EU AI Act legacy integration patterns for enterprise AI agents — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · EU AI Act · 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 EU AI Act, and the question it actually asks is what risk class is this system, and where is the technical documentation. The specific problem here is mainframe integration. The shape below holds p95 ≤ 2400ms over outbound webhooks with signed payloads, and ships with a standard-library implementation that runs as written.

The architecture friction point

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 EU AI Act specifically requires

The EU AI Act regulates the SYSTEM, not the data. The first question is its risk classification, and a high-risk classification pulls in Article 11 technical documentation, Article 12 record-keeping and Article 14 human oversight — obligations that have to be designed in, not attached later.

The artifact that satisfies it is a model card plus Article 12 automatic logs. The way teams most often fail it: a system classified as limited-risk that is actually high-risk because it informs a decision about a person.

What the auditor will ask

• What is this system's risk classification under Annex III, who decided, and what happens to that classification when the use case changes?
  The classification is recorded against its Annex III category with a named owner, and a material change of use triggers re-classification before the change ships.
• 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 outbound webhooks with signed payloads; the failure mode it guards against is at-least-once delivery handled as exactly-once by the consumer. The output you hand the assessor is the Annex III classification record and model card.

"""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 EU AI Act control, as code

Annex IV documentation completeness and Article 12 logging. This is the artifact an assessor asks for under EU AI Act 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.

"""Annex IV technical documentation completeness + Article 12 logging.

A high-risk system under the EU AI Act needs technical documentation covering
specified elements, and automatic recording of events over the system lifetime.
The failure is rarely refusal to document — it is documentation that is stale,
partial, or cannot be tied to the exact model version that produced an output.

This checks the documentation set against the Annex IV elements and refuses to
mark a release conformant while any element is missing or stale.
"""
from datetime import date, timedelta

ANNEX_IV_ELEMENTS = {
    "general_description": "intended purpose, provider, version",
    "design_specification": "architecture, algorithmic logic, design choices",
    "training_data": "provenance, scope, characteristics, labelling",
    "human_oversight": "measures enabling natural-person oversight",
    "accuracy_metrics": "accuracy, robustness and cybersecurity metrics",
    "risk_management": "risk management system per Article 9",
    "change_log": "changes made through the lifecycle",
    "post_market_plan": "post-market monitoring plan",
}

STALENESS_DAYS = 180


class TechnicalFile:
    def __init__(self, system: str, model_version: str, today: date):
        self.system, self.model_version, self.today = system, model_version, today
        self.elements: dict[str, date] = {}

    def attach(self, element: str, updated: date) -> None:
        if element not in ANNEX_IV_ELEMENTS:
            raise KeyError(f"{element} is not an Annex IV element")
        self.elements[element] = updated

    def missing(self) -> list[str]:
        return sorted(set(ANNEX_IV_ELEMENTS) - set(self.elements))

    def stale(self) -> list[str]:
        cutoff = self.today - timedelta(days=STALENESS_DAYS)
        return sorted(k for k, v in self.elements.items() if v < cutoff)

    def conformant(self) -> dict:
        missing, stale = self.missing(), self.stale()
        return {"system": self.system, "model_version": self.model_version,
                "missing": missing, "stale": stale,
                "conformant": not missing and not stale}


class EventLog:
    """Article 12: records must tie an output to the version that produced it."""

    def __init__(self):
        self.events: list[dict] = []

    def record(self, model_version: str, event: str, ref: str) -> None:
        self.events.append({"model_version": model_version,
                            "event": event, "ref": ref})

    def trace(self, ref: str) -> dict | None:
        for e in self.events:
            if e["ref"] == ref:
                return e
        return None

    def untraceable(self, known_versions: set[str]) -> list[str]:
        return [e["ref"] for e in self.events
                if e["model_version"] not in known_versions]


if __name__ == "__main__":
    today = date(2026, 10, 16)
    tf = TechnicalFile("claims-triage", "v4.2.0", today)
    for element in ANNEX_IV_ELEMENTS:
        tf.attach(element, today - timedelta(days=30))
    assert tf.conformant()["conformant"], tf.conformant()

    partial = TechnicalFile("claims-triage", "v4.3.0", today)
    for element in list(ANNEX_IV_ELEMENTS)[:5]:
        partial.attach(element, today - timedelta(days=10))
    partial.attach("change_log", today - timedelta(days=400))
    report = partial.conformant()
    assert not report["conformant"], report
    assert "post_market_plan" in report["missing"], report
    assert "change_log" in report["stale"], report

    log = EventLog()
    log.record("v4.2.0", "inference", "out-9001")
    log.record("v4.1.7", "inference", "out-8800")
    assert log.trace("out-9001")["model_version"] == "v4.2.0"
    assert log.untraceable({"v4.2.0"}) == ["out-8800"]

    print("EU AI Act Annex IV + Article 12: OK")
    print(f"  complete file conformant={tf.conformant()['conformant']}, "
          f"partial missing {len(report['missing'])}, stale {len(report['stale'])}")

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.

Modular implementation framework

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.

Related reference architectures

• Part of legacy system integration with ai agents.
• ISO/IEC 27001:2022 legacy integration patterns for enterprise AI agents covers the adjacent case.
• This pattern also governs HIPAA legacy integration patterns for enterprise AI agents.
• The same control appears in PCI DSS 4.0 legacy integration patterns for enterprise AI agents.
• This pattern also governs SOX legacy integration patterns for enterprise AI agents.
• If this is the blocker in front of you: enterprise AI infrastructure review.