A single AI coding agent can write code. But give it a big task, and it drifts. It skips tests. It forgets the plan. It says "done" when it is not. This is not a model problem. It is a workflow problem.
Two open-source frameworks on GitHub found the same answer. Split the work across multiple agents. Make them check each other. Force a loop: plan, implement, review, fix. The bigger one has 118,624 stars.
Why splitting the work stops the drift
When one agent does everything, its context grows with every step. After 30 minutes, it loses track of the original plan. After an hour, it invents new requirements that nobody asked for. The failure patterns are predictable. The agent tries to build everything at once and the structure falls apart. It declares "done" halfway through. It marks features complete without writing tests. It leaves the environment in a broken state at the end of a session.
Multi-agent orchestration fixes this by keeping each step small. A planning agent breaks the task into pieces. An implementation agent handles one piece at a time. A review agent checks if the code matches the plan. If it does not, the work goes back to implementation. Each agent starts with a clean, focused context. The drift stops. Superpowers makes this explicit. It requires plans to be "clear enough for a junior engineer with enthusiasm but no sense, no judgment, no project context, and reluctance to test."
This is not a new idea. It is the same structure as CI/CD pipelines in software development. The difference is that agents now fill the roles that humans used to fill. The handoff between stages is automatic.
Discipline or flexibility. Two frameworks, two designs.
Install Superpowers (118,624 stars) and the agent changes how it works. When it detects you want to build something, it stops. It does not start writing code. It asks what you actually want. After it pulls out the spec from the conversation, it shows the design in short chunks you can read and understand. You approve the design. The agent writes a plan. You say "execute" and sub-agent-driven development starts.
Each task in the plan is 2-5 minutes long. Every task has exact file paths, full code, and verification steps. After each sub-agent finishes a task, a two-stage review runs. Stage one checks if the code matches the spec. Stage two checks code quality. TDD is RED-GREEN-REFACTOR. Code written before a test exists gets deleted. 14 built-in skills enforce this 7-step workflow as "mandatory, not a suggestion." It works with Claude Code, Cursor, Codex, OpenCode, and Gemini CLI.
oh-my-claudecode (13,996 stars) takes the team approach. Its Team mode runs a staged pipeline: team-plan, team-prd, team-exec, team-verify, team-fix. Since v4.4.0, it can run Claude, Codex, and Gemini at the same time using tmux workers. You can send code reviews to Codex, UI tasks to Gemini, and integration work to Claude in one command:
omc team 2:codex "Review auth module for security issues"
omc team 2:gemini "Redesign UI components for accessibility"
omc team 1:claude "Implement payment flow"
It has 32 specialized agents and routes tasks to Haiku or Opus based on difficulty. You can also run a cross-model PR review in one line:
/ccg Review this PR — architecture (Codex) and UI components (Gemini)
The standout feature is automatic skill learning. When you debug a problem, OMC extracts the fix as a skill file. For example, if you fix an aiohttp proxy crash, it saves this:
# .omc/skills/fix-proxy-crash.md
---
name: Fix Proxy Crash
triggers: ["proxy", "aiohttp", "disconnected"]
source: extracted
---
Wrap the handler at server.py:42 with try/except ClientDisconnectedError...
Next time the same error shows up, this skill gets injected automatically. No manual call needed. When requirements are unclear, /deep-interview "I want to build a task management app" starts Socratic questioning. It finds hidden assumptions and measures clarity on weighted dimensions before any code is written.
Superpowers enforces discipline to ensure quality. oh-my-claudecode offers multi-provider flexibility to widen your options. Both use the same core loop: plan, implement, review, fix.
The model is not the bottleneck. The workflow is.
Harness engineering showed that one agent needs a good environment to work well over time. Multi-agent orchestration goes further. It splits the work across agents and makes them inspect each other. The quality comes from the structure, not from any single model.
Both frameworks arrived at the same core loop: plan, implement, review, fix. The next time your AI agent produces bad code, try adding structure before switching models.
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