A Position Piece · A Governed AI Harness for the Collector's Court अपील · सुनावणी · आदेश

Governing the Determinism Spectrum in the Collector's Court

The officer signs the order in their own hand and answers for every line of it. Our job is to make deterministic the parts of that order that must never be left to chance.

The pitch, in one breath

The Additional Collector, Nagpur decides land-dispute appeals as a quasi-judicialQuasi-judicial authorityA revenue officer deciding appeals is not a court, but must act like one: give notice, hear both sides, weigh the record, and pass a reasoned order. Those orders face revision, and writ scrutiny by the High Court. authority — roughly 1,500 live files, in three languages, every order exposed to revisionMLRC § 257Above the appeal under § 247 sits the revisional jurisdiction: a superior revenue authority may call for and examine the record of any inquiry or proceedings to satisfy itself of the legality or propriety of a decision.Maharashtra Land Revenue Code, 1966 ↗ and writ scrutiny. An AI platform can digitise the record, summarise the file, and draft the order — but the officer who signs it remains accountable for output they have no realistic way to re-verify page by page. A confidence score of 0.85 is built for engineers, not for how a deciding officer reasons about a record.

The answer is not a better confidence score. It is deciding, deliberately, which parts of the appellate workflow must not be stochastic — the cited provision of the MLRCMaharashtra Land Revenue Code, 1966The statute governing revenue administration in Maharashtra — record of rights, mutations (§ 150), appeals (§ 247), revision (§ 257) and review. The law the Additional Collector's orders must live inside.India Code: MLRC, 1966 ↗, the completeness of the record, the service of notice, the reasons behind the order, the signature that issues it — and guaranteeing them in code the model cannot route around. We call this gradual determinismOur framingBorrowed from “gradual typing” in programming: instead of making the whole AI deterministic, you opt into hard guarantees only at the points that must not vary, and leave it fluent everywhere else.: let the AI stay fluent and fast where it earns its keep — reading a 687-page bundle, summarising, drafting — and pin down the handful of things that, if wrong, vitiate orders and end careers. The result is a domain harness for the Collectorate in which “the AI assists, the officer decides” is not a policy statement but a property of the system.

01 / THE PREMISE

A safeguard the model can skip is not a safeguard

India already has its case study. In late 2024 the Bengaluru bench of a tax tribunal recalled its own order after it was found to rest on judgments that did not exist; in 2025 the Bombay High Court quashed a ₹27.91-crore assessment because the deciding authority had relied on three fabricated decisions; and the Supreme Court has since held that citing AI-generated authorities without verification is professional misconduct. Note what these have in common: the failures happened inside adjudication — tribunals and revenue authorities, not careless juniors. An AI that usually cites a real provision, usually links a finding to the record, and usually respects the hearing procedure is not a safeguard for a quasi-judicial office — it is a ground of appeal with good odds. When the rule and the actor share a room, the actor wins; a model can paraphrase a checklist, decide a verification was unnecessary this time, or fabricate a citation that reads perfectly in two languages.

The Nagpur pilot's own safety posture says the right words: “AI shall not independently decide any case. AI shall not issue orders. Every final decision shall remain with the competent authority.” This paper is about the difference between saying that and making it structurally true. A deterministic check is also the opposite of another review for the officer to perform: the harness runs the verification, and refuses to finish until it passes. The re-do is removed, not relocated onto the person who was already carrying the responsibility.

02 / THE RISK MAP

Where each friction meets a surface

The frictions of the Additional Collector's office — the ones the pilot proposal documents slide by slide — sort cleanly onto the control surfaces below. Most are direct: a guarantee can be placed exactly where the friction lives. One — the officer's judicial discretion — is deliberately not a surface at all, and is marked accordingly.

The friction · the fear
Surface
Officer signs orders built on AI summaries they cannot re-verify page by page
Hooks · Loops
Fabricated or out-of-force citations surviving into a signed order
Hooks
Missing mandatory records discovered at the hearing, not before
Commands · Scripts
Petitions, maps and prior orders scattered across bulky trilingual paper files
Scripts · Hooks
Cause-lists and hearing prep re-assembled by hand before each sitting
Commands · Scripts
Every officer in the chain re-reads the file from scratch; no shared brief
Commands · Scripts
Orders typed from a clean sheet; formats drift between clerks and benches
Scripts · Hooks
Every order faces revision and writ scrutiny; the office must show the rule held
Wrappers · Hooks
How the officer weighs evidence and decides the appeal
Not a surface — stays human

03 / THE FRAME

Five surfaces, one axis

The mechanisms already standardised in modern agent harnesses line up along the anatomy of the appeal. Commands govern its entry. Scripts govern execution. Hooks govern the exit. Loops govern continuation. Wrappers own the whole workflow. Read left to right, the ordering tracks two things at once: how much is guaranteed, and how far the control sits beyond the model's reach. A skill the model can paraphrase is in-band; a hook the harness enforces regardless of the model is out-of-band — and that gap is the whole difference between a pattern and a control.

CommandsENTRY ScriptsEXECUTION HooksEXIT · VERIFY LoopsCONTINUATION WrappersWHOLE WORKFLOW
← model discretion · in-bandoffice guarantee · out-of-band →

04 / THE SURFACES, IN THE COLLECTORATE

Where determinism is bought

Commands

Routing determinism

governs entry

Skill selection belongs to the model; a clerk cannot compel a skill to fire. Commands invert that. A command is operator-invoked — a named, logged, auditable entry point into a procedure that runs the same way for every clerk, every bench assistant, every officer in the chain. You cannot make the model choose the right path; you can hand the office a path the model cannot decline.

  • /register-appeal — registers a new appeal under MLRC § 247AppealsSection 247 provides the appeal against orders of subordinate revenue officers — the Tahsildar, Naib Tahsildar and SDO — to the appellate authority; alongside it sit revision (§ 257) and review. The jurisdictional doorway to every file in this pilot.Maharashtra Land Revenue Code, 1966 ↗ with structured metadata — parties, village, survey number, impugned order, the officer who passed it — and computes the limitation position at the door rather than at the first hearing. A registration that “usually captures everything” is a defective file in waiting; as a command it is a guaranteed gate with a record that it fired. registration
  • /scrutiny — runs the case-type checklist deterministically: the 7/12 extractसातबारा · village forms VII & XIIThe record-of-rights extract for a land parcel — occupants, tenure, crops — maintained under the MLRC and the Record of Rights rules. The single most-demanded document in any revenue appeal, and the first thing found missing., the mutation extractMLRC § 150The register of mutations records changes in rights over land; disputed entries go to the register of disputed cases. Appeals against mutation entries are a staple of the Additional Collector's docket.MLRC, 1966, § 150 ↗, the map, the certified copy of the impugned order, the lower record — and issues a deficiency memo the same day, not at the hearing three months later. deficiency
  • /cause-list — assembles the day's cause-list from the case database: party readiness, pending notices, prior order-sheets — one invocation replacing the manual re-assembly before each sitting. hearings
  • /hearing-record — the structured capture of a hearing: recording or upload, type (Roznamaरोजनामा · order sheetThe running diary of a revenue case — each hearing's date, appearances, what transpired, and the interim directions. The procedural backbone an appellate or revisional authority reads first. / party statement / officer note), participants, hearing date — routed to the right slot in the file every time, in the same shape, by every member of the hearing staff. roznama
  • /case-brief — generates the officer-ready brief before a hearing from the indexed record — so the third officer to touch the file inherits a structured brief instead of re-reading 687 pages from scratch. The prose is the model's; that a brief exists, in the office's format, before every hearing, is the command's. preparation
GuaranteeThis procedure ran — because a person invoked it — not because the model judged it relevant this time.

whether registration, scrutiny and the hearing record happened at all.

Scripts

Computational determinism

governs execution

A workflow phase written in natural language leaves both interpretation and code generation to the model at runtime — the right default for novel work, the wrong one for a calculation that must come out the same for every appellant. Once a phase has been generated, reviewed and approved, the code is frozen as a versioned script and the phase reduced to “invoke it.” The non-determinism of re-derivation collapses into a reviewed, auditable artifact.

  • Limitation arithmetic. The appeal window computed deterministically from the date of the impugned order, with the time spent obtaining the certified copy excluded the same way every time. Whether an appeal is in time decides jurisdiction; it is precisely the arithmetic that must never be improvised afresh per file. limitation
  • Deficiency checklist per case type. Which records are mandatory for a mutation appeal versus a partition or demarcation matter is a frozen, versioned table — not a judgment call the model re-makes per file. The AI reads documents; the list of what must exist is code. deficiency
  • Digitisation & indexing pipeline. Scanning, OCR and indexing of Marathi, Hindi and English records as a reproducible pipeline: the same 687-page file yields the same indexed record on every run, with every extracted event tied to its page. Corrections are new versions, not silent drift. record
  • Pendency & dashboard metrics. Cases this month, pending review, long-pending, reserved orders — computed by query, not narrated by a model. A dashboard the Collector reads must never be a hallucination surface. pendency
  • Bilingual order templates. The skeleton of a final order — cause title, parties, procedural history from the Roznama, operative-part scaffolding — populated deterministically from case metadata in Marathi and English, removing transcription error from the very documents that get appealed. orders
GuaranteeThis step computes the same way every time, from code the office approved — not freshly improvised prose.

limitation dates, mandatory-record lists, and the contents of the indexed record.

Hooks

Verification determinism

governs exit

Hooks fire deterministic code at fixed points in the loop. Their headline job here is at the exit — inspecting a draft before it is allowed to be “done” — but the same mechanism also gates each tool call: a PreToolUse hook can allow, deny or rewrite a call before it runs, and a PostToolUse hook inspects the result once it returns. Any of them can gate the work, and a stop-hook can re-drive it: a failed check feeds its reason back to the model as the next instruction, so the failure is worked off rather than landing on the officer's desk. One guardrail is mandatory at the exit: the harness exposes a flag that is true when the model is already in a forced continuation, and the stop-hook must honour it or it will loop forever.

  • Provision & citation verifier. Every MLRC section, rule and case-law citation in a draft order is checked against the office's statute store — the Code as amended through the configured cut-off date — and against authoritative law reports before the draft can leave the harness. This is the mechanical answer to the run of Indian fabricated-citation incidents, to the Supreme Court's misconduct warning, and to the Kerala High Court's AI policyFirst binding judicial AI policy · July 2025The Kerala High Court's policy on AI tools in the district judiciary (19 July 2025): AI is never a substitute for decision-making or legal reasoning, only approved tools may be used, and all AI-produced citations and translations must be meticulously verified.Kerala HC AI Guidelines (PDF) ↗ requirement that AI citations be meticulously verified — enforced as a gate, not a reminder. citations
  • Source-link gate. No paragraph of a draft order or case brief may survive without a page-level citation into the digitised record (p.3 · p.239). An unsourced finding cannot be the final turn — the “every line cites its source” promise of the pilot, made unskippable. provenance
  • Natural-justice gate. Before an order finalises, a deterministic check that audi alteram partemNatural justice“Hear the other side” — the rule that no one is condemned unheard. For a quasi-judicial authority, an order passed without notice and opportunity of hearing is liable to be set aside regardless of its merits. is satisfied on the record: notices issued and served on every respondent, appearances or ex-parte procedure recorded in the Roznama. An order that skipped a hearing is void on arrival; the gate makes it unreachable instead. natural justice
  • Reasoned-order gate. Every operative conclusion must be supported by recorded reasons tied to the record — the duty the Supreme Court fixed on quasi-judicial authorities in Kranti Associates(2010) 9 SCC 496Kranti Associates Pvt. Ltd. v. Masood Ahmed Khan: the Supreme Court's synthesis of why quasi-judicial orders must record reasons — reasons restrain arbitrariness, enable appeal and revision, and are as indispensable as natural justice itself.Kranti Associates v. Masood Ahmed Khan ↗. A bare, unreasoned draft is blocked with the gaps named. reasons
  • Egress & data-protection gate. Blocks any output that would route case records or citizens' personal data to an un-approved destination — the concrete control behind “no public AI model training on government data,” on-premise deployment, and the DPDP ActDigital Personal Data Protection Act, 2023India's personal-data statute. Land-dispute files are dense with citizens' personal data — names, holdings, family partitions — and a government platform processing them needs purpose-bound, auditable data flows. obligations that come with a file full of citizens' holdings and family disputes. confidentiality
  • Record-access gate. A PreToolUse hook confines each retrieval to the cases the requesting role is permissioned for; a PostToolUse hook stamps provenance on every document returned and writes the access to the audit trail — case-wise permissioning enforced at the tool boundary, not in a policy document. access

Where the property needs judgment rather than a pass/fail rule — “does this order actually deal with every ground the appellant pressed?” — a prompt- or agent-type hook runs a cheap evaluator in the same slot. The shape is identical; only the oracle changes.

#!/usr/bin/env python3
"""Stop-hook gate: a draft order cannot leave the harness until every cited
provision verifies against the statute store, every paragraph carries a
page-level source link, and the natural-justice checks pass on the record.

The harness invokes this when the model tries to end its turn. Emitting a
"block" decision returns control to the model with `reason` as its next
instruction; exiting 0 silently lets the turn finish.
"""
import json, sys
from collectorate.verify import (
    unverified_provisions,   # MLRC as amended through the cut-off
    unsourced_paragraphs,    # every finding needs a page citation
    natural_justice_breaches, # notice served, hearing on the Roznama
)

payload = json.load(sys.stdin)

# Honour the forced-continuation flag, or this gate loops forever.
if payload.get("stop_hook_active"):
    sys.exit(0)

draft = payload["last_output"]
problems = (unverified_provisions(draft)
            + unsourced_paragraphs(draft)
            + natural_justice_breaches(draft))
if problems:
    print(json.dumps({
        "decision": "block",
        "reason": "Resolve before finishing:\n- " + "\n- ".join(problems),
    }))
sys.exit(0)
GuaranteeThe turn cannot end until the draft passes checks the model does not control — and failure re-drives the work, instead of landing on the officer's desk.

cited provisions, source links, notice & hearing, reasons, and data egress.

Loops

Conditional continuation

governs iteration

A loop construct runs a task repeatedly until a satisfactory outcome is reached. It is not deterministic on its own — but it raises the floor of what the office can expect, and it composes naturally with hooks: the hook is the oracle, the loop is the driver. The honest caveat is that a loop is only as trustworthy as its verifier; a loop without a real test simply burns time.

  • Draft order, until clean. Redraft until the provision verifier, the source-link gate and the reasoned-order gate all report clean — convergence on verifiable properties, not a single best-effort pass that the officer must then debug. orders
  • Case timeline, until sourced. Refine the reconstructed history of the case — notices, hearings, mutations, prior orders — until every event links to its page in the digitised file; no orphan facts surviving into the brief the officer relies on. timeline
  • Hearing record, until complete. Iterate transcription and structuring until every hearing on the cause-list has its Roznama entry, participant log and order sheet attached to the right case. roznama
GuaranteeThe harness keeps working until a stated condition holds — as trustworthy as the condition you can verify, and no more.

that a draft is only “finished” once a named test is satisfied.

Wrappers

Orchestration determinism

governs the whole workflow

A custom wrapper drives the harness through its API or CLI — the most deterministic surface, because the control flow lives in code the office owns rather than in the model's discretion. A first headless call returns a session identifier; subsequent calls resume that session, preserving full context across turns, so an external program can hold a case open from registration to disposal, insert hard human gates between phases, and bound iteration. The model supplies capability; the wrapper supplies the guarantee that nothing issues from the office unsigned.

  • Officer sign-off gate. “AI shall not issue orders” made structural: the issue step is unreachable in code without the Additional Collector's recorded approval. The AI drafts; the workflow makes it impossible for a draft to become an order any other way. sign-off
  • Role-routed workflow. Record-room staff digitise, scrutiny staff run checklists, hearing staff capture Roznamas, the officer decides — the wrapper enforces who can trigger what, so case-wise, role-based permissioning is workflow fact rather than access-policy aspiration. roles
  • Objective docket record. The wrapper emits a structured, real-time view of every appeal — what ran, what is pending, what is deficient, what is overdue — so the Collector supervises on an objective record rather than on what a monthly return chooses to report. supervision
  • Tamper-evident custody. The wrapper owns the thread that drives every document pull and every edit, writing each to an append-only audit trail — the chain of custody that lets a digitised record stand behind the certificate an electronic record needs in evidence. audit
"""Deterministic envelope around the harness for an appeal file.

The office — not the model — owns the control flow: it opens a session,
holds it open by session id across turns, and makes the "issue" step
structurally unreachable without the Additional Collector's sign-off.
"""
import json, subprocess

def run(prompt, session=None):
    """Run one turn of the harness; return its parsed JSON result.

    :param prompt: the operator instruction for this turn.
    :param session: a session id to resume, or None to open a new case thread.
    :returns: the result dict, including `session_id` for continuation.
    """
    cmd = ["claude", "-p", prompt, "--output-format", "json", "--max-turns", "8"]
    if session:
        cmd += ["--resume", session]
    return json.loads(subprocess.run(cmd, capture_output=True, text=True).stdout)

draft = run("Draft the order for appeal 118/A-247/2026. Do not issue anything.")
sid = draft["session_id"]

# The order cannot issue without the competent authority's approval.
if additional_collector_signs_off(draft["result"]):
    run("Finalise the approved order and enter it in the case record.", session=sid)
GuaranteeThe entire control flow — gates, approvals, supervision — lives in code the office owns; the model is a callable step inside it, never the thing that decides what issues.

what leaves the office as an order, and who signed it.

05 / THE DISCRETION QUESTION

What the harness can and cannot do to the officer's judgment

We will be straight about this, because it is the question every deciding officer should ask first. A determinism harness does not weigh evidence, does not prefer one witness's 7/12 story over another's, and does not decide an appeal. Judicial discretion — the appreciation of the record, the balancing of equities, the decision itself — belongs to the competent authorityThe pilot's own words“AI shall not independently decide any case. AI shall not issue orders. Every final decision shall remain with the competent authority.” The Kerala High Court's district-judiciary AI policy draws the same line: AI is never a substitute for decision-making or legal reasoning., and no surface on this spectrum touches it. That is not a limitation of the design; it is the design.

What the harness does is supply the substrate that discretion has always deserved: a complete record, verified citations, a sourced brief, a hearing history that is actually on the file, and a draft whose every line can be traced to its page. The officer's judgment gets spent on judging — not on finding, checking, and repeating. And because every control is code, the office holds a guaranteed, auditable account of which checks ran on which file: the evidence on which the order's integrity can stand when it is tested in revision or under Article 227. The discretion remains the officer's; the harness makes it defensible.

06 / COMPOSITION

Deterministic skeleton, stochastic muscle

These surfaces are not alternatives; they stack. A command routes the clerk into the office's procedure, a script performs the limitation arithmetic that must come out identically, a hook verifies the draft and re-drives on failure, and a wrapper holds the officer's signature around the whole exchange. The model keeps its fluency in the gaps between — reading a trilingual 687-page record, summarising it, drafting the first version of an order — which is exactly where fluency is worth having and where a wrong first draft is cheap to catch.

We are not making the AI deterministic. We are deciding, surface by surface, which things in an appeal the AI is no longer permitted to get wrong.

It is worth being precise about the claim. Generation stays stochastic; the model still improvises a draft. What the office gains is guaranteed invariants at the points that carry legal consequence — islands of determinism around a fluent core. That is a more honest promise than “trustworthy AI,” and a far more useful one to an authority whose every order must survive appellate scrutiny.

07 / THE PILOT, READ THROUGH THIS LENS

The Nagpur modules already sort onto the spectrum

The pilot proposed for the Additional Collector's office — ten modules over ~1,500 live land-dispute appeals, deployed in 12–16 weeks — was not written in this vocabulary, but it decomposes into it almost without remainder. Reading the module list against the spectrum shows which guarantees the pilot already implies, and where the discipline must be made explicit rather than assumed.

Pilot module
Surface it lives on
M·01Case registration — structured metadata, tracked from filing
Commands
M·02Digitisation — scanning, OCR, trilingual indexing
Scripts
M·03AI document intelligence — classification, semantic search
The stochastic core
M·04Document deficiency — checklist-driven flags for missing records
Scripts · Hooks
M·05Hearing management — cause-lists, recording, order sheets
Commands · Scripts
M·06Case summary — officer-ready briefs from the indexed record
Loops · Hooks
M·07Draft order — source-linked clauses, edited by the officer
Loops · Hooks
M·08Dashboard — pendency, upcoming hearings, disposal
Scripts · Wrappers
M·09User management — role-based, case-wise access
Wrappers
M·10Audit trail — every action, edit and access logged
Wrappers

Two readings follow. First, the pilot's core AI modules — document intelligence, summaries, draft orders — are exactly the stochastic muscle this paper says to keep stochastic; their value is fluency across Marathi, Hindi and English at a scale no officer can match. Second, everything around them that the proposal promises as a feature — source-linked clauses, deficiency flags, human approval, audit logs — is only a guarantee if it is implemented out-of-band: as the command, script, hook and wrapper surfaces above, where the model cannot paraphrase its way past them. The distance between a feature and a guarantee is the distance between a demo and an institution.

08 / WHY IT MATTERS HERE

The control surface is the audit surface

A revisional authority, a High Court under Article 227, an RTI applicant and a departmental audit do not ask for cleverness. They ask whether the office can show, after the fact, that the rule held — that notice was served, that the record was complete, that the cited provision exists as amended, that the officer and not the machine decided. Every surface on this spectrum is code — a command definition, a frozen script, a hook, an orchestration wrapper — and code is versionable, reviewable, signable and attestable. The same move that makes a control tamper-resistant against the model makes it legible to the Collector, to the appellate chain, and to the citizen whose land is in dispute.

For a quasi-judicial office adopting AI in the middle of India's fabricated-citation moment — with the Supreme Court calling unverified AI authorities misconduct and the first High Court policies drawing hard lines around judicial AI use — that shift from hoping the AI behaved to demonstrating the envelope it ran inside is the whole point. It is how the office sees its own compliance posture, how the Collector supervises work they cannot personally re-read, and how the officer who signs the order is finally equipped to stand behind it. Gradual determinism is how a Collectorate adopts AI without surrendering the authority, discretion and responsibility that make it a court.