EU AI Act agent permission and delegated access architecture for enterprise LLM deployment — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · EU AI Act · layout diagram_first · implementation executed in CI before publish

The control that resolves this is an Intersecting Delegated Authority, 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 shape below holds p95 ≤ 600ms over REST over HTTPS, and ships with a standard-library implementation that runs as written.

The blueprint

Intersecting Delegated Authority — the request path, with the decision points that must exist before the first model call:

flowchart LR
    USER[Invoking user<br/>entitlements] --> INT{Intersect}
    GRANT[Agent grant<br/>time-boxed] --> INT
    INT --> EFF[Effective permissions]
    EFF --> ACT{Action in effective set?}
    ACT -->|no| DENY[Deny + audit reason]
    ACT -->|yes| DO[Execute as the user, not the agent]
    EFF --> HOP[Delegate to agent B] --> INT2{Intersect again} --> EFF2[Narrower still]

Component breakdown

1. Principal entitlements — What the human may do, from the system of record.
2. Agent grant — What the agent may ever do, scoped per task.
3. Intersection at call time — Effective = user ∩ grant, computed per request.
4. Time-boxed grants — Expiry enforced, so delegation is not standing authority.
5. Chain intersection — Agent-to-agent delegation intersects again; a hop cannot widen.
6. Denial audit — Every denial recorded with the reason — the escalation signal.

Implementation parameter matrix

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

The architecture friction point

The agent is given a service account so it can complete its work, and it now acts with more authority than any human who invokes it. An intern's request and an administrator's request reach the same powerful identity.

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.

Reference implementation — Delegated authority as an intersection, with expiry

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 REST over HTTPS; the failure mode it guards against is a retried POST creating a duplicate record. The output you hand the assessor is the Annex III classification record and model card.

"""Delegated authority: an agent never exceeds the user it acts for.

The failure: an agent is given a service account so it can "do its job", and it
then acts with more authority than any user who invokes it. Correct delegation
is an intersection — the agent may do only what BOTH its own grant and the
invoking user permit — and it must be time-boxed, because a standing delegation
is a standing privilege.
"""
from dataclasses import dataclass


@dataclass(frozen=True)
class Grant:
    subject: str
    permissions: frozenset
    expires_at: int

    def valid(self, now: int) -> bool:
        return now < self.expires_at


class DelegationError(PermissionError):
    pass


class DelegatedAuthority:
    """Effective permissions = user ∩ agent grant, never a union, and never
    the agent's own service identity."""

    def __init__(self):
        self.users: dict[str, frozenset] = {}
        self.grants: dict[str, Grant] = {}
        self.audit: list[dict] = []

    def define_user(self, user: str, permissions: set) -> None:
        self.users[user] = frozenset(permissions)

    def delegate(self, agent: str, permissions: set, expires_at: int) -> Grant:
        g = Grant(agent, frozenset(permissions), expires_at)
        self.grants[agent] = g
        return g

    def effective(self, agent: str, user: str, now: int) -> frozenset:
        g = self.grants.get(agent)
        if g is None:
            raise DelegationError(f"no grant for agent {agent!r}")
        if not g.valid(now):
            raise DelegationError(f"grant for {agent!r} expired")
        if user not in self.users:
            raise DelegationError(f"unknown principal {user!r}")
        return g.permissions & self.users[user]

    def authorise(self, agent: str, user: str, action: str, now: int) -> bool:
        try:
            allowed = self.effective(agent, user, now)
        except DelegationError as exc:
            self.audit.append({"agent": agent, "user": user, "action": action,
                               "allowed": False, "reason": str(exc)})
            return False
        ok = action in allowed
        self.audit.append({"agent": agent, "user": user, "action": action,
                           "allowed": ok,
                           "reason": "" if ok else "outside the intersection"})
        return ok

    def chain(self, agent_a: str, agent_b: str, user: str, now: int) -> frozenset:
        """A delegating to B cannot widen authority — chaining intersects
        again, which is what stops escalation through a hop."""
        return self.effective(agent_a, user, now) & self.grants[agent_b].permissions


if __name__ == "__main__":
    da = DelegatedAuthority()
    da.define_user("analyst", {"read_reports", "read_pii"})
    da.define_user("intern", {"read_reports"})
    da.delegate("summariser", {"read_reports", "read_pii", "delete_records"},
                expires_at=100)

    assert da.authorise("summariser", "analyst", "read_pii", now=1) is True
    # The intern cannot read PII, so neither can an agent acting for them.
    assert da.authorise("summariser", "intern", "read_pii", now=1) is False
    # Nobody granted delete to a user, so the agent's own grant cannot supply it.
    assert da.authorise("summariser", "analyst", "delete_records", now=1) is False

    eff = da.effective("summariser", "intern", now=1)
    assert eff == frozenset({"read_reports"}), eff

    # Expiry is enforced, not advisory.
    assert da.authorise("summariser", "analyst", "read_reports", now=101) is False
    assert "expired" in da.audit[-1]["reason"]

    # Chaining must not widen authority.
    da.delegate("exporter", {"read_reports", "export"}, expires_at=100)
    chained = da.chain("summariser", "exporter", "analyst", now=1)
    assert chained == frozenset({"read_reports"}), chained
    assert "export" not in chained, "chaining widened authority"

    assert da.authorise("unknown_agent", "analyst", "read_reports", now=1) is False

    print("Delegated authority intersection: OK")
    print(f"  audit entries {len(da.audit)}, "
          f"denials {sum(1 for a in da.audit if not a['allowed'])}")

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'])}")

Risk mitigation matrix

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.

Questions to answer before approval

• 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.
• If an intern invokes the agent, can it read data only an analyst may see?
  No. Effective permissions are the intersection of the agent's grant and the intern's entitlements, so anything the intern lacks is unavailable regardless of the agent's own grant.
• What authority does the agent have when nobody has invoked it?
  None that matters. The grant is time-boxed and only becomes effective when intersected with an invoking principal, so there is no standing authority to borrow.
• Can agent A delegate to agent B and end up with more permissions than A had?
  No. Chaining intersects again, so each hop can only narrow the set. Widening through a hop is the escalation path this design exists to close.
• How quickly does a revoked entitlement take effect for an in-flight agent?
  Within the entitlement cache TTL, which is bounded by the revocation SLO. The stale-grant window is documented rather than assumed to be zero.
• What is recorded when an agent attempts something outside its effective set?
  The agent, the invoking user, the attempted action and the reason for denial. Repeated denials are the signal that something is probing for authority it does not have.

Related reference architectures

• Part of enterprise ai compliance architecture.
• A different failure mode of the same component: EU AI Act reference architecture for enterprise LLM deployment at scale.
• EU AI Act provider failover and degradation architecture for enterprise covers the adjacent case.
• This pattern also governs EU AI Act legacy integration patterns for enterprise AI agents.
• EU AI Act cost control architecture for enterprise LLM deployment applies the same mechanism under a different obligation.
• If this is the blocker in front of you: enterprise AI infrastructure review.