Published 2026-07-25
One agent, five jobs
Most agent setups still ask one model to research, architect, build, test, and validate in a single pass. That works for a small tweak. On a greenfield app or a multi-part initiative, the failure mode looks familiar from human orgs: unclear scope, overlapping ownership, interfaces that drift silently, and coordination buried in one long chat.
When that happens, teams often blame the model. Sometimes the model is fine. The operating model is missing. Claude Code and Cursor already run agents. What they do not give you out of the box is plan-before-build hierarchy, single-job roles, a visible board, and frozen seams between parallel teams.
What an operating model adds
Skailr nests work the way a real org does: portfolio for many concurrent bets, program for one large initiative with workstreams and frozen contracts, and workstream for one cohesive feature. Agents specialize. Teams load just in time from a thin registry. Coordination happens on an append-only markdown board, not private side chats. Cross-team interfaces freeze after plan approval so parallel builds do not invent incompatible seams.
The chooser is simple. A hot fix stays a patch. One feature ships through a gated or YOLO feature path. A whole app or unclear multi-part scope goes through discover, plan, and build at the program tier—or a one-shot program YOLO when you accept assumption-driven decisions instead of sign-offs. Plain chat routes the same way when you do not reach for a slash command.
Where skailr-agents fits
skailr-agents is an installable pack for Claude Code and Cursor, not a LangGraph-style runtime. You clone it into a repo, commit the agents and commands so teammates share the same roles, and run discovery or delivery from slash commands—or let intake route ordinary chat. Optional script gates check ownership, ledgers, and contracts when you want mechanical enforcement.
If large agent work keeps collapsing into one overloaded thread, start with the public MIT repo and install the operating model once. Then compare notes with how we approach multi-agent systems research in the lab.
Related: skailr.io and how we approach AI and technology research.