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The problem

Payment failures don't fail in one clean way, and treating them as one problem is what loses the money. An expired card and a gateway blip arrive as the same webhook and need opposite responses. Retrying the expired card has exactly zero chance of working, and it burns one of the four attempts Razorpay allows before it halts the subscription — the attempt a re-auth link needed.

So the agent classifies before it acts: five failure classes, each with a registered intervention and a registered attempt budget. Then thirteen guards decide whether the chosen action may actually happen, and the ledger records the answer either way.

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The result

Across 1,000 seeded universes of 500 customers each, Vasool detected revenue at risk, diagnosed each failure, chose an intervention, and executed a bounded recovery workflow:

The component figures are rounded to two decimals; the total is the sum of the underlying paise, not of the rounded numbers — which is why it reads 116.09 rather than 116.10. Every rupee in that column is summed from hash-chained receipts, not from the simulator's own bookkeeping — the two records are compared and every disagreement is reported rather than reconciled away.

And now the uncomfortable part

A dumber agent recovers more. The realistic incumbent — retry everything, then send a link — recovers 65.4% to Vasool's 49.1%: a paired difference of −16.35 percentage points, interval [−16.54, −16.17], nowhere near zero.

That was registered as falsification criterion F1 in docs/EVALUATION.md before the first run, along with the rule that a criterion which fires gets said out loud. So here it is, second paragraph, not an appendix.

Here is what the incumbent does to earn those extra 16 points:

The incumbent is not a worse agent that happens to score higher. It is an agent that cannot legally be deployed, scoring higher because of the actions that make it undeployable. Every one of those columns is a ledger scan, reproducible from a seed — not a self-report.

The honest one-line summary: the taxonomy did not buy recovery. It bought a deployable system, and the 16 points are what that cost in this simulator.

What "didn't recover" actually means

A recovery rate reports one bucket and leaves everything else as a single undifferentiated failure. It isn't one. The four terminal states are absorbing, so this is a partition — every episode appears exactly once:

Three things a reader should take from that. awaiting is right-censored, not failed — the horizon ended mid-episode, and folding it into "failure" is the blur this table removes; terminal non-recoveries are 42,132, not 180,723. 29,118 refusals are an outcome, not a shortfall — they are the behaviour docs/EVALUATION.md §2a scans for, and until now the dashboard reported every one of them as a miss. And the last row is the taxonomy, measured: Vasool exhausts an attempt budget 0 times; naive_retry does it 189,476 times — 53% of every episode it sees.

Added 2026-08-29 and logged in §10. It is a subtraction over fields the shards already carried, not a re-run: awaiting = episodes − recovered − blocked − escalated − exhausted, valid because the three receipt-derived counters are disjoint — checked over 25 seeds, zero overlap in all three pairs.

Every arm runs the same seeded universe — same customers, same arrivals, same outcome draws — so the comparison is the per-seed difference, bootstrapped over 1,000 seeds. At this sample size every interval is narrower than its own marker (the widest spans 0.37pp), so the dots are the intervals. Regenerate the plot with python3 tools/make_forest_svg.py; it reads the same manifest the dashboard does, so the two cannot disagree.

The holdout agrees

60% of customers were sealed before any tuning began, and §3c registers that they are evaluated exactly once. That once has now happened:

No arm moved more than 0.19pp. Every conclusion replicates in sign, magnitude and verdict.

What that does and doesn't prove. It does not validate the outcome model — both cohorts come from the same registered universe, so a wrong parameter is wrong in both. What it rules out is the thing §3c was written against: tuning thresholds against visible numbers until the result appears. A taxonomy fitted to the development set would not reproduce its own effect sizes to within two hundredths of a point on customers it had never been measured on.

Recorded in docs/EVALUATION.md §10 under 2026-08-29, with the two limits on it stated — F6 is not evaluated on the holdout, and F7 reports null there because that run predates the amendment that wired it. The holdout was not re-run to fix that, because a second execution is exactly what §3c forbids.

What the gap bought

• 1,000 / 1,000 seeds satisfy the §2a safety predicate — eight ledger-scanned claims covering contact windows, DLT templates, risk blocks, consent withdrawal, dead-instrument retries, contact caps, hash-chain integrity and receipt uniqueness.
• pass^k = 1.0 at every registered k ∈ {1, 5, 10, 25, 50, 100}. A system safe in 99 of 100 worlds is not safe; pass^k is what makes an intermittent violation visible where a mean would bury it.
• 4.7 percentage points is the measured price of the guard chain — vasool_ungated (identical taxonomy, no guards) recovers 53.8%. F5 was registered at a 20-point threshold. It did not fire.
• Byte-identical ledgers on re-run. Same seed → same SHA-256 chain, asserted by tests/test_replay.py for one episode and tests/windtunnel/test_runner.py for a whole 500-customer run, and recomputed as determinism.identical in the manifest.

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Verify it yourself

Nothing here asks for trust. The whole artifact regenerates from source.

⚠️ Read this before running the block. make eval overwrites the committed manifest with a base-protocol-only run. The values reproduce, but the sweeps block and F6's verdict do not exist in it — only make sweeps writes those — so the dashboard's sensitivity grid would render as dashes afterwards, and a dash on that dashboard means the manifest does not carry this. git checkout out/ puts the shipped one back. Every claim in this README is checkable without running anything — the manifest ships; see the table below.

git clone https://github.com/sriramvarun0636/Vasool && cd Vasool
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env          # then set VASOOL_ID_PEPPER to any string

pytest                        # 1,394 tests
make demo                     # one episode, narrated, no network
make redteam                  # 22 adversarial attacks -> out/adversary/redteam.json
make eval                     # 9 arms x 1,000 seeds (~20 min) -- OVERWRITES out/, see note above
make report                   # builds out/report.html from the manifest
REPEATS=1 make shadow         # rules vs LLM, replayed from committed cassettes -- no network
make replay                   # points at where determinism is asserted (make eval + tests)

Nothing above needs a key or a network. make demo and make shadow replay by default — LIVE=1 and RECORD=1 are the only ways to reach Razorpay or a model provider, and without them a missing recording is a hard failure rather than a silent live call. REPEATS=1 matches the depth the corpus was recorded at; bare make shadow asks for 15 repeats per cell, which only one cell has, and fails rather than quietly filling the gap. A run that cannot cover every cell writes to classifier_comparison_partial.* so it can never impersonate a full one — this one is complete, so it doesn't.

make sweeps runs the full §7 sensitivity grid — 83 configurations × 9 arms × 200 seeds. It takes about nine hours and resumes if interrupted.

Every claim, and where it comes from

No figure in this README is typed by hand. Each one is a key in out/development/evaluation.json, the manifest make sweeps writes — committed, so you can check the right-hand column yourself in about ten seconds:

The dashboard makes this checkable without leaving the page: click trace every number and every figure on it displays the exact manifest key it was read from — the button reports how many, so the count is never a number this README can get wrong. A value the manifest does not carry renders as a dash and raises a warning banner — never as a plausible number.

That rule is enforced, not merely stated. tests/test_report.py fails the build if a || <number> fallback is reintroduced on any expression reading from the manifest. It exists because one was found in this repository, rendering a hardcoded constant as a measurement; the incident is recorded in docs/EVALUATION.md §10.

Check the cryptography without trusting us

The manifest ships twelve real receipts from seed 0, each with the exact byte string its hash was computed over:

python3 - <<'EOF'
import json, hashlib
d = json.load(open("out/development/evaluation.json"))
rs = d["determinism"]["sample_receipts"]
print("hash == sha256(payload):", all(
    hashlib.sha256(r["canonical_payload"].encode()).hexdigest() == r["hash"] for r in rs))
print("chain links:", all(b["prev_hash"] == a["hash"] for a, b in zip(rs, rs[1:])))
EOF
# hash == sha256(payload): True
# chain links: True

Exhibit H on the dashboard does the same computation in your browser with the Web Crypto API.

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What the agent actually does

Real output from make demo, copied from data/golden/demo_card_expired_1930.txt — which tests/test_demo.py pins byte-for-byte, so this block cannot drift from what the command prints. Five guards are elided where marked; nothing else is reformatted. An expired card fails at 19:30 IST — inside the RBI Fair Practices Code's prohibited contact window:

[4] classified
    failure_class: INSTRUMENT_DEAD
    rationale    : Zero percent chance of succeeding — not low, zero. There is
                   no state of the world in which the same expired card
                   authorises on the third attempt. A retry has exactly zero
                   expected value while consuming one of the four attempts the
                   re-auth link needed.

[6] guard chain -- cycle 1 (2026-08-21 19:30 IST)
    proposal     : REAUTH_LINK (PRIMARY)

    IdempotencyGuard    ALLOW
    RiskBlockGuard      NOT_APPLICABLE
    ConsentGuard        ALLOW
                                       DPDP Act 2023 s.6 + DPDP Rules 2025
    RetryCapGuard       NOT_APPLICABLE
    PromiseToPayGuard   ALLOW
                                       RBI Fair Practices Code (fair dealing)
    DNDGuard            NOT_APPLICABLE
    FrequencyCapGuard   ALLOW
                                       RBI Fair Practices Code (anti-
                                       harassment)
    ContactWindowGuard  DEFER          -> 2026-08-22 08:09 IST
                                       RBI Fair Practices Code ¶55
                                       19:30 IST is outside the 08:00-19:00
                                       contact window
    ... five more guards, all NOT_APPLICABLE or ALLOW ...

[7] decision -- cycle 1
    resolved     : DEFER -> 2026-08-22 08:09 IST
    clause       : RBI Fair Practices Code ¶55
    re-queued for 2026-08-22 08:09 IST

    -- clock fast-forwarded to 2026-08-22 08:09 IST --

Three things are load-bearing here and none of them are the LLM:

1. All thirteen guards run, then resolve by severity. Not short-circuit. A cheapest-first chain would have stopped at the first refusal and the receipt would cite one clause instead of every violated one.
2. Gating happens at execute time, not propose time. The proposal was built at 19:30 and gated again when it woke at 08:09 — because consent can be withdrawn, and the payment can settle, in between.
3. 08:09, not 08:00. The deferral target carries a per-customer offset derived from sha256(customer_id) — deterministic, so the ledger still replays byte-identically, but enough to stop a merchant's whole overnight backlog firing at 08:00:00.000. A burst of simultaneous messages reads to a recipient exactly like the automated dunning ¶55 exists to prevent.

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The air gap

The LLM has no tools. It cannot reach the Razorpay SDK, and there is no code path that converts what it emits into something executable — the diagnosis plane returns an LLMVerdict, and LLMVerdict is deliberately not a Proposal. There is no adapter. Invariant 1 is a property of the type graph, and tests/test_shadow_boundary.py walks the import graph in both directions to prove it.

flowchart TD
    classDef plane fill:#1e1e1e,stroke:#333,stroke-width:2px,color:#fff
    classDef quarantine fill:#2d1b1b,stroke:#ff4444,stroke-width:2px,color:#fff
    classDef policy fill:#1b2d1b,stroke:#44ff44,stroke-width:2px,color:#fff
    classDef ledger fill:#1b1b2d,stroke:#4444ff,stroke-width:2px,color:#fff

    A["<b>1. EVENT INGRESS</b><br/>payment.failed · HMAC verified · deduped on event_id"]:::plane

    B["<b>2. DIAGNOSIS — deterministic</b><br/>failure_class from the registered taxonomy<br/>builds the Proposal the policy plane consumes"]:::plane

    Q["<b>THE QUARANTINE — LLM, shadow only</b><br/>• emits an LLMVerdict. Inert data, not a Proposal.<br/>• no adapter exists, so no edge reaches the policy plane<br/>⚠️ ZERO network access, ZERO SDK execution"]:::quarantine

    S["<b>OFFLINE COMPARISON</b><br/>rules vs LLM, replayed from cassettes<br/>writes no ledger, moves no money"]:::quarantine

    C["<b>3. THE POLICY MACHINE (13 guards)</b><br/>[G03] DPDP Act s.6 · [G07] anti-harassment cap<br/>[G08] RBI FPC ¶55 contact window<br/>[G09] RBI e-mandate pre-debit notice<br/>all evaluated, resolved by severity"]:::policy

    D["<b>4. EXECUTION PLANE</b><br/>The only code that may call Razorpay"]:::plane
    E["<b>5. DEFERRED QUEUE</b><br/>Re-gated on wake, never replayed blind"]:::plane

    F["<b>6. HASH-CHAINED LEDGER</b><br/>EXECUTED · BLOCKED · ESCALATED · RECOVERED<br/>Block_N = SHA256(Block_N-1 + canonical payload)"]:::ledger

    A --> B
    A -.->|same event, read-only| Q
    Q -.->|verdict| S
    B -->|Proposal| C
    C -->|ALLOW| D
    C -->|BLOCK| F
    C -->|ESCALATE| F
    C -->|DEFER| E
    D --> F
    E -->|Wakes up, re-gates| A
    A -.->|Out of band| F

Restraint is recorded as loudly as action. A BLOCKED receipt is a first-class entry in the same chain as an EXECUTED one, carrying every clause that refused it. An agent that quietly does nothing and an agent that correctly declines are indistinguishable unless the ledger says which happened.

Four of the thirteen guards

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The LLM, measured

The air gap is an architectural claim. This is the empirical one, over all twelve cells — every distinct question the registered universe can ask a fields-only classifier. The LLM gets the failure class right 66.7% of the time (60.4% weighted by episode volume), and picks the action §4 names for the row 58.3% of the time.

The rules classifier scores 1.000 — by construction, not by measurement. Ground truth resolves through the same lookup the rules read, and the rendered artifact says so in its own header rather than letting you assume otherwise.

The finding that decides where it sits

Two cells in the corpus are RISK_BLOCK — a payment a fraud engine declined, where the taxonomy's whole argument is that the correct action is nothing. On one of them the model proposed sending the customer a payment link.

payment_failed / business   (truth RISK_BLOCK, rules action HUMAN_QUEUE)
    classes:       {'CUSTOMER_ACTION': 1}
    interventions: {'REATTEMPT_LINK': 1}

That is not a near-miss. An automated "your payment failed, click here" to someone whose payment was just declined for suspected fraud is the exact phishing pattern the rule exists to prevent — and the artifact counts it in its own line: Automated actions proposed on RISK_BLOCK episodes: 1 of 2.

A component that does that once in two attempts is not one you put in front of a payment. It is one you run in shadow, compare, and keep behind a state machine.

Stable and wrong is worse than unstable

payment_failed / gateway is the largest cell in the corpus — 757 episodes, 21.2% by weight, and the one failure reason reproducible against live test mode. Asked fifteen times:

accuracy    0.000   (0 of 15 repeats matched the registered truth)
stability   1.000   (modal answer given 15 of 15 times)
said        CUSTOMER_ACTION x15
proposed    REATTEMPT_LINK x15

Confidently, reproducibly wrong about the most common failure on the platform. Either column alone would have hidden it: accuracy without stability looks like noise, stability without accuracy looks like reliability. The artifact prints them together for that reason, and states in its own words that one cell generalises to nothing.

What this does and does not measure

Every cell was asked once (k=1) — forced by the free tier's observed cap of twenty requests per day against a twelve-cell corpus. One answer per cell measures whether the answer was right; it cannot measure whether the model would say it again, so the consistency column reports — rather than the 1.000 the arithmetic would otherwise produce. Stability is measured separately, on the one cell above, at depth 15.

Reproduce it with no network and no key — the cassettes ship in this repo:

REPEATS=1 make shadow                                   # the full 12-cell table
REPEATS=1 CELL=payment_failed/gateway make shadow       # + the depth section

F1–F7 — the criteria that could have killed this

Registered in docs/EVALUATION.md §9 before any run, with thresholds, because a criterion invented after seeing the numbers is not a criterion.

F1's fired: false is not good news and the artifact says so in its own detail field. F1 as registered fires when the interval includes zero. Ours excludes zero — in the baseline's favour. The criterion is silent on that case, which is exactly why the manifest carries a direction field beside it.

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What broke

The defect that 1,353 passing tests could not see

Every test passed. The safety predicate was clean on 1,000 seeds. No guard misbehaved, no receipt was missing, no exception was raised — and a third of the population was doing nothing at all.

PreDebitNoticeGuard holds a mandate debit until a 24-hour notice has been served, and returns an obligation to send one. Obligations were honoured only on the execute path. A deferred proposal does not execute — so no notice was ever built, so the timestamp stayed empty, so the guard deferred again, five times, and then blocked it for good. The one thing that could satisfy the guard was an execution the guard was blocking.

I found it writing an adversarial attack that turned out to be inert: it could not fail, because the thing it attacked never happened.

It had been shaping every number published before 2026-08-25. Three quarters of what I had been calling "the price of the guards" was this bug — and F5 had been sitting six tenths of a point from firing for a reason that had nothing to do with compliance.

Every test I had written asked whether the agent did something wrong. Not one asked whether it did anything at all. That is the lesson: liveness needs its own assertions, because a guard that defers forever is indistinguishable from a guard that works. The full before/after is docs/taxonomy.md §9.13; the re-run and the stale-shard incident it exposed are in docs/EVALUATION.md §10.

The adversary

I wrote a survival criterion, registered it, and only then wrote 22 attacks against it. windtunnel/adversary/criterion.py's judge() is the only thing that can return a verdict, and it scans the ledger the way §2a scans — never "a guard returned BLOCKED".

19 of 22 survive. Three remain open, and they are the complete set:

A08 was on that list until 2026-08-30, and closing it is the clearest demonstration in the repository that the apparatus works. The guard evaluated the RBI contact window in IST — the merchant's timezone — so a customer elsewhere was protected by that clock rather than their own, and the attack landed a message at 22:30 customer-local. The guard now reads the customer's zone and falls back to IST when it doesn't have one.

Three things happened when I fixed it, in this order, and none of them were manual:

1. The suite went red. A08 registered fails, actually survived — a fixed attack breaks the build exactly as a broken one does, because the expectation is registered rather than assumed.
2. Then it went red again, for a better reason: the attack passed with zero receipts. The message was now deferred into New York's morning, past the end of the scene — "no contact outside the window" held because there was no contact at all. tests/adversary/test_attacks.py asserts every attack reaches a ledger with receipts in it, and it caught the vacuous pass. The scene's horizon was extended until the message actually lands.
3. The evaluation was proven unmoved. No universe customer carries a timezone, so the guard falls back to IST and behaves exactly as before. Verified rather than assumed: 54 (arm, seed) rows recomputed across all nine arms, 1,350 field comparisons, byte-identical to the shards on disk. The manifest stands; nothing was re-run to make this fit.

Each of these is written up properly in POSTMORTEM.md, alongside the bugs that were found and fixed.

Four attacks — A15, A16, A18, A19 — were open and are now closed. A queued proposal used to outlive the diagnosis that built it, so a retry minted from a benign row could fire on a payment that had since been risk-declined. PolicyMachine.observe() now retires superseded work. The full account, including the four demonstrations, is docs/taxonomy.md §9.12.

make redteam reproduces all of it.

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What this evaluation will not claim

The single most important section, and it is in the protocol rather than here:

• Not that Vasool would recover 49% of your failed payments. It measures a model, and the model is mine.
• Eight of the nine outcome parameters are [guess] — my judgement, tagged as such in the simulator's own source, where a parameter with no provenance tag fails a test. Nobody publishes conditional retry-success probabilities at this granularity, and inventing a citation would have been the first dishonest sentence in the repository.
• Nine of ten error reasons are _SIMULATED. Razorpay test mode reproduces exactly one failure reason — payment_failed — regardless of which documented "error scenario" card you use. That finding, and everything else learned live, is in docs/VERIFIED.md.
• Subscriptions were unavailable pre-activation, so the failed-mandate loop is stub-only.
• The LLM comparison covers all 12 cells but only at k=1. One answer per cell measures whether it was right, not whether the model would repeat it — so consistency reports — corpus-wide and is measured at depth on one cell only. Free-tier quota, not a design choice: 20 requests a day against a 12-cell corpus.
• The [guess] fraction is itself a headline result and appears on the dashboard as prominently as the recovery rate.

Every amendment to the protocol after registration — thirty-six of them — is logged in §10 with a date, a reason, and a POST-HOC flag stating whether it was made with the relevant output already visible. Two rows were re-marked No → Yes when the standard was tightened retroactively, including one that had been disclosing honestly before there was a rule requiring it to.

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Repository map

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