A refactor of an existing Markdown-review app produced a substantial, independently reviewed candidate—but it did not show that AI agents made the work faster, cheaper, or more efficient. In Aashish Bhandari’s case study, a principal coding agent and delegated workers implemented the changes, a separate agent reviewed them, and a human engineer set priorities and approved checkpoints. The measurements offer a useful baseline, not proof that any portion of the work was waste or could have been avoided.
What was refactored
ReviewWithAI was an alpha application for reviewing Markdown documents. Users could select text, attach comments, hand work to an external coding agent, check changed anchors, record repairs, and accept a particular source revision. This was a refactor of an existing product, not a greenfield exercise in asking an agent to generate an application.
Bhandari describes Goku as the principal architect and implementing agent, Naruto as an independent design and code reviewer, and himself as the person who set priorities, resolved material decisions, and authorized review checkpoints. The work covered server and browser structure, authorization, persistence, tests, operational diagnostics, documentation, and release tooling.
How the work was organized
The effort addressed twelve review findings through eleven low-level designs; findings Q3 and Q4 were combined in one design. The designs moved through three checkpoints:
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Reported changes included typed handlers and a more modular browser structure; clearer ownership of transactions and rollback behavior; redacted diagnostics; stricter inputs for external agents; bounded document discovery; handoff provenance; contributor documentation; and release curation. The process therefore involved more than code generation: it included architecture, review, integration, operational safeguards, and preparation of a candidate release.
What the checks establish—and what they do not
Bhandari reports that the candidate passed independent checks, including tests, browser workflows, and reproduction of its package. The reported totals include 100/100 TAP tests and 73/73 browser checks. These are evidence about the reviewed candidate and the checks run against it. They do not establish that the product was production-ready or defect-free.
“Those results establish a reviewed engineering outcome; they do not establish production readiness or prove that the process was efficient.” — Aashish Bhandari
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That distinction matters when judging agent-assisted engineering. A successful test suite and review checkpoint help support confidence in a particular result. They cannot, on their own, show how much human work was required, whether another workflow would have reached the same quality, or whether the project used fewer resources than a comparable alternative.
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What the token figures mean
For the measured implementation task, the case study reports 1,505 activations and 158,137,319 processed tokens across the parent agent, sixteen delegated worker threads, and approval-review components. Cached input is included in the processed-token total. The measured scope did not include Bhandari’s time or Naruto’s separate review sessions.
These are session-accounting figures. They are not counts of unique code or text, energy use, quota consumption, or a subscription invoice. The report also notes that its collector omitted some compaction activity, some terminal failure information was not explicit in its routine counter, approval reviewers used resources separately, and the evaluation session’s total could not be cleanly isolated from other work. Read the totals as measurements with defined coverage, not as a complete ledger of every cost.
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Why 28.49% is not a waste estimate
The case study associates 10,281,999 processed tokens with parent-agent activations that issued waits, or 28.49% of the canonical parent-token total. That percentage describes the recorded usage of those activations; it does not isolate the marginal cost of waiting. Some waits returned completed work, and an activation that issues a wait can include work beyond the wait itself.
“Some waits returned completed work, so that share cannot simply be called waste or promised as recoverable savings.” — Aashish Bhandari
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Consequently, the report does not show that eliminating waits would save 28.49%, or that another orchestration pattern could recover those tokens without affecting quality or completion. It identifies an area worth measuring—not a demonstrated efficiency opportunity.
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What worker reuse might—and might not—tell us
Worker consumption was concentrated in four reused threads. That pattern raises a reasonable question about whether continuity helped workers preserve context and produce correct work, or whether fresh workers could have completed comparable tasks with less effort. The case study does not test that alternative, so it cannot establish that reuse caused either higher quality or greater consumption.
A useful comparison would hold the task and acceptance criteria steady while varying worker continuity. It would track not only token totals but also accepted quality, rework, recovery from failures, elapsed time, and human effort. Without those outcomes, a lower resource count alone would not demonstrate a better engineering process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the rate calculations are not prices
The report includes analytical model-rate equivalents calculated using rates frozen to 15 September 2026. They apply rates to recorded token categories; they are not measured charges or current price quotations. Because most recorded input was cached, and because token accounting does not by itself capture the full scope of work, those calculations should not be treated as an invoice or proof that switching to a cheaper model would reduce total effort.
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What a stronger evaluation would measure
This single project has no matched alternative orchestration run. A fair follow-up would compare workflows on the same kind of task and record:
- Accepted quality: what passed review and acceptance, rather than raw output volume.
- Rework and recovery: defects, failed attempts, rollback, and effort needed to reach an acceptable result.
- Human effort: time spent directing agents, resolving decisions, reviewing work, and handling exceptions.
- Elapsed time: including waiting, parallel work, integration, and review.
- Resource accounting: model and token usage, separating cached from uncached input and documenting what the counters include or omit.
- Orchestration choices: worker continuity versus fresh workers, wait handling, and approval-review configuration.
Bhandari points to deterministic counters, evaluation budgets, and controlled comparisons that include accepted quality, recovery, and human effort as useful next steps. Those measures would help distinguish a genuinely more efficient process from one that merely shifts work or resource use outside the measured total.
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