analysis_claude_code/s16_workflow_runtime

s16: Workflow Runtime — Put the Recipe in Code

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s01 → ... → s14 → s15 → s16 → s17

"Chatting turn-by-turn is like texting the chef every ten seconds. A workflow is a recipe the kitchen can follow."

Harness layer: Orchestration — a multi-agent script above the single-agent loop.

Trust the model; engineer the harness. Workflows are that idea at the orchestration layer.

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Picture cooking with a friend over text. “Chop the onions.” Wait. “Done?” Then the pan, then the salt. One dish survives that rhythm. A feast with twenty plates does not: you forget steps, repeat yourself, and if the phone dies you start over cold.

That is what it feels like when the model is both chef and clipboard — planning and doing inside the same chat. A workflow is the written recipe. The kitchen (the runtime) follows it. Helpers (subagents) taste and judge. The bowls of half-finished work sit on the counter, not in the group thread.

Why bother with another harness?

The default Claude Code harness is already good at coding-shaped work: change something, run it, read the error, try again. One loop, one mind, a surprising amount of craft.

But some jobs are a different shape — deep research, security sweeps, agent teams, a review that fans out across a whole change set. For those, people have long built a second harness on top. You can still hand-write that layer in an SDK. Or — and this is the lively part — Claude can draft a harness for this task, run it, and keep the good ones.

Same course motto, one floor up: trust the model inside each step; decide the shape of the steps yourself.

What goes wrong in a long chat

From s01 through s15, plan and action share one context window. That is wonderful when the next move depends on what you just found.

It frays when the job is long, massively parallel, rigidly structured, or needs a skeptical second opinion. Watch a long chat carefully and you will see familiar habits. It gets tired and declares victory after thirty-five of fifty review items. Asked to check its own homework, it grades kindly — the fox scoring the henhouse. And across many turns and compressions, the quiet constraint (“don’t touch X”) fades until nobody remembers why it was there.

Claude Code’s designers call these agentic laziness, self-preferential bias, and goal drift. The names matter less than the feeling: the same window that does the work is also trying to remember the plan. Chat history is a soft place to keep parallelism, stable result shapes, and a way to resume after a crash. Review-many-files, research-then-verify, migrate-N-modules — those jobs already know their shape. Soft memory is not enough.

The idea, once it clicks

What if the plan lived in code?

Helpers still think — each at a clean desk, with one focused job. The script owns the loops, the fan-out, the merge. Intermediate results live in variables and a journal, not in the conversation. Laziness has a harder time stopping the fleet early. Self-checking bias meets a second helper who was not the author. Drift loses its grip because the topology is not rewritten every turn by a tired narrator.

In one line: workflows move orchestration from intelligence to structure. The model still judges inside each agent(); the script owns the map.

One Workflow tool call starts that run. Progress ticks while it works; one tool result comes back with launch info, the outcome, and task state.

Two doors into the same kitchen

Claude Code is straightforward about how you enter.

Sometimes the model writes a JavaScript orchestration script for this task and hands it over as script (or later edits scriptPath). That is the dynamic door — a harness tailored while the problem is still warm.

Sometimes a good script has already been saved under something like .claude/workflows/. You call it by name and args. That is the saved door — the reusable residue of a run that earned its keep.

There is a cousin outside this lesson too: static harnesses you write ahead of time with the Agent SDK or claude -p. Those have to survive every edge case, so they stay generic. Dynamic ones are cut for this cloth; save them when the fit is right.

This chapter is a Python teaching runtime. Same ideas, every line readable. Our demo registers one saved workflow by name; the concepts map one-to-one onto Claude Code’s script world. We will not pretend “the model cannot submit executable code” — that was never true of Claude Code. We simply do not embed a full JavaScript interpreter here.

# Teaching adapter: the saved door (name + args).
# Claude Code also accepts script / scriptPath / resumeFromRunId.
WORKFLOW_TOOL = {
    "name": "Workflow",
    "input_schema": {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "args": {"type": "object"},
            "resume_from_run_id": {"type": "string"},
            "resumeFromRunId": {"type": "string"},
        },
        "required": ["name"],
    },
}

A few kitchen verbs

Imagine a school bake sale. Every table needs mix → bake → box. Helpers taste; the recipe decides order.

agent(...) is asking one helper to do one job. pipeline(items, *stages) is the default: each cake walks the stages on its own, so one can be boxing while another is still mixing. parallel(...) is the barrier — wait until every tray is back — and you only want that when the next step truly needs all of them together, like writing the scorecard after tasting the whole tray.

Around those sit quieter verbs: phase to announce where you are on the board, log for a short shout, workflow to nest one smaller recipe, args for the ingredients list, budget for how many oven-minutes (tokens) you may burn.

# Each review dimension walks audit → verify on its own.
results = await ctx.pipeline(DIMENSIONS, audit, verify)
confirmed = [f for r in results if r for f in r["confirmed"]]

Once you can write the recipe

The verbs above are flour and heat. What people keep reinventing are a handful of shapes — common patterns for dynamic agentic workflows. Think of them as a toolbox, not a mandatory menu. Reach for one when its pain shows up; leave the rest on the pegboard.

Six shapes people keep reinventing. The script owns the topology; agent / parallel / pipeline / phase / journal are how each shape is spoken in this lesson.

Classify-And-Act. Pain: one generic helper is mediocre at everything. Shape: a classifier looks at the task, then routes to specialist A, B, or C. In this lesson that is usually one agent({schema}) that returns a label, then an if/match in the script that calls the right follow-up agent (or a nested workflow). Skip it when every item truly needs the same treatment — routing is just ceremony then.

Fanout-And-Synthesize. Pain: fifty files will not fit one tired context, and they contaminate each other if they try. Shape: split the work, run many agents, wait at a barrier, merge. Map it with pipeline when each item has its own stages, or parallel when the next step needs every result together; put the merge in ordinary Python after the gather. Skip it for three related files a single pass can hold.

Adversarial Verification. Pain: the fox grades the henhouse. Shape: a worker produces; independent verifiers try to refute or stress-test; only survivors remain. Map it with a produce agent, then parallel of verifier agents (ideally schema’d), then a filter. Phases help (“Review” then “Verify”). Skip it when the cost of a wrong answer is low — not every note needs a tribunal.

Generate-And-Filter. Pain: you need options, not the first idea that sounded clever. Shape: many generators spill ideas into a rubric + dedupe filter; best stay, rest go. Map it with parallel over generators, then script-side filter (or one judge agent with a schema). Journal/resume matter when generation is expensive. Skip it when the space of good answers is already tiny.

Tournament. Pain: absolute scores are mushy for taste and ranking (“how good is this name?”). Shape: pairwise judges, a bracket, a winner — comparative judgment beats lonely scoring. Map it with rounds of parallel judge agents over pairs, looping in the script until one remains. Skip it when a clear rubric already picks a winner in one pass.

Loop Until Done. Pain: you do not know how many passes the mine still holds. Shape: keep spawning while “new findings?” is yes; stop on dry rounds or a done condition. Map it with a while over agent/parallel, a schema’d stop check, and a hard budget so the loop cannot eat the house. Pair with journal resume when a long dig may pause. Skip it when the work has a known size — a fixed pipeline is simpler and safer.

After a few of those have a face, the toolbox fits in one glance:

Pattern	Primitive sketch	Reach for it when…
Classify-And-Act	agent → branch → agent	Items need different specialists
Fanout-And-Synthesize	pipeline / parallel → merge	Many clean desks, then one summary
Adversarial Verification	produce → parallel(verify) → filter	Wrong answers are expensive
Generate-And-Filter	parallel(gens) → rubric filter	You need options, then taste
Tournament	pairwise judge agents in a bracket	Ranking / taste without a sharp scale
Loop Until Done	while + stop check + budget	Unknown amount of buried work

Compositions are normal. Deep research often stacks fanout → filter → verify → synthesize. Our sample is already a small chord of two notes.

Answers the next stage can hold

If a helper returns a poem, the next stage cannot zip findings to verdicts. Pass a schema. The runtime asks for JSON, checks it, and gives one retry. Fail again and that call errors — which brings us to how the fleet stays kind under failure.

out = await ctx.agent(
    f"Inspect this change for {dimension} issues:\n{changes}",
    schema=FINDINGS_SCHEMA,
    label=f"audit:{dimension}",
)

Chat with you can stay prose. A pipeline needs sockets that fit.

When one tray burns

The fleet should not stop because one helper had a bad oven.

In parallel, a failing thunk becomes null / None in that slot; the gather itself does not reject. In pipeline, a failing stage drops that item to null and skips its later stages; the other items keep walking. Filter with care before you merge — if r, or .filter(Boolean) in the JS world.

verdicts = await ctx.parallel([...])  # some slots may be None
confirmed = [
    f for f, v in zip(findings, verdicts)
    if v and v.get("isReal")
]

A notebook you can reopen

Every run gets a runId. As each agent() finishes, a line lands in a journal on disk — a notebook ordered by the moment you called the helper, not by who wandered back from the oven first.

Resume (resume_from_run_id / resumeFromRunId) runs the script from the top again, but kindly. Call by call, in order, it matches the next journal line. The longest unchanged prefix replays from cache. At the first changed or unfinished call, the prefix breaks — and everything after runs live, even if an old key still sits further down the notebook. No silent leaps over a break.

That is also why real JavaScript workflow runtimes ban Date.now(), Math.random(), and bare new Date(). Clocks and dice make prompts or call order wobble, and the notebook stops lining up. This Python demo does not fully sandbox that. Write deterministic scripts anyway.

journal:  [A ✓] [B ✓] [C ✓] [D ✓]
resume:   A hit → B hit → C changed → D runs live

Walking review-changes — a composition

The sample is not “one pattern.” It is Fanout-And-Synthesize with Adversarial Verification inside — and a light generate-and-filter at the end when only isReal findings survive.

correctness ── audit ── verify ──┐
security    ── audit ── verify ──┤── confirmed findings
performance ── audit ── verify ──┤
style       ── audit ── verify ──┘
         fanout                         synthesize
              └── each finding: skeptical verify ──┘

pipeline(DIMENSIONS, audit, verify) gives each dimension its own desk so correctness talk does not bleed into security. Inside verify, parallel of verifier agents is the adversarial chord. Ordinary list filtering is the synthesize step. Phases mark Review then Verify; the journal remembers every agent() so a pause does not redo the audits.

You can almost feel the three failure modes losing their favorite seats: the fleet cannot stop after two dimensions, the author is not the judge, and the topology does not drift mid-run.

async def sample_workflow(ctx, args):
    ctx.phase("Review")
    results = await ctx.pipeline(DIMENSIONS, audit, verify)
    confirmed = [f for r in results if r for f in r["confirmed"]]
    ctx.log(f"confirmed {len(confirmed)} real finding(s)")
    return {"confirmed": confirmed}

Hanging on s15 without replacing it

s15 is still the host loop. s16 only adds a tool named Workflow. You (or the model) ask for a saved name; the adapter finds the script and runs it.

In the real product, that run can sit in the background with notifications while the session stays responsive. Our teaching CLI keeps demo and resume in the foreground so you can watch phases and cache hits without squinting. Same ideas; we say so when we simplify.

The main loop does not become a workflow engine. It borrows one tool the way it borrows bash or task.

Turning the gem: who holds the plan?

Look at the neighbors and the same object shows a new face. The useful question is not “how many agents?” but who owns the topology, and where the half-finished bowls live.

Neighbor	Who holds the plan	Where intermediates live	Best for
s06 Subagent	Model, one-shot	Mostly discarded	One dirty subtask, isolated
s13 Agent Teams	Lead, turn by turn + mailbox	Shared tasks / messages	Long-running peers
s15 Integrated Harness	Model in one loop	Conversation messages[]	Cumulative coding agent
s16 Workflow	Script	Variables + journal	Structured fan-out and verify
s17 Goal Loop	Evaluator at stop time	Conversation as evidence	“Is the whole goal done?”

Cheaper paths still win often: a skill as a soft plan, a short multi-agent chat, a hand-written static orchestrator, or one larger model turn. Reach for a workflow when the structure must outlast a single context — not because a panel of reviewers sounds impressive.

And when to leave it on the shelf

Workflows spend tokens and coordination. Most ordinary coding does not need five reviewers.

Before you spin one up, ask whether the job truly wants more compute and a custom harness. If a normal s15 turn — or one honest s06 subagent — will do, stop there. Restraint is part of the thought: parallelism and specialization have to earn their keep.

Try it

python s16_workflow_runtime/code.py          # s15 host + Workflow (real API)
python s16_workflow_runtime/code.py demo     # fixed fixture; watch phases
python s16_workflow_runtime/code.py resume   # same runId; expect cache hits

Watch Review give way to Verify. Watch agents flip from done to cached on a full resume. At the end, a short confirmed list — and on a clean resume, agents=0 tokens=0, which is the notebook saying: nothing needed reheating.

Next

s16 is how a batch runs. s17 Goal Loop asks a different question at the door: should we stop, or take another turn? Pair them when a repeatable recipe also needs a hard “done.”