Anthropic published a piece in November on code execution with MCP: give an agent a sandbox and a generated API instead of raw tool calls, and a task that would cost 150,000 tokens costs 2,000 instead — a 98.7% reduction, because the raw data never has to pass through the model's context. Cloudflare shipped the same idea back in September under the name “Code Mode,” and later put a number on it: 2,500+ API endpoints, 1.17M tokens down to about 1,000.

Our first reaction was “we already built this.” Every claude/codex/opencode session in the swarm ships a rubric today — system.agent.context_mode in src/prompts/session-templates.ts — that tells the agent: 10+ items or a bulk fan-out means reach for a script, not N individual tool calls. We didn't add this for the post. It's just what the swarm already does, and it's the same machinery behind Script Workflows.

What we hadn't done is measure it against Anthropic's own yardstick, with our own production data. So we did — using a script two of our own agents wrote and ship globally: workflow-triage.

What the script does

workflow-triage scans every automation the swarm runs — all workflows and all cron schedules — and flags which ones are dead, failing, or fine. Under the hood, that's 26 separate calls: one workflow_list, one schedule_list, and one workflow_listRuns per workflow (24 of them). Normally, an agent doing this “by hand” would make 26 individual tool calls, and every one of those raw JSON payloads would sit in its context for the rest of the conversation.

Instead, the script makes all 26 calls itself, inside its own sandboxed subprocess, and only the final distilled result — one summary object — ever reaches the agent.

What we measured, live

We didn't estimate this. We ran the script and the underlying calls separately, against the real production catalog (24 workflows, 60 schedules), and measured both sides directly.

Being honest about what's measured vs. estimated

The wall-clock and cost of the raw path are the only estimated numbers here, and we want to be upfront about exactly how each was built, because the whole point of this post is “show your work,” not “trust our vibes”:

• The script's own numbers are 100% measured. durationMs: 13120 and the 25,811-character result came directly off a live workflow-triage run, no rounding tricks.
• The raw-path character count is measured-then-extrapolated, not invented. We called the exact same underlying SDK methods the script calls (workflow_list, schedule_list, workflow_listRuns) directly, outside the script, to see what a raw sequential-tool-calling agent would actually receive: workflow_list({}) → 16,304 characters (measured); schedule_list({}) → 72,105 characters (measured); workflow_listRuns sampled directly on 4 of the 24 workflows spanning small to large — 6 runs → 98,706 chars, 26 runs → 293,036 chars, 53 runs → 157,720 chars, 227 runs → 525,968 chars. That's 1,075,430 characters across 312 sampled runs — a measured average of 3,447 characters per run. We applied that measured average to the real total run count the script itself recorded across all 24 workflows (920 runs) to get the ~3.17M character estimate for the full raw-equivalent scan, then added the two list calls.
• Why we didn't just run all 26 raw calls for real: doing so would mean pulling ~3.2 million characters of raw JSON into the very conversation used to write this post, to prove a post about not doing that. We sampled the smallest and largest workflows instead and extrapolated from the swarm's own recorded run counts — which is a more honest demonstration of the problem than it sounds, because it's exactly the judgment call the rubric asks an agent to make before it starts calling tools one at a time.
• One footnote worth flagging: the limit: 25 we pass into workflow_listRuns doesn't actually cap the result — it returned all 227 runs for our busiest workflow, not 25. That's a real gap in the underlying API worth a ticket, but it doesn't change this post's math; if anything it makes the raw-sequential-calls number a floor, not a ceiling.
• The $2.44 raw-path cost is a floor, not a real total. It only prices the tokens once, as if they entered context on a single turn. In a real agentic loop, each of the 26 raw tool results stays in context and gets re-sent as input on every subsequent turn — which is the exact mechanic behind Anthropic's own 150K-token example (the transcript passes through the model's context twice: once on read, once on write). A real 26-turn sequential run would cost meaningfully more than $2.44; we can't defend a precise multiplier without actually running that wasteful version.

The code that does it

This is the entire script — not a snippet, the whole thing, id da3b5c7b-b9a6-4f9e-be9e-f682aca48ea0, global scope, callable by any agent in the swarm:

// workflow-triage — read-only health scan of all workflows + schedules.
// Returns per-item run stats + DEAD/FAILING/OK flags + a markdown report of flagged items.
export default async function (args: any, ctx: any) {
  const runLimit = args?.runLimit ?? 25;
  const staleDays = args?.staleDays ?? 14;
  const includeSchedules = args?.includeSchedules ?? true;
  const now = Date.now();
  const dayMs = 86400000;
  const isFail = (s: string) => /fail|error|halt|timeout|abort/i.test(String(s || ""));

  // ---- Workflows: run stats require workflow_listRuns (no lastRunAt on the row) ----
  const wl = await ctx.swarm.workflow_list({});
  const wfs: any[] = wl?.data ?? [];
  const rows: any[] = [];
  const pool = 6; // small concurrency to avoid hammering
  for (let i = 0; i < wfs.length; i += pool) {
    const batch = wfs.slice(i, i + pool);
    const res = await Promise.all(batch.map(async (w: any) => {
      let runs: any[] = [];
      try { const rr = await ctx.swarm.workflow_listRuns({ workflowId: w.id, limit: runLimit }); runs = rr?.data ?? []; }
      catch { runs = []; }
      const total = runs.length;
      const failed = runs.filter((r: any) => isFail(r.status)).length;
      const stamps = runs.map((r: any) => Date.parse(r.startedAt || r.createdAt || r.finishedAt || "")).filter((n: number) => !isNaN(n));
      const lastRun = stamps.length ? Math.max(...stamps) : null;
      const daysSince = lastRun ? Math.round((now - lastRun) / dayMs * 10) / 10 : null;
      const failRatio = total ? Math.round(failed / total * 100) / 100 : 0;
      const dead = !w.enabled || total === 0 || (lastRun != null && daysSince! > staleDays);
      const failing = total >= 2 && failRatio >= 0.5;
      return {
        kind: "workflow", id: w.id, name: w.name, enabled: w.enabled,
        runs: total, failed, failRatio,
        lastRunAt: lastRun ? new Date(lastRun).toISOString() : null,
        daysSinceLastRun: daysSince, nodeCount: w.nodeCount,
        flag: failing ? "FAILING" : dead ? "DEAD" : "OK",
      };
    }));
    rows.push(...res);
  }

  // ---- Schedules: lastRunAt + consecutiveErrors already on the row ----
  const scRows: any[] = [];
  if (includeSchedules) {
    const sl = await ctx.swarm.schedule_list({});
    const scs: any[] = sl?.data?.schedules ?? [];
    for (const s of scs) {
      const lastRun = s.lastRunAt ? Date.parse(s.lastRunAt) : null;
      const daysSince = lastRun ? Math.round((now - lastRun) / dayMs * 10) / 10 : null;
      const dead = !s.enabled || (!lastRun) || (daysSince != null && daysSince > staleDays);
      const failing = (s.consecutiveErrors ?? 0) >= 3;
      scRows.push({
        kind: "schedule", id: s.id, name: s.name, enabled: s.enabled,
        consecutiveErrors: s.consecutiveErrors ?? 0,
        lastRunAt: s.lastRunAt ?? null, daysSinceLastRun: daysSince,
        nextRunAt: s.nextRunAt ?? null, cron: s.cronExpression,
        flag: failing ? "FAILING" : dead ? "DEAD" : "OK",
      });
    }
  }

  const rank = (f: string) => f === "FAILING" ? 0 : f === "DEAD" ? 1 : 2;
  rows.sort((a, b) => rank(a.flag) - rank(b.flag) || (b.failRatio - a.failRatio));
  scRows.sort((a, b) => rank(a.flag) - rank(b.flag) || (b.consecutiveErrors - a.consecutiveErrors));

  const sum = (arr: any[]) => ({
    total: arr.length,
    failing: arr.filter(r => r.flag === "FAILING").length,
    dead: arr.filter(r => r.flag === "DEAD").length,
    ok: arr.filter(r => r.flag === "OK").length,
  });

  // markdown report of only the flagged (non-OK) items
  const flaggedW = rows.filter(r => r.flag !== "OK");
  const flaggedS = scRows.filter(r => r.flag !== "OK");
  let report = `## Workflow triage — flagged\n`;
  report += `Workflows: ${sum(rows).failing} failing / ${sum(rows).dead} dead / ${sum(rows).ok} ok (of ${rows.length})\n`;
  // ... table-building omitted here for length; full source has per-row markdown
  //     tables for both workflows and schedules — see the script catalog entry.

  return {
    params: { runLimit, staleDays, includeSchedules },
    workflows: { summary: sum(rows), rows },
    schedules: includeSchedules ? { summary: sum(scRows), rows: scRows } : null,
    report,
  };
}

There's no framework here. It's a for loop with a concurrency pool of 6, a couple of Date.parse calls, and a sort. The only thing that makes it “code mode” rather than “26 tool calls” is where it runs: inside the script sandbox, not inside the agent's context. Every ctx.swarm.workflow_listRuns call above returns its (large, sometimes limit-ignoring) payload straight back into the loop variable runs — never into the conversation. The agent that eventually calls this script sees only the return statement at the bottom: one object, 25,811 characters, done.

The mechanism, in one sentence

“Intermediate results stay in the execution environment by default... the agent only sees what you explicitly log or return.” — Anthropic, Code execution with MCP

That's precisely what runs is doing on line after line above, and precisely why the raw-path column in our table is ~126x bigger than the script column — none of it is a framework trick, it's just that raw data stopped being handed to the model 24 times in a row.

What it's worth across models

The table above prices everything at Claude Sonnet 5's rate, since that's the model this measurement ran on. The reduction itself is a token-count story, not a pricing story — it holds at the same ~99.2% regardless of which model reads the result. Here's what the two token counts (~6,450 measured / ~815,000 measured-floor) are worth at five current models' own direct-provider input rates, pulled from the swarm's live pricing table (src/be/modelsdev-cache.json, verified 2026-07-08):

Same measured/extrapolated caveats as the table above apply to every row here — the “script” column is a direct conversion of the 25,811-character measured result, the “raw path floor” column is the measured-then-extrapolated ~815,000 token estimate, and both are input-token pricing only, priced once rather than compounding across a real multi-turn session. Rates are each provider's direct API pricing, not a router or resale price.

The one-line takeaway

This already ships by default in every claude/codex/opencode session's system prompt — the decision in front of us isn't “should we adopt code mode,” it's “how do we make the rubric sharper now that we have real numbers to back it,” and workflow-triage — 26 calls, one result, 13.12 seconds, ~$0.02 — is the worked example we'll use to do that.