How to choose the right MuxAgent workflow config

Most teams ask the wrong question when they first look at MuxAgent workflow configs.

They ask, “Which one is fastest?”

The better question is, “Where do we want uncertainty to be resolved?”

That is what the config actually controls. A MuxAgent config is not a theme. It is a workflow graph: the sequence of nodes the agent follows, the edges between those nodes, and the points where humans may or may not need to step in.

The built-in configs are deliberately opinionated:

• default
• autonomous
• plan-only
• yolo

Each one assumes a different answer to a few operational questions:

• Should a human approve the approach before code is changed?
• Is the task ambiguous enough that planning deserves its own pass?
• If verification fails, should the workflow loop back automatically?
• Are we running one bounded pass, or an autonomous multi-wave effort?

If you choose the config based only on speed, you usually get the wrong result. You either add too much ceremony to easy tasks or remove the exact checkpoint you needed on the tasks that can actually hurt you.

First, separate workflow config from runtime choice

Before comparing the configs, keep one boundary clear.

In MuxAgent, workflow config and runtime selection are different things. That boundary is central to how MuxAgent works — if you want to understand why it matters so much, read why Codex and Claude Code need a workflow layer.

The workflow config decides the graph and the product intent. It answers questions like:

• do we stop for approval,
• do we stop after planning,
• do we run in waves,
• and which transitions are allowed after review rejection or verification failure?

Runtime selection decides which coding runtime executes the agent nodes, for example Codex or Claude Code.

That separation matters because people often try to smuggle process decisions into runtime preferences. A stronger model does not automatically mean you want less approval. A cheaper model does not automatically mean you want a plan-only workflow. Those are different axes.

Choose the workflow for the job. Choose the runtime for the execution.

Start with four decision filters

You can usually pick the right config by answering four questions in order.

1. How expensive is a wrong implementation?

If the cost of a bad code change is high, keep an approval checkpoint. If a wrong implementation is easy to throw away, you can remove more friction.

2. Is the task already well framed?

If the real work is still understanding the problem, do not jump straight to implementation. Use a workflow that lets planning stand on its own. The quality of task framing matters more than most people expect — see how to structure AI coding tasks that agents can finish for a deeper look at what a strong task brief includes.

3. How much human attention is actually available?

Some teams want explicit sign-off. Others care more about keeping the agent moving as long as review and verification remain strong. Pick the graph that matches the operating reality, not the process you wish you had.

4. Are you doing one pass or autonomous waves?

Some tasks can finish in one bounded loop. Others need a system that can evaluate progress, lock the next wave, and continue without pretending the whole request should be solved in a single attempt.

Once those answers are clear, the built-in configs become much easier to choose.

plan-only: when the highest-value output is a reviewed plan

The plan-only workflow runs:

• plan
• review
• done

This is the right choice when code should not be touched yet.

That sounds conservative, but it is often the fastest path for the kinds of tasks people otherwise botch with premature implementation:

• a vague feature request that still needs scoping,
• a risky migration where you want sequence and rollback logic first,
• a bug report that might be multiple bugs,
• a refactor proposal that needs dependencies surfaced before editing starts,
• or a task handoff where the next person mostly needs a strong implementation plan.

The advantage of plan-only is not that it avoids work. It avoids the wrong work.

A reviewed plan forces the agent to externalize assumptions early. It also gives humans a smaller, cleaner artifact to approve or revise. If the task is underspecified, this config prevents the common failure mode where the agent fills in too many gaps on its own and creates code that technically compiles but solves the wrong problem.

Use plan-only when the question is still “what exactly should happen?” rather than “can the agent start editing now?”

default: when you want human sign-off before code changes land

The default workflow runs:

• plan
• review
• approve
• implement
• verify

This is the safest general-purpose config, and for most teams it should be the default in the literal sense.

It is the right choice when:

• the task changes behavior other people rely on,
• the repo has real blast radius,
• the agent may touch multiple files or subsystems,
• you want a human to explicitly approve the approach before implementation,
• or you are still building trust in how your team uses agents.

The important thing about default is that it keeps the approval checkpoint after the plan has already been reviewed. That means the human is not being asked to bless a raw idea. They are being asked to approve an approach that already survived one quality pass. For a longer argument about why that checkpoint matters, see why approval checkpoints are a feature, not a slowdown.

That is a strong operating model for serious code work because it matches how responsible teams behave anyway:

• think first,
• review the thinking,
• approve the direction,
• then let execution proceed.

If you are unsure which config to start with, start here.

It adds some friction, but it adds the right kind of friction. You are not waiting for the sake of waiting. You are putting the pause at the last sensible moment before edits become real.

autonomous: when you trust the task more than you need the checkpoint

The autonomous workflow runs:

• plan
• review
• implement
• verify

Compared with default, it removes the explicit human approval stop before implementation. That makes it a good fit when you still want structure and recovery, but you do not want to babysit every bounded code change.

Use autonomous when:

• the task is well scoped,
• the change is reversible,
• verification is meaningful,
• the repo is familiar,
• and the cost of a bad first attempt is low enough that you would rather keep momentum than wait for approval.

This is a strong choice for repeated internal tasks such as:

• fixing known classes of bugs,
• making mechanical cleanup changes with clear checks,
• tightening docs and examples,
• or iterating on internal tooling where rapid feedback matters more than formal sign-off.

What autonomous does not mean is “turn your brain off.” It still keeps planning, review, and verification because those phases solve real problems. The config is faster than default, but it is still structured enough to catch a lot of avoidable failures.

Think of it as “high trust, but not reckless.”

yolo: when the task needs autonomous waves, not just fewer pauses

The yolo workflow is different in kind, not just degree.

It runs fully autonomously, disables approval and clarification, and adds an evaluation step after verification so work can continue over multiple waves.

This is not the config you choose because you are impatient. It is the config you choose because the task itself is wave-shaped.

Use yolo when:

• the job can be decomposed into bounded waves,
• the contract for each wave can be reviewed objectively,
• clarification is intentionally disabled,
• and you want the system to keep planning the next slice of work until the task is actually complete.

That makes yolo appropriate for things like:

• large but decomposable maintenance work,
• content or docs programs that need several publishable batches,
• broad cleanup efforts where each wave can define its own done condition,
• or autonomous backlogs where the task is too large for one pass but still safe to split into auditable increments.

yolo is powerful because it encodes a truth many teams ignore: some work should not be forced into one implementation burst. It should be handled as a sequence of reviewed waves.

That power also makes it the easiest config to misuse.

If the wave contract is weak, yolo turns ambiguity into momentum, which is the wrong trade. If the verification criteria are fuzzy, the workflow can keep moving without actually proving value. The config needs strong planning discipline to be worth it.

Choose yolo only when you are confident the task can be governed by explicit wave contracts instead of live human steering.

Common selection mistakes

Most bad config choices come from a handful of predictable mistakes.

Mistake 1: using autonomous for ambiguous work

If the task is still fuzzy, removing approval does not make it faster. It just lets the agent commit to assumptions earlier. When the ambiguity is the main problem, plan-only or default is usually better.

Mistake 2: using default for disposable scratch work

If you are experimenting on a local branch and the cost of being wrong is tiny, the approval checkpoint may add more friction than value. That is where autonomous earns its keep.

Mistake 3: using yolo as a synonym for “go faster”

yolo is not simply the fast config. It is the multi-wave autonomous config. If the task does not actually need wave planning and evaluation, you are likely picking the wrong tool.

Mistake 4: using plan-only when you really want implementation but are afraid to say so

Sometimes teams choose plan-only because they do not trust the task enough for code changes, but they also never convert the plan into a follow-on execution flow. If you regularly end up implementing the reviewed plan immediately anyway, you may really want default.

A practical way to standardize this on a team

Do not make people choose from scratch every time.

Instead, define a short team rubric:

• plan-only for vague or high-ambiguity requests,
• default for most production-facing code changes,
• autonomous for reversible, well-scoped internal work,
• yolo for bounded multi-wave tasks with strong verification.

That kind of shared rule does two useful things.

First, it turns config choice into a repeatable operating decision rather than a personal preference.

Second, it makes exceptions visible. If someone wants autonomous for a task the team would normally route through default, that becomes a concrete discussion about risk and trust instead of vague process discomfort.

Use the built-ins first, customize second

MuxAgent stores the included workflow configs as task config bundles under ~/.muxagent/taskconfigs, and you can clone them to change the graph, prompts, runtime, iteration limits, or clarification settings.

That means you do not need to invent your own graph on day one.

Start with the built-ins long enough to learn where your actual friction is. Then customize with evidence.

There is also a useful safety detail in the built-in handling: if you already have a user config named plan-only, autonomous, or yolo, MuxAgent preserves it and installs the built-in under a fallback alias such as builtin-plan-only. Existing bundle files are not overwritten.

That keeps experimentation sane. You can adapt the defaults without losing the reference implementation they came from.

Choose the config by the checkpoint you cannot afford to skip

If you want one rule to remember, use this one:

Pick the config based on the checkpoint you cannot afford to skip.

If you cannot afford to skip a reviewed plan, use plan-only or stronger.

If you cannot afford to skip human sign-off before code changes, use default.

If you can afford to skip approval but not structure, use autonomous.

If you need autonomous waves with explicit evaluation between them, use yolo.

That framing is more reliable than “which one is fastest?” because it matches the real cost structure of the work.

The same config choices apply when running agent tasks in automated pipelines — see integrating MuxAgent into your CI/CD pipeline for how workflow configs map to CI use cases.

Make one deliberate choice this week

The best next step is simple:

1. Pick one recurring task category on your team.
2. Decide which built-in config should be the default for that category.
3. Write the rule down in one sentence.
4. Run a few tasks through it before you customize anything.

If you do that well, config choice stops feeling like hidden tooling trivia and starts becoming part of how your team reasons about agent oversight.

That is the point.

MuxAgent configs are useful because they turn trust, reversibility, and supervision into something operationally explicit. Once you see them that way, the right choice is usually much clearer.