Guarded-Policy Agent in Python: Vollständiges Beispiel

Production-style runnable Beispiel eines Guarded-Policy Agents in Python mit policy-gate allow/deny/rewrite/escalate und transparentem trace/history.

Auf dieser Seite

Kern des Musters (Kurz)
Was Dieses Beispiel Zeigt
Architektur
Projektstruktur
Ausführen
Aufgabe
Code
context.py — request envelope
tools.py - ausfuhrbare Tools
agent.py — proposed actions + final answer
gateway.py — policy boundary + execution boundary
main.py — orchestrate policy-gated execution
Beispielausgabe
Typische stop_reason
Was Hier NICHT Gezeigt Wird
Was Du Als Nachstes Probieren Kannst

Kern des Musters (Kurz)

Guarded-Policy Agent bedeutet: Der Agent kann jede Aktion vorschlagen, aber die Ausfuhrung geht nur durch die Policy-Grenze.

Vor jedem Tool-Call gibt das Policy-Gate eine der Entscheidungen zuruck:

allow
deny
rewrite
escalate

Mehr uber Guarded-Policy Agent

Was Dieses Beispiel Zeigt

die LLM/Agent-Layer schlagt einen Aktionsplan vor (actions)
die Execution-Layer validiert den Action-Vertrag
die Policy-Grenze wendet allow/deny/rewrite/escalate an
runtime execution allowlist existiert als separater Guard-Pfad (in diesem Run nicht ausgelost; wird in einem separaten Experiment gepruft)
escalate: die Policy erstellt eine sichere Aktion, human approval bestatigt die Ausfuhrung
die finale Antwort wird nur aus sicher gewonnenen Observations erstellt

Architektur

agent.py erstellt einen Plan mit vier Aktionen.
gateway.py validiert jede Aktion und trifft die Policy-Entscheidung.
a2 wird als deny (pii_export_blocked) blockiert und nicht ausgefuhrt.
a3 geht zu escalate: die Policy erstellt eine sichere Variante, human approval bestatigt den Scope, danach wird als human_approved ausgefuhrt.
a4 demonstriert rewrite (template/recipient cap) und wird als policy_rewrite ausgefuhrt.
main.py sammelt trace/history und liefert den finalen Brief zuruck.

Projektstruktur

TEXT

agent-patterns/
└── guarded-policy-agent/
    └── python/
        ├── main.py
        ├── gateway.py
        ├── tools.py
        ├── agent.py
        ├── context.py
        ├── README.md
        └── requirements.txt

Ausführen

BASH

git clone https://github.com/AgentPatterns-tech/agentpatterns.git
cd agentpatterns

cd agent-patterns/guarded-policy-agent/python
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Python 3.11+ ist erforderlich.

Variante über export:

BASH

export OPENAI_API_KEY="sk-..."
# optional:
# export OPENAI_MODEL="gpt-4.1-mini"
# export OPENAI_TIMEOUT_SECONDS="60"

python main.py

Variante über .env (optional)

BASH

cat > .env <<'EOF'
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4.1-mini
OPENAI_TIMEOUT_SECONDS=60
EOF

set -a
source .env
set +a

python main.py

Das ist die Shell-Variante (macOS/Linux). Unter Windows ist es einfacher, set-Variablen zu verwenden oder optional python-dotenv.

Aufgabe

Production-Fall:

"Erstelle ein customer-safe Update zu einem P1-Payment-Incident fur US. Blockiere PII export und begrenze Massenkommunikation auf Policy-Ebene."

Code

`context.py` — request envelope

PYTHON

from __future__ import annotations

from typing import Any


def build_request(*, report_date: str, region: str, incident_id: str) -> dict[str, Any]:
    return {
        "request": {
            "report_date": report_date,
            "region": region.upper(),
            "incident_id": incident_id,
        },
        "policy_hints": {
            "status_update_template": "incident_p1_v2",
            "max_recipients_per_send": 50000,
        },
    }

`tools.py` - ausfuhrbare Tools

PYTHON

from __future__ import annotations

import time
from typing import Any


def fetch_incident_snapshot(report_date: str, region: str, incident_id: str) -> dict[str, Any]:
    time.sleep(0.08)
    return {
        "status": "ok",
        "data": {
            "incident_id": incident_id,
            "report_date": report_date,
            "region": region.upper(),
            "severity": "P1",
            "failed_payment_rate": 0.034,
            "chargeback_alerts": 5,
            "affected_checkout_share": 0.27,
            "eta_minutes": 45,
        },
    }


def send_status_update(
    channel: str,
    template_id: str,
    audience_segment: str,
    max_recipients: int,
) -> dict[str, Any]:
    time.sleep(0.05)
    return {
        "status": "ok",
        "data": {
            "channel": channel,
            "template_id": template_id,
            "audience_segment": audience_segment,
            "queued_recipients": int(max_recipients),
            "delivery_id": "upd_20260306_001",
        },
    }


def export_customer_data(fields: list[str], destination: str) -> dict[str, Any]:
    del fields, destination
    time.sleep(0.03)
    return {
        "status": "ok",
        "data": {
            "export_id": "exp_20260306_001",
            "rows": 18240,
        },
    }


def create_manual_review_ticket(reason: str, payload: dict[str, Any]) -> dict[str, Any]:
    time.sleep(0.03)
    return {
        "status": "ok",
        "data": {
            "ticket_id": "pol_20260306_001",
            "reason": reason,
            "payload_keys": sorted(payload.keys()),
        },
    }

`agent.py` — proposed actions + final answer

PYTHON

from __future__ import annotations

from typing import Any


def propose_action_plan(*, goal: str, request: dict[str, Any]) -> dict[str, Any]:
    req = request["request"]
    del goal
    return {
        "actions": [
            {
                "id": "a1",
                "tool": "fetch_incident_snapshot",
                "args": {
                    "report_date": req["report_date"],
                    "region": req["region"],
                    "incident_id": req["incident_id"],
                },
            },
            {
                "id": "a2",
                "tool": "export_customer_data",
                "args": {
                    "fields": ["email", "country", "payment_last4"],
                    "destination": "external_s3",
                },
            },
            {
                "id": "a3",
                "tool": "send_status_update",
                "args": {
                    "channel": "external_email",
                    "template_id": "free_text_v0",
                    "audience_segment": "all_customers",
                    "max_recipients": 120000,
                    "free_text": "We are fully recovered.",
                },
            },
            {
                "id": "a4",
                "tool": "send_status_update",
                "args": {
                    "channel": "status_page",
                    "template_id": "free_text_v0",
                    "audience_segment": "enterprise_active",
                    "max_recipients": 120000,
                },
            },
        ]
    }


def compose_final_answer(
    *,
    request: dict[str, Any],
    state: dict[str, Any],
    policy_summary: dict[str, Any],
) -> str:
    req = request["request"]
    snap = state.get("snapshot") or {}
    delivery = state.get("delivery") or {}

    blocked = ", ".join(policy_summary.get("denied_tools") or []) or "none"
    sent = ""
    if delivery:
        sent = (
            f" Status update queued via {delivery.get('channel')} for {delivery.get('audience_segment')} "
            f"using template {delivery.get('template_id')} to {delivery.get('queued_recipients')} recipients."
        )

    failed_rate = snap.get("failed_payment_rate")
    failed_rate_str = f"{float(failed_rate) * 100:.1f}%" if isinstance(failed_rate, (int, float)) else "?"
    share = snap.get("affected_checkout_share")
    share_str = f"{float(share) * 100:.0f}%" if isinstance(share, (int, float)) else "?"

    return (
        f"Operations brief ({req['region']}, {req['report_date']}): incident {req['incident_id']} remains "
        f"{snap.get('severity', '?')} with failed payments at {failed_rate_str} and "
        f"{snap.get('chargeback_alerts', '?')} chargeback alerts. Affected checkout share is "
        f"{share_str} and ETA is ~{snap.get('eta_minutes', '?')} minutes "
        f"(estimate, subject to change).{sent} Blocked by policy: {blocked}."
    )

`gateway.py` — policy boundary + execution boundary

PYTHON

from __future__ import annotations

from concurrent.futures import ThreadPoolExecutor, TimeoutError as FuturesTimeoutError
from dataclasses import dataclass
from typing import Any, Callable


class StopRun(Exception):
    def __init__(self, reason: str):
        super().__init__(reason)
        self.reason = reason


@dataclass(frozen=True)
class Budget:
    max_seconds: int = 25
    max_actions: int = 8
    action_timeout_seconds: float = 1.2
    max_recipients_per_send: int = 50000


@dataclass(frozen=True)
class Decision:
    kind: str
    reason: str
    enforced_action: dict[str, Any] | None = None


def _normalize_action(raw: Any) -> dict[str, Any]:
    if not isinstance(raw, dict):
        raise StopRun("invalid_action:not_object")

    action_id = raw.get("id")
    tool = raw.get("tool")
    args = raw.get("args")

    if not isinstance(action_id, str) or not action_id.strip():
        raise StopRun("invalid_action:id")
    if not isinstance(tool, str) or not tool.strip():
        raise StopRun("invalid_action:tool")
    if not isinstance(args, dict):
        raise StopRun("invalid_action:args")

    return {
        "id": action_id.strip(),
        "tool": tool.strip(),
        "args": dict(args),
    }


def validate_plan(raw_actions: Any, *, max_actions: int) -> list[dict[str, Any]]:
    if not isinstance(raw_actions, list) or not raw_actions:
        raise StopRun("invalid_plan:actions")
    if len(raw_actions) > max_actions:
        raise StopRun("invalid_plan:too_many_actions")
    return [_normalize_action(item) for item in raw_actions]


def validate_tool_observation(raw: Any, *, tool_name: str) -> dict[str, Any]:
    if not isinstance(raw, dict):
        raise StopRun(f"tool_invalid_output:{tool_name}")
    if raw.get("status") != "ok":
        raise StopRun(f"tool_status_not_ok:{tool_name}")
    data = raw.get("data")
    if not isinstance(data, dict):
        raise StopRun(f"tool_invalid_output:{tool_name}")
    return data


class PolicyGateway:
    def __init__(
        self,
        *,
        allowed_tools_policy: set[str],
        allowed_tools_execution: set[str],
        budget: Budget,
    ):
        self.allowed_tools_policy = set(allowed_tools_policy)
        self.allowed_tools_execution = set(allowed_tools_execution)
        self.budget = budget
        self.allowed_templates = {"incident_p1_v2", "incident_p2_v1"}
        self._pool = ThreadPoolExecutor(max_workers=4)

    def close(self) -> None:
        self._pool.shutdown(wait=False, cancel_futures=True)

    def evaluate(self, *, action: dict[str, Any], state: dict[str, Any]) -> Decision:
        del state
        normalized = _normalize_action(action)
        tool = normalized["tool"]
        args = dict(normalized["args"])

        if tool not in self.allowed_tools_policy:
            return Decision(kind="deny", reason="tool_denied_policy")
        if tool == "export_customer_data":
            return Decision(kind="deny", reason="pii_export_blocked")
        if tool not in self.allowed_tools_execution:
            return Decision(kind="deny", reason="tool_denied_execution")

        if tool != "send_status_update":
            return Decision(kind="allow", reason="policy_pass")

        rewrite_reasons: list[str] = []
        rewritten = dict(args)

        if rewritten.get("template_id") not in self.allowed_templates:
            rewritten["template_id"] = "incident_p1_v2"
            rewrite_reasons.append("template_allowlist")

        raw_recipients = rewritten.get("max_recipients", self.budget.max_recipients_per_send)
        try:
            recipients = int(raw_recipients)
        except (TypeError, ValueError):
            recipients = self.budget.max_recipients_per_send
        if recipients > self.budget.max_recipients_per_send:
            recipients = self.budget.max_recipients_per_send
            rewrite_reasons.append("recipient_cap")
        rewritten["max_recipients"] = recipients

        if "free_text" in rewritten:
            rewritten.pop("free_text", None)
            rewrite_reasons.append("free_text_removed")

        if (
            rewritten.get("channel") == "external_email"
            and rewritten.get("audience_segment") == "all_customers"
        ):
            rewritten["channel"] = "status_page"
            rewritten["audience_segment"] = "enterprise_active"
            enforced = {
                "id": normalized["id"],
                "tool": normalized["tool"],
                "args": rewritten,
            }
            return Decision(
                kind="escalate",
                reason="mass_external_broadcast",
                enforced_action=enforced,
            )

        if not rewrite_reasons:
            return Decision(kind="allow", reason="policy_pass")

        enforced = {
            "id": normalized["id"],
            "tool": normalized["tool"],
            "args": rewritten,
        }
        return Decision(
            kind="rewrite",
            reason=f"policy_rewrite:{','.join(rewrite_reasons)}",
            enforced_action=enforced,
        )

    def dispatch(
        self,
        *,
        tool_name: str,
        tool_fn: Callable[..., dict[str, Any]],
        args: dict[str, Any],
    ) -> dict[str, Any]:
        future = self._pool.submit(tool_fn, **args)
        try:
            raw = future.result(timeout=self.budget.action_timeout_seconds)
        except FuturesTimeoutError as exc:
            raise StopRun(f"tool_timeout:{tool_name}") from exc
        except Exception as exc:  # noqa: BLE001
            raise StopRun(f"tool_error:{tool_name}:{type(exc).__name__}") from exc

        return validate_tool_observation(raw, tool_name=tool_name)

`main.py` — orchestrate policy-gated execution

PYTHON

from __future__ import annotations

import json
import time
import uuid
from typing import Any

from agent import compose_final_answer, propose_action_plan
from context import build_request
from gateway import Budget, PolicyGateway, StopRun, validate_plan
from tools import (
    create_manual_review_ticket,
    export_customer_data,
    fetch_incident_snapshot,
    send_status_update,
)

GOAL = (
    "Prepare a customer-safe operations update for a US payments incident. "
    "Use policy-gated execution and never expose customer PII."
)
REQUEST = build_request(
    report_date="2026-03-06",
    region="US",
    incident_id="inc_payments_20260306",
)

BUDGET = Budget(
    max_seconds=25,
    max_actions=8,
    action_timeout_seconds=1.2,
    max_recipients_per_send=50000,
)

ALLOWED_TOOLS_POLICY = {
    "fetch_incident_snapshot",
    "send_status_update",
    "export_customer_data",
    "create_manual_review_ticket",
}
ALLOWED_TOOLS_EXECUTION = {
    "fetch_incident_snapshot",
    "send_status_update",
    "create_manual_review_ticket",
}

TOOLS: dict[str, Any] = {
    "fetch_incident_snapshot": fetch_incident_snapshot,
    "send_status_update": send_status_update,
    "export_customer_data": export_customer_data,
    "create_manual_review_ticket": create_manual_review_ticket,
}


def simulate_human_approval(*, action: dict[str, Any], reason: str) -> dict[str, Any]:
    # Demo policy: approve only the broadcast-risk escalation after safe rewrite.
    if reason == "mass_external_broadcast":
        return {"approved": True, "action": action, "reason": "approved_with_safe_scope"}
    return {"approved": False, "action": action, "reason": "human_rejected"}


def run_guarded_policy_agent(*, goal: str, request: dict[str, Any]) -> dict[str, Any]:
    run_id = str(uuid.uuid4())
    started = time.monotonic()
    trace: list[dict[str, Any]] = []
    history: list[dict[str, Any]] = []
    state: dict[str, Any] = {"snapshot": None, "delivery": None}

    gateway = PolicyGateway(
        allowed_tools_policy=ALLOWED_TOOLS_POLICY,
        allowed_tools_execution=ALLOWED_TOOLS_EXECUTION,
        budget=BUDGET,
    )

    def stopped(stop_reason: str, *, phase: str, **extra: Any) -> dict[str, Any]:
        payload = {
            "run_id": run_id,
            "status": "stopped",
            "stop_reason": stop_reason,
            "phase": phase,
            "trace": trace,
            "history": history,
        }
        payload.update(extra)
        return payload

    decision_counts = {"allow": 0, "rewrite": 0, "deny": 0, "escalate": 0}
    denied_tools: list[str] = []
    rewritten_tools: list[str] = []
    escalated_tools: list[str] = []

    phase = "plan"

    try:
        if (time.monotonic() - started) > BUDGET.max_seconds:
            return stopped("max_seconds", phase="plan")

        raw_plan = propose_action_plan(goal=goal, request=request)
        actions = validate_plan(raw_plan.get("actions"), max_actions=BUDGET.max_actions)
        phase = "execute"

        for idx, action in enumerate(actions, start=1):
            if (time.monotonic() - started) > BUDGET.max_seconds:
                return stopped("max_seconds", phase="execute")

            decision = gateway.evaluate(action=action, state=state)
            decision_counts[decision.kind] += 1

            trace_item = {
                "step": idx,
                "action_id": action["id"],
                "tool": action["tool"],
                "policy_decision": decision.kind,
                "policy_reason": decision.reason,
                "executed_from": "none",
                "ok": False,
            }
            history_item: dict[str, Any] = {
                "step": idx,
                "proposed_action": action,
                "policy": {"decision": decision.kind, "reason": decision.reason},
            }

            if decision.kind == "deny":
                denied_tools.append(action["tool"])
                trace.append(trace_item)
                history.append(history_item)
                continue

            executed_action = action
            executed_from = "original"
            if decision.kind == "rewrite":
                executed_action = decision.enforced_action or action
                rewritten_tools.append(action["tool"])
                executed_from = "policy_rewrite"
            elif decision.kind == "escalate":
                escalated_tools.append(action["tool"])
                escalation_ticket = gateway.dispatch(
                    tool_name="create_manual_review_ticket",
                    tool_fn=TOOLS["create_manual_review_ticket"],
                    args={"reason": decision.reason, "payload": (decision.enforced_action or action)},
                )
                history_item["escalation_ticket"] = escalation_ticket
                human = simulate_human_approval(
                    action=(decision.enforced_action or action),
                    reason=decision.reason,
                )
                if not human["approved"]:
                    return stopped("policy_escalation_rejected", phase="execute")
                executed_action = human["action"]
                executed_from = "human_approved"
                history_item["human"] = {
                    "approved": True,
                    "reason": human["reason"],
                }

            tool_name = executed_action["tool"]
            tool_fn = TOOLS.get(tool_name)
            if tool_fn is None:
                return stopped(f"tool_unmapped:{tool_name}", phase="execute")

            # Keep the decision in trace even if execution fails later.
            trace.append(trace_item)
            observation = gateway.dispatch(
                tool_name=tool_name,
                tool_fn=tool_fn,
                args=executed_action["args"],
            )
            if tool_name == "fetch_incident_snapshot":
                state["snapshot"] = observation
            elif tool_name == "send_status_update":
                state["delivery"] = observation

            trace_item["executed_from"] = executed_from
            trace_item["ok"] = True

            history_item["executed_action"] = executed_action
            history_item["executed_from"] = executed_from
            history_item["observation"] = observation
            history.append(history_item)

        if not isinstance(state["snapshot"], dict):
            return stopped("missing_required_observation:snapshot", phase="finalize")
        if not isinstance(state["delivery"], dict):
            return stopped("missing_required_observation:send_status_update", phase="finalize")

        aggregate = {
            "report_date": request["request"]["report_date"],
            "region": request["request"]["region"],
            "incident": state["snapshot"],
            "delivery": state["delivery"],
        }
        policy_summary = {
            "decisions": decision_counts,
            "denied_tools": sorted(set(denied_tools)),
            "rewritten_tools": sorted(set(rewritten_tools)),
            "escalated_tools": sorted(set(escalated_tools)),
        }
        answer = compose_final_answer(
            request=request,
            state=state,
            policy_summary=policy_summary,
        )

        trace.append(
            {
                "step": len(actions) + 1,
                "phase": "finalize",
                "ok": True,
            }
        )
        history.append({"step": len(actions) + 1, "action": "finalize"})

        return {
            "run_id": run_id,
            "status": "ok",
            "stop_reason": "success",
            "outcome": "policy_guarded_success",
            "answer": answer,
            "proposed_plan": actions,
            "executed_plan": [
                step["executed_action"]
                for step in history
                if isinstance(step, dict) and isinstance(step.get("executed_action"), dict)
            ],
            "aggregate": aggregate,
            "policy_summary": policy_summary,
            "trace": trace,
            "history": history,
        }
    except StopRun as exc:
        return stopped(exc.reason, phase=phase)
    finally:
        gateway.close()


def main() -> None:
    result = run_guarded_policy_agent(goal=GOAL, request=REQUEST)
    print(json.dumps(result, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()

Was hier am wichtigsten ist (einfach erklart)

der Agent schlagt Aktionen vor, aber die Ausfuhrung hat keinen direkten Zugriff auf Tools
pii_export_blocked zeigt ein Policy-Level-Deny fur riskanten Export
tool_denied_execution bleibt als Runtime-Deny-Pfad verfugbar, wird in diesem Run aber nicht ausgelost
escalate: die Policy erstellt eine sichere Aktionsversion, human approval bestatigt den Scope, danach Ausfuhrung als human_approved
trace/history machen Policy-Entscheidungen auditierbar
gateway.close() wird in finally aufgerufen, damit der Pool nicht offen bleibt

Beispielausgabe

JSON

{
  "run_id": "62537585-a465-4b35-bbd8-40153df722eb",
  "status": "ok",
  "stop_reason": "success",
  "outcome": "policy_guarded_success",
  "answer": "Operations brief (US, 2026-03-06): incident inc_payments_20260306 remains P1 with failed payments at 3.4% and 5 chargeback alerts. Affected checkout share is 27% and ETA is ~45 minutes (estimate, subject to change). Status update queued via status_page for enterprise_active using template incident_p1_v2 to 50000 recipients. Blocked by policy: export_customer_data.",
  "proposed_plan": [
    {
      "id": "a1",
      "tool": "fetch_incident_snapshot",
      "args": {
        "report_date": "2026-03-06",
        "region": "US",
        "incident_id": "inc_payments_20260306"
      }
    },
    {
      "id": "a2",
      "tool": "export_customer_data",
      "args": {
        "fields": ["email", "country", "payment_last4"],
        "destination": "external_s3"
      }
    },
    {
      "id": "a3",
      "tool": "send_status_update",
      "args": {
        "channel": "external_email",
        "template_id": "free_text_v0",
        "audience_segment": "all_customers",
        "max_recipients": 120000,
        "free_text": "We are fully recovered."
      }
    },
    {
      "id": "a4",
      "tool": "send_status_update",
      "args": {
        "channel": "status_page",
        "template_id": "free_text_v0",
        "audience_segment": "enterprise_active",
        "max_recipients": 120000
      }
    }
  ],
  "executed_plan": [
    {
      "id": "a1",
      "tool": "fetch_incident_snapshot",
      "args": {
        "report_date": "2026-03-06",
        "region": "US",
        "incident_id": "inc_payments_20260306"
      }
    },
    {
      "id": "a3",
      "tool": "send_status_update",
      "args": {
        "channel": "status_page",
        "template_id": "incident_p1_v2",
        "audience_segment": "enterprise_active",
        "max_recipients": 50000
      }
    },
    {
      "id": "a4",
      "tool": "send_status_update",
      "args": {
        "channel": "status_page",
        "template_id": "incident_p1_v2",
        "audience_segment": "enterprise_active",
        "max_recipients": 50000
      }
    }
  ],
  "aggregate": {
    "report_date": "2026-03-06",
    "region": "US",
    "incident": {
      "incident_id": "inc_payments_20260306",
      "report_date": "2026-03-06",
      "region": "US",
      "severity": "P1",
      "failed_payment_rate": 0.034,
      "chargeback_alerts": 5,
      "affected_checkout_share": 0.27,
      "eta_minutes": 45
    },
    "delivery": {
      "channel": "status_page",
      "template_id": "incident_p1_v2",
      "audience_segment": "enterprise_active",
      "queued_recipients": 50000,
      "delivery_id": "upd_20260306_001"
    }
  },
  "policy_summary": {
    "decisions": {
      "allow": 1,
      "rewrite": 1,
      "deny": 1,
      "escalate": 1
    },
    "denied_tools": [
      "export_customer_data"
    ],
    "rewritten_tools": [
      "send_status_update"
    ],
    "escalated_tools": [
      "send_status_update"
    ]
  },
  "trace": [
    {
      "step": 1,
      "action_id": "a1",
      "tool": "fetch_incident_snapshot",
      "policy_decision": "allow",
      "policy_reason": "policy_pass",
      "executed_from": "original",
      "ok": true
    },
    {
      "step": 2,
      "action_id": "a2",
      "tool": "export_customer_data",
      "policy_decision": "deny",
      "policy_reason": "pii_export_blocked",
      "executed_from": "none",
      "ok": false
    },
    {
      "step": 3,
      "action_id": "a3",
      "tool": "send_status_update",
      "policy_decision": "escalate",
      "policy_reason": "mass_external_broadcast",
      "executed_from": "human_approved",
      "ok": true
    },
    {
      "step": 4,
      "action_id": "a4",
      "tool": "send_status_update",
      "policy_decision": "rewrite",
      "policy_reason": "policy_rewrite:template_allowlist,recipient_cap",
      "executed_from": "policy_rewrite",
      "ok": true
    },
    {
      "step": 5,
      "phase": "finalize",
      "ok": true
    }
  ],
  "history": [{...}]
}

trace enthalt keine Parameter-Diffs; sichere Args stehen in history[*].executed_action.args.

Typische `stop_reason`

success - Run wurde erfolgreich abgeschlossen
max_seconds - gesamtes Time-Budget aufgebraucht
invalid_plan:* - ungueltiger Aktionsplan
invalid_action:* - ungueltiger Action-Vertrag
tool_timeout:* - Tool hat nicht rechtzeitig geantwortet
tool_error:* - Exception wahrend Tool-Ausfuhrung
tool_status_not_ok:* - Tool gab status != "ok" zuruck
tool_invalid_output:* - Tool gab einen ungueltigen Vertrag zuruck
tool_unmapped:* - Tool nicht in der Dispatch-Map gefunden
policy_escalation_rejected - human approval hat die Eskalation abgelehnt
missing_required_observation:* - kritische Daten fur Finalisierung fehlen

Was Hier NICHT Gezeigt Wird

persisted policy/event logs (DB/SIEM)
role/tenant-scoped policy packs
dynamic risk scoring (beyond static rules)
async approval queue (Jira/ServiceNow/Slack workflow)

Was Du Als Nachstes Probieren Kannst

send_status_update aus ALLOWED_TOOLS_EXECUTION entfernen und beobachten: policy_decision=deny (tool_denied_execution) -> final stop_reason=missing_required_observation:send_status_update.
Eine zweite Eskalations-Policy hinzufugen (z. B. fur eta_minutes > 120) und trace prufen.
Policy-Reasons in strukturierte Codes aufteilen (policy.mass_broadcast, policy.pii_export) fur einfachere Analytics.

Code-Execution Agent in Python: Vollständiges Beispiel

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Fallback-Recovery Agent in Python: Vollständiges Beispiel

Code-Execution Agent in Python: Vollständiges Beispiel

⏱️ 12 Min. Lesezeit • Aktualisiert 6. März 2026Schwierigkeit: ★★☆

Von Patterns genutzt

Erforderliche Governance

Integriert: Production ControlOnceOnly

Port this example to production with runtime guardrails

Keep example logic, then add policy + dedupe + run timeline without rewriting the architecture.

Budgets (Steps / Spend Caps)
Tool-Permissions (Allowlist / Blocklist)
Kill switch & Incident Stop
Idempotenz & Dedupe
Audit logs & Nachvollziehbarkeit

OnceOnly testen Doku & Beispiele

Integrierter Hinweis: OnceOnly ist eine Control-Layer für Production-Agent-Systeme.

Autor

Nick — Engineer, der Infrastruktur für KI-Agenten in Produktion aufbaut.

Fokus: Agent-Patterns, Failure-Modes, Runtime-Steuerung und Systemzuverlässigkeit.

🔗 GitHub: https://github.com/mykolademyanov

Redaktioneller Hinweis

Diese Dokumentation ist KI-gestützt, mit menschlicher redaktioneller Verantwortung für Genauigkeit, Klarheit und Produktionsrelevanz.

Die Beispiele dienen Lernzwecken und können simulierte Tools und Daten verwenden. Prüfen Sie vor dem Produktionseinsatz Zuverlässigkeit, Sicherheit und Wiederherstellung in Ihrer eigenen Umgebung.

Guarded-Policy Agent in Python: Vollständiges Beispiel

Kern des Musters (Kurz)

Was Dieses Beispiel Zeigt

Architektur

Projektstruktur

Ausführen

Aufgabe

Code

context.py — request envelope

tools.py - ausfuhrbare Tools

agent.py — proposed actions + final answer

gateway.py — policy boundary + execution boundary

main.py — orchestrate policy-gated execution

Was hier am wichtigsten ist (einfach erklart)

Beispielausgabe

Typische stop_reason

Was Hier NICHT Gezeigt Wird

Was Du Als Nachstes Probieren Kannst

Von Patterns genutzt

Verwandte Failures

Erforderliche Governance

Autor

Redaktioneller Hinweis

`context.py` — request envelope

`tools.py` - ausfuhrbare Tools

`agent.py` — proposed actions + final answer

`gateway.py` — policy boundary + execution boundary

`main.py` — orchestrate policy-gated execution

Typische `stop_reason`