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 one floor up.

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Picture cooking with a friend over text. “Chop the onions.” Wait. “Done yet?” 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 one model is both chef and clipboard — planning and doing inside the same chat. A workflow is the written recipe. The kitchen (a small runtime) follows it. Helpers (subagents) taste and judge. Half-finished bowls sit on the counter — in variables and a journal — not in the group thread.

Why another harness at all?

The default Claude Code harness is already strong 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 want a custom harness on top — deep research, security sweeps, agent teams, a review that fans across a whole change set. You can hand-write that layer once in an SDK. Or — and this is the lively idea — Claude can draft a harness for this task, run it, and keep the good ones.

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

What a long chat quietly does wrong

From s01 through s15, plan and action share one context window. 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 meet the habits before you learn their names. 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. Across many turns and compressions, the quiet “don’t touch X” fades until nobody remembers why it was there.

Those are 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. Soft chat memory is a weak place to keep parallelism, stable result shapes, and resume.

The idea, once it clicks

What if the plan lived in code?

Helpers still think — each at a clean desk. The script owns loops, fan-out, and merge. Intermediate results live in variables and a journal, not in the conversation. Laziness struggles to stop 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.

Workflows move orchestration from intelligence to structure. The model still judges inside each agent(); the script owns the map.

  messages[] ──► Workflow(...) ──► tool_result { launched, result, task }
                      │
                      ▼
              ┌───────────────┐
              │  script owns  │
              │  the topology │
              └───────┬───────┘
                      │ agent / parallel / pipeline
                      ▼
                 variables + journal

One Workflow tool call starts that run. Progress ticks while it works; one tool result comes back when the recipe finishes.

Runtime overview diagram (optional)

Two doors — and a cousin outside

Claude Code is straightforward about how you enter.

Dynamic — the model writes a JavaScript orchestration script for this task (script, later scriptPath). A harness cut while the problem is still warm.

Saved — a good script already lives under something like .claude/workflows/. You call it by name + args. The reusable residue of a run that earned its keep.

Outside sits a cousin: static harnesses you write ahead with the Agent SDK or claude -p. Those must survive every edge case, so they stay generic. Dynamic ones are cut for this cloth; save them when the fit is right.

Same question, two harnesses. Left: fixed search→verify→summarize → a generic report. Right: read your billing code, branch, invite a devil’s advocate → a specific recommendation.

This chapter is a Python teaching runtime. Same ideas, every line readable. The demo registers one saved workflow by name; concepts map 1:1 to Claude Code’s script world. We will not pretend “the model cannot submit executable code” — that was never true of Claude Code. We simply skip embedding a JS interpreter.

# teaching sketch — saved door (not the full Claude Code schema)
Workflow({ "name": "review-changes", "args": { "changes": "..." } })

# Claude Code also accepts: script | scriptPath | resumeFromRunId

Three verbs the script speaks

School bake sale. Every table: mix → bake → box. Helpers taste; the recipe decides order.

  agent      one helper, one job
  pipeline   each cake walks stages alone   (default — no barrier)
  parallel   wait until EVERY tray is back  (barrier — use sparingly)

agent(prompt, opts?) asks one helper. With schema, you get validated JSON — a socket the next stage can hold — and one retry if the first reply is messy.

pipeline lets cake A box while cake B is still mixing. parallel is for when the next step truly needs all results together — tasting every tray before the scorecard.

# teaching sketch — shape only (see code.py for the runnable sample)
results = await ctx.pipeline(DIMENSIONS, audit, verify)
confirmed = [f for r in results if r for f in r["confirmed"]]

When a helper fails, the fleet stays kind: a parallel miss becomes null in that slot; a pipeline miss drops that item and skips its later stages. Filter before you merge.

When the kitchen pauses, a journal on disk remembers calls in invocation order. Resume replays the longest unchanged prefix; at the first change, everything after runs live. Real JS runtimes ban Date.now() / Math.random() so the notebook stays aligned. This Python demo does not fully sandbox that — write deterministic scripts anyway.

  journal   [A] [B] [C] [D]
  resume     hit hit  ✂  live   ← prefix breaks at C

Official primitive card + quieter verbs

Official card: agent, then parallel (barrier) vs pipeline (streaming stages). Claude Code also exposes model / isolation / agentType; our teaching runtime keeps a smaller surface.

Quieter verbs: phase, log, nested workflow (one level), args, budget.

Once you can write the recipe — the pattern toolbox

The verbs are flour and heat. What people keep reinventing are a handful of shapes — a toolbox, not a mandatory menu.

Official six-pattern grid. Script owns topology; this lesson speaks each shape with agent / parallel / pipeline / journal.

Three shapes matter most for the sample ahead — feel them before the names pile up.

Fanout-And-Synthesize — fifty files will not fit one tired context. Split, run many, merge at a barrier.

  task ──► ● ● ● ● ══barrier══► synthesize

Adversarial Verification — the fox must not grade the henhouse. A worker produces; independent verifiers try to knock it down; only survivors remain.

  worker ──► verifier
         ├──► verifier
         └──► verifier   → keep what still stands

Generate-And-Filter — you need options, not the first clever-sounding idea. Many generators, then a rubric (and dedupe).

The same toolbox holds Classify-And-Act (route to a specialist), Tournament (pairwise judges to a winner), and Loop Until Done (keep spawning while “new findings?” is yes, with a hard budget). Borrow a style only when its cost buys clarity or safety.

How each pattern maps to this lesson’s primitives

Pattern	Primitive sketch	Skip when…
Classify-And-Act	agent → branch → agent	Every item needs the same treatment
Fanout-And-Synthesize	pipeline / parallel → merge	A single pass already fits
Adversarial Verification	produce → parallel(verify) → filter	A wrong answer is cheap
Generate-And-Filter	parallel(gens) → filter	The answer space is already tiny
Tournament	pairwise judge agents	A clear rubric picks a winner in one pass
Loop Until Done	while + stop + budget	The work has a known size

# teaching sketch — classify then act
kind = await ctx.agent("classify this ticket", schema=KIND)
if kind["type"] == "billing":
    return await ctx.agent("handle billing…")

Compositions are normal: deep research often stacks fanout → filter → verify → synthesize.

When workflows meet untrusted input

Support tickets and user feedback are untrusted. The agent that reads them should not also hold the keys that open a PR. Keep an airlock: readers stay read-only, pass only a structured summary; a trusted actor acts on the summary — never the raw text.

  backlog (untrusted)
       │
       ▼
  ┌─ QUARANTINE (read-only) ─┐
  │  readers → dedupe → summary │
  └────────────┬───────────────┘
               ▼
  ┌─ TRUSTED (high privilege) ─┐
  │  actor → fix / escalate     │
  └─────────────────────────────┘

Official quarantine figure

Readers classify and dedupe in quarantine; high-privilege tools live on the trusted side. Pair with /loop if the backlog never sleeps.

Walking review-changes — a composition

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

  correctness ── audit ── verify ──┐
  security    ── audit ── verify ──┤── confirmed
  performance ── audit ── verify ──┤
  style       ── audit ── verify ──┘
       fanout        ▲                synthesize
                     └── skeptical verify per finding

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

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

# from code.py — runnable sample (abbreviated)
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}

How this hangs on s15 (without replacing it)

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

In the product, the run can sit in the background with notifications. Our teaching CLI keeps demo / resume in the foreground so phases and cache hits are easy to watch. Same ideas; we say so when we simplify.

Turning the gem: who holds the plan?

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
s13 Agent Teams	Lead + mailbox	Shared tasks / messages	Long-running peers
s15 Integrated Harness	Model in one loop	messages[]	Cumulative coding agent
s16 Workflow	Script	Variables + journal	Structured fan-out + verify
s17 Goal Loop	Evaluator at stop	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 structure must outlast a single context — not because a panel sounds impressive.

And when to leave it on the shelf

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

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 design thought.

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. On a full resume, agents flip to cached and you should see agents=0 tokens=0 — 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?