SOC 2 Type II legacy integration patterns for enterprise AI agents — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · SOC 2 Type II · layout troubleshooting · implementation executed in CI before publish

The control that resolves this is an Idempotent System-of-Record Bridge, evaluated before the request reaches a model provider rather than audited afterwards. In regulated enterprise the binding constraint is SOC 2 Type II, and the question it actually asks is did this control operate effectively for the whole observation window. The specific problem here is mainframe integration. The shape below holds p95 ≤ 2400ms over scheduled batch file exchange (SFTP), and ships with a standard-library implementation that runs as written.

The control gap

Agents can read the legacy estate and cannot safely write to it. A retry after a gateway timeout creates a duplicate posting, and in an ERP a duplicate journal entry is a finance incident. So the team that owns the system of record withholds write access, and the automation stalls at read-only.

What makes mainframe integration specifically hard

The mainframe is the system of record and its change window is measured in quarters. Anything that requires a change on that side is not an integration plan, it is a request to join a release queue.

What resolves it is a side-car integration that requires zero mainframe change. The failure to design against: a design whose first step is a CICS change nobody will schedule.

What SOC 2 Type II specifically requires

SOC 2 Type II is not a point-in-time assertion. The auditor samples evidence across a 3-12 month observation window and asks whether the control operated on every day of it, not whether it exists today.

The artifact that satisfies it is a sampled, time-ordered population the auditor can draw from. The way teams most often fail it: the control was added mid-window, so the population has a hole and the observation period restarts.

Questions to answer before approval

• Show me the control-execution population for the full window, and the days where it is empty.
  Every request emits an execution record, so the population is continuous by construction. A gap is an alertable incident, not something discovered during the audit.
• What does this require the mainframe team to change, and when is their next window?
  Nothing. All new behaviour lives in the side-car and the existing interface is consumed as-is, so delivery does not depend on their release queue.
• If the gateway times out and the agent retries, how many documents exist — and what proves it?
  One. The idempotency key is derived from the business keys, so both attempts collide on the same claim; the retry observes the completed claim and replays the original result. The transport is invoked once, which the test asserts.
• Which exact fields may an agent write per document type, and what happens to a field outside that list?
  Only the fields in that document type's allow-list, and all of them — the projection rejects both unknown fields and incomplete payloads. A field outside the contract raises rather than posting hopefully.
• What does the system-of-record owner see when a posting is wrong, and what reverses it?
  The reconciliation job compares posted documents against recorded intents and surfaces drift; each writable document type has a defined, rehearsed compensating action for reversal.
• Where is the intent recorded before the transport call, and what resolves a mid-flight crash?
  In the outbox, written before the transport call. On restart, reconciliation resolves anything left in flight, so a crash leaves a recoverable state rather than an ambiguous one.
• Is the extract pulling the minimum field set, with a justification per field?
  It should be — projection applies on read as well as write, with a recorded justification per field, so the extract schema matches the documented minimum field set.

Deployment parameters

Every value below is a parameter you can hold a vendor or an internal team to. Adjectives are not acceptance criteria.

Working implementation — Idempotent write-back with a typed write contract

Standard-library Python, no external dependencies, runs as written. Executed by the publish gate: if it raises, this page does not ship. Wire it behind scheduled batch file exchange (SFTP); the failure mode it guards against is a partial file processed as complete. The output you hand the assessor is the observation-window evidence export.

"""Idempotent write-back adapter for a legacy system of record (SAP/ERP shape).

Why this shape: the blocker on legacy integration is almost never reading. It
is writing. An agent that retries a posting because a gateway timed out will
happily create a duplicate journal entry, and in an ERP a duplicate posting is
a finance incident, not a bug report.

Three controls make agent-driven writes acceptable to the team that owns the
system of record:
  * a deterministic idempotency key derived from business content
  * a claim table, so a retry observes the first attempt instead of repeating it
  * a bounded, typed projection, so the agent can never post a field it does
    not understand
"""
import hashlib
import json
import time
from dataclasses import dataclass


class DuplicatePosting(Exception):
    """Raised when a retry would create a second document for one intent."""


def idempotency_key(doc_type: str, business_keys: dict) -> str:
    """Derived from BUSINESS content, never from a uuid4.

    A random key makes every retry unique, which is the opposite of what
    idempotency means. Two attempts to post the same invoice must collide.
    """
    canonical = json.dumps({k: business_keys[k] for k in sorted(business_keys)},
                           sort_keys=True, separators=(",", ":"))
    return f"{doc_type}:" + hashlib.sha256(canonical.encode()).hexdigest()[:32]


ALLOWED_FIELDS = {
    "journal_entry": {"company_code", "posting_date", "currency",
                      "debit_account", "credit_account", "amount", "reference"},
}


def project(doc_type: str, payload: dict) -> dict:
    """Allow-list projection. An agent-proposed field that is not in the
    contract is dropped loudly, not posted hopefully."""
    allowed = ALLOWED_FIELDS.get(doc_type)
    if allowed is None:
        raise ValueError(f"no write contract defined for {doc_type!r}")
    unknown = set(payload) - allowed
    if unknown:
        raise ValueError(f"fields outside the write contract: {sorted(unknown)}")
    missing = allowed - set(payload)
    if missing:
        raise ValueError(f"write contract requires: {sorted(missing)}")
    return {k: payload[k] for k in sorted(allowed)}


@dataclass
class ClaimStore:
    """Stands in for a real transactional table. In production this is a row
    in the SAME database as your outbox, claimed with SELECT ... FOR UPDATE."""

    def __post_init__(self):
        self._claims: dict = {}

    def claim(self, key: str, ttl: float = 900.0) -> tuple:
        """-> (is_new, existing_result). Atomic in a real DB."""
        now = time.time()
        rec = self._claims.get(key)
        if rec and (rec["status"] == "done" or now - rec["at"] < ttl):
            return False, rec
        self._claims[key] = {"status": "in_flight", "at": now, "result": None}
        return True, None

    def complete(self, key: str, result: dict):
        self._claims[key] = {"status": "done", "at": time.time(), "result": result}

    def fail(self, key: str):
        self._claims.pop(key, None)      # let a genuine retry through


def post_document(store: ClaimStore, doc_type: str, business_keys: dict,
                  payload: dict, transport) -> dict:
    """The only sanctioned path from an agent to the system of record."""
    key = idempotency_key(doc_type, business_keys)
    body = project(doc_type, payload)

    is_new, existing = store.claim(key)
    if not is_new:
        if existing["status"] == "done":
            # The correct response to a duplicate is the ORIGINAL result.
            return {"idempotency_key": key, "replayed": True,
                    **existing["result"]}
        raise DuplicatePosting(f"{key} is already in flight")

    try:
        result = transport(doc_type, body, key)
    except Exception:
        store.fail(key)
        raise
    store.complete(key, result)
    return {"idempotency_key": key, "replayed": False, **result}


if __name__ == "__main__":
    calls = []

    def fake_sap(doc_type, body, key):
        calls.append(key)
        return {"document_number": f"50000{len(calls)}", "posted": True}

    store = ClaimStore()
    keys = {"invoice_id": "INV-8831", "company_code": "1000"}
    payload = {"company_code": "1000", "posting_date": "2026-10-16",
               "currency": "EUR", "debit_account": "400000",
               "credit_account": "113100", "amount": "14500.00",
               "reference": "INV-8831"}

    first = post_document(store, "journal_entry", keys, payload, fake_sap)
    retry = post_document(store, "journal_entry", keys, payload, fake_sap)
    print("first :", first["document_number"], "replayed", first["replayed"])
    print("retry :", retry["document_number"], "replayed", retry["replayed"])
    print("transport invocations:", len(calls), "(must be 1)")
    try:
        project("journal_entry", {**payload, "tax_code": "V1"})
    except ValueError as e:
        print("contract rejects unknown field:", e)

The SOC 2 Type II control, as code

Type II evidence sampling over the observation window. This is the artifact an assessor asks for under SOC 2 Type II specifically — it is not the same control as the one above, and a system that implements one does not thereby implement the other. It executes under the same publish gate.

"""SOC 2 Type II evidence sampling over an observation window.

Type II is not "the control exists" — it is "the control operated throughout
the period". An auditor samples days from the window and asks for proof the
control ran on each sampled day. A gap of one day with no evidence is an
exception, so the population must be built from operational records rather
than asserted.
"""
import hashlib
from datetime import date, timedelta


class ObservationWindow:
    """The population an auditor samples from, plus deterministic selection."""

    def __init__(self, start: date, end: date, control_id: str):
        if end < start:
            raise ValueError("window end precedes start")
        self.start, self.end, self.control_id = start, end, control_id
        self._evidence: dict[date, list[str]] = {}

    def record(self, day: date, artifact_ref: str) -> None:
        if not (self.start <= day <= self.end):
            raise ValueError(f"{day} outside observation window")
        self._evidence.setdefault(day, []).append(artifact_ref)

    def population(self) -> list[date]:
        n = (self.end - self.start).days + 1
        return [self.start + timedelta(days=i) for i in range(n)]

    def gaps(self) -> list[date]:
        """Days with no evidence. Each one is a Type II exception."""
        return [d for d in self.population() if not self._evidence.get(d)]

    def sample(self, size: int) -> list[date]:
        """Deterministic, reproducible selection — an auditor must be able to
        re-derive the same sample from the same seed rather than trust ours."""
        pop = self.population()
        size = min(size, len(pop))
        seed = f"{self.control_id}:{self.start}:{self.end}"
        scored = sorted(
            pop, key=lambda d: hashlib.sha256(f"{seed}:{d}".encode()).hexdigest())
        return sorted(scored[:size])

    def evidence_for(self, day: date) -> list[str]:
        return list(self._evidence.get(day, []))

    def attestation(self, size: int = 25) -> dict:
        sampled = self.sample(size)
        missing = [d for d in sampled if not self._evidence.get(d)]
        return {
            "control_id": self.control_id,
            "window": f"{self.start}..{self.end}",
            "population_days": len(self.population()),
            "sampled_days": len(sampled),
            "exceptions": [str(d) for d in missing],
            "opinion": "unqualified" if not missing else "qualified",
        }


if __name__ == "__main__":
    start, end = date(2026, 1, 1), date(2026, 3, 31)
    w = ObservationWindow(start, end, "CC6.1-tenant-isolation")
    for d in w.population():
        w.record(d, f"s3://evidence/{d}/isolation-check.json")
    clean = w.attestation()
    assert clean["opinion"] == "unqualified", clean
    assert clean["population_days"] == 90, clean

    # One missing day is one exception — and it must survive sampling to matter.
    w2 = ObservationWindow(start, end, "CC6.1-tenant-isolation")
    sampled = set(w2.sample(25))
    for d in w2.population():
        if d != sorted(sampled)[0]:
            w2.record(d, "ok")
    assert w2.attestation()["opinion"] == "qualified", w2.attestation()
    assert len(w2.gaps()) == 1, w2.gaps()

    # The sample must be reproducible from the same seed.
    assert w.sample(25) == ObservationWindow(start, end,
                                             "CC6.1-tenant-isolation").sample(25)
    print("SOC 2 Type II evidence sampling: OK")
    print(f"  population {clean['population_days']} days, "
          f"sample {clean['sampled_days']}, opinion {clean['opinion']}")

The request path

Idempotent System-of-Record Bridge — the request path, with the decision points that must exist before the first model call:

flowchart LR
    AG[Agent] --> PR[Projection<br/>allow-listed fields]
    PR -->|unknown field| REJ[Reject - loud]
    PR --> IK[Idempotency key<br/>sha256 of business keys]
    IK --> CL{Claim table}
    CL -->|already done| RP[Replay original result]
    CL -->|in flight| DUP[Refuse duplicate]
    CL -->|new| OB[(Outbox)]
    OB --> TR[Transport - OData / IDoc / RFC]
    TR --> SOR[(System of record<br/>SAP / ERP / AS400)]
    SOR --> RC[Reconciliation]
    RC -->|drift| CMP[Compensating action]

Risk register with acceptance criteria

Each row carries an acceptance parameter rather than an adjective, because "low latency" cannot be signed off. Retention here is bound by per the data class retention schedule, enforced automatically.

Component breakdown

1. Content-derived idempotency key — Derived from business keys, so two attempts at one intent collide.
2. Claim table — Transactional claim in the same database as the outbox; a retry observes the first attempt.
3. Typed write contract — Allow-listed fields per document type; unknown fields are rejected loudly.
4. Outbox — Durable intent record, so a crash mid-flight is recoverable rather than ambiguous.
5. Reconciliation job — Compares posted documents against intents and surfaces drift.
6. Compensating action — A defined reversal path per document type, because some writes must be undoable.

Adjacent problems

• Part of legacy system integration with ai agents.
• Where this interacts with another regime, see GDPR legacy integration patterns for enterprise AI agents.
• HIPAA legacy integration patterns for enterprise AI agents covers the adjacent case.
• Where this interacts with another regime, see PCI DSS 4.0 legacy integration patterns for enterprise AI agents.
• fedRAMP Moderate legacy integration patterns for enterprise AI agents applies the same mechanism under a different obligation.
• If this is the blocker in front of you: enterprise AI infrastructure review.