Open runtime guardrails for AI agents

import uuid, requests

# The identity four-tuple. All four always present; "" = nothing to assert
# (the runtime then derives identity from the API key).
IDENTITY = {
    "agent_id":        "invoice-bot",     # WHICH agent — unique in your org
    "agent_type":      "my-harness",      # what KIND — a label, never policy
    "agent_workspace": "finance-agents",  # agent GROUP — one policy set
    "agent_user":      "u-8232",          # who is USING it this session
}

SESSION = uuid.uuid4().hex   # optional session_hint: one id per conversation —
                             # sessions become declared instead of inferred

def evaluate(kind, step_id, payload):
    """The whole protocol is this one call. Fail-open: no verdict -> proceed."""
    try:
        r = requests.post(f"{OGR}/v1/evaluate",
                          headers={"Authorization": f"Bearer {KEY}"},
                          json={"kind": kind, "step_id": step_id,
                                "llm_protocol": "openai.chat",
                                "session_hint": SESSION,
                                **IDENTITY, "payload": payload},
                          timeout=5)
        return r.json() if r.ok else None
    except requests.RequestException:
        return None

def blocked(v):
    return v is not None and v["decision"] == "block"

# your agent loop, with the two calls added:
while True:
    step_id = uuid.uuid4().hex                    # binds this call's 2 events
    body = {"model": "gpt-5", "messages": messages, "tools": TOOLS}
    if blocked(evaluate("step/request", step_id, body)):   # 1) before the model
        break
    resp = call_llm(body)                                  # your code, unchanged
    if blocked(evaluate("step/response", step_id, resp)):  # 2) before acting
        break
    ...                                           # execute tool calls, loop