Stop reviewing
what was already
safe to run.

Correct in every way, and still refused.

A limit is not a second opinion on one action. It is a ceiling on the total, checked after everything else has already said yes. Here is one agent with €1,100 of refund authority for the day.

There is no approval card on that last one, by design. A cap that produces a request someone clicks through at the end of a long day is not a cap. More room is a raised limit, which leaves a record of somebody raising it.

None of that is visible when it works, which is why it is usually written last and discovered in production. It is the part we built, and the point of building it is not the refusal. It is that the other ninety-nine go through without anyone reading them.

Different frameworks. Same reality.

Keep Agno, LangGraph, OpenAI, Google ADK, Strands, n8n, or your own stack. KIFF Guard connects their pre-execution seam to the same operational domain, so state, rules, and history survive every model and framework change.

pip install kiff-guard   # or: npm i @kiff/kiff-guard

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.agno import agno_hook

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="agno")

agent = Agent(model=..., tools=[refund_order],
              tool_hooks=[agno_hook(guard)])   # decides before the tool runs

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.langgraph import kiff_wrap_tool_call

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="langgraph")

agent = create_agent(model=..., tools=[refund_order],
                     middleware=[kiff_wrap_tool_call(guard)])

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.openai_agents import kiff_tool_input_guardrail

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="openai-agents")

@function_tool(tool_input_guardrails=[kiff_tool_input_guardrail(guard)])
def refund_order(order_id: str, amount: int, reason: str): ...

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.google_adk import kiff_before_tool_callback

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="google-adk")

agent = Agent(tools=[refund_order],
              before_tool_callback=kiff_before_tool_callback(guard))

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.pydantic_ai import kiff_before_tool_execute

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="pydantic-ai")

agent = Agent(model=...,
              before_tool_execute=kiff_before_tool_execute(guard))

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.strands import kiff_hook_provider

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="strands")

agent = Agent(model=..., tools=[refund_order],
              hooks=[kiff_hook_provider(guard)])

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.microsoft_agent_framework import kiff_guard_middleware

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="ms-agent-framework")

agent = Agent(tools=[refund_order],
              middleware=[kiff_guard_middleware(guard)])

from kiff_guard import Guard, HTTPClient, ToolMap
from kiff_guard.adapters.hermes import register_kiff_guard

tm = ToolMap().bind("refund_order", action="REFUND_ORDER",
                    entity_type="Order", entity_arg="order_id")
guard = Guard(client=HTTPClient(api_key=KEY, tool_map=tm),
              tenant="acme", agent="refunds", mode="enforce")
guard.connect(adapter="hermes")

register_kiff_guard(ctx, guard)   # in your Hermes plugin's register()

import { Guard, HTTPClient, ToolMap } from "@kiff/kiff-guard";
import { registerKiffGuard } from "@kiff/kiff-guard/adapters/openclaw";

const tm = new ToolMap().bind("refund_order", {
  action: "REFUND_ORDER", entityType: "Order", entityArg: "order_id" });
const client = new HTTPClient({ apiKey: KEY, toolMap: tm });
const guard = new Guard({ client, tenant: "acme", agent: "refunds", mode: "enforce" });

registerKiffGuard(ctx, guard);   // in your OpenClaw plugin

# No adapter needed. Wrap the one function that moves money.
def issue_refund(order, amount):
    d = kiff.decide("REFUND_ORDER", entity=order, amount=amount)
    if not d.allowed:
        return d                         # blocked or held, never execute
    payments.refund(order, amount)       # your code, unchanged

# No SDK. Any language. POST the proposed action; act only on "allowed".
curl -s https://api.kiff.dev/v1/proposals/decide \
  -H "Authorization: Bearer $KIFF_KEY" -H "Content-Type: application/json" \
  -d '{"id":"rd-4471","entity_id":"order-4471","entity_type":"Order",
       "action_name":"REFUND_ORDER","actor_id":"refunds",
       "parameters":{"amount":8400,"reason":"damaged"}}'
# -> {"outcome":"allowed"}   then POST .../execute for a signed receipt

The prompt changes. Reality does not.

Connect the execution point once. After the order is refunded, every later actor meets the same state and the same valid actions, regardless of what its model remembers or reasons.