AI governance for financial decisions.

from kevros_governance import GovernanceClient client = GovernanceClient(agent_id="trading-bot-001") # Verify authorization before executing a trade result = client.verify( action_type="trade", action_payload={ "symbol": "BTC-USD", "side": "BUY", "quantity": 2.5, "price_limit": 62000.00, "exchange": "coinbase", }, agent_id="trading-bot-001", policy_context={ "max_values": {"quantity": 10.0, "price_limit": 100000.00}, }, ) if result.decision.value == "ALLOW": print(f"Trade authorized: {result.release_token}") execute_trade(token=result.release_token) elif result.decision.value == "CLAMP": print(f"Trade clamped: {result.applied_action}") execute_trade(token=result.release_token, params=result.applied_action) elif result.decision.value == "DENY": print(f"Trade denied: {result.reason}") alert_risk_team(result)

from kevros_governance import GovernanceClient, IntentType client = GovernanceClient(api_key="kvrs_your_key_here") # 1. VERIFY — Is this trade authorized? verify = client.verify( action_type="trade", action_payload={"symbol": "ETH-USD", "side": "SELL", "quantity": 50}, agent_id="algo-trader-003", ) # 2. BIND — Declare intent and bind to the trade command bind = client.bind( agent_id="algo-trader-003", intent_type=IntentType.AI_GENERATED, intent_description="Sell 50 ETH based on momentum reversal signal", command_payload={"symbol": "ETH-USD", "side": "SELL", "quantity": 50}, goal_state={"position_delta": -50, "asset": "ETH"}, ) # 3. Execute the trade (your exchange logic) fill = execute_trade_on_exchange(verify.release_token) # 4. ATTEST — Record what actually happened attest = client.attest( agent_id="algo-trader-003", action_description="Sold 50 ETH on Coinbase at $3,412.50", action_payload={ "symbol": "ETH-USD", "side": "SELL", "quantity": 50, "filled_price": 3412.50, "order_id": fill.order_id, }, ) # 5. VERIFY OUTCOME — Did the result match the intent? outcome = client.verify_outcome( agent_id="algo-trader-003", intent_id=bind.intent_id, binding_id=bind.binding_id, actual_state={"position_delta": -50, "asset": "ETH"}, ) print(f"Chain length: {attest.chain_length}") print(f"Outcome: {outcome.status}") # ACHIEVED

# Generate audit-ready evidence for a regulatory review bundle = client.bundle( agent_id="algo-trader-003", time_range_start="2026-01-01T00:00:00Z", time_range_end="2026-03-31T23:59:59Z", include_intent_chains=True, include_pqc_signatures=True, include_verification_instructions=True, ) print(f"Records: {bundle.record_count}") print(f"Chain integrity: {bundle.chain_integrity}") # Present bundle to SEC, FINRA, or internal compliance

from langchain_openai import ChatOpenAI from langchain.agents import initialize_agent, AgentType from kevros_tools import get_identity_tools # Governance tools for your trading agent tools = get_identity_tools(agent_id="langchain-trader") llm = ChatOpenAI(model="gpt-4", temperature=0) agent = initialize_agent( llm=llm, tools=tools, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, ) # The agent calls kevros_verify before executing trades # and kevros_attest after — building a provenance chain response = agent.run( "Verify authorization to buy 100 shares of AAPL at market, " "execute the trade, then record the fill in the provenance ledger." )