and a write scope, and makes every phase boundary a gate that records evidence. The agent starts loaded with prior decisions and learnings instead of cold:
> /council --mixed validate this PR
[council] evidence sealed → 6 judges across Claude Code + Codex CLI
[claude/judge-1] WARN rate limiting missing on /login
[codex/judge-1] WARN token bucket lacks jitter under burst
[claude/judge-2] PASS redis integration follows pattern
Consensus: WARN, fix /login limit + refill jitter before shipping
Recorded → .agents/council/<run-id>/verdict.md
Four layers that compound, all in local .agents/ you can grep, diff, and review (no telemetry, no hosted control plane):
| Layer | The problem | What AgentOps adds | |---|---|---| | Bookkeeping | agents forget