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Documentation Index

Fetch the complete documentation index at: https://policykit.xyz/llms.txt

Use this file to discover all available pages before exploring further.

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Agent Wallet Example

This example shows how to set up strict guardrails for an AI agent-controlled wallet. The policy ensures the agent can only perform specific, bounded actions.

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Scenario

You have an AI agent that:
  • Manages a portfolio by executing trades
  • Needs tight spending controls
  • Should not be able to drain the wallet
  • Must wait between actions to prevent rapid-fire trading

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Policy Definition

import { PolicyBuilder } from "@policy-kit/sdk";
import { parseEther, parseUnits } from "viem";

const UNISWAP_ROUTER = "0x68b3465833fb72A70ecDF485E0e4C7bD8665Fc45";
const ONEINCH_ROUTER = "0x1111111254EEB25477B68fb85Ed929f73A960582";
const USDC = "0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48";
const USDT = "0xdAC17F958D2ee523a2206206994597C13D831ec7";
const WETH = "0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2";

const policy = new PolicyBuilder("ai-agent-guardrails")
  // Strict target allowlist — only approved DEXes and tokens
  .allowTargets([UNISWAP_ROUTER, ONEINCH_ROUTER, USDC, USDT, WETH])

  // Only allow swap functions — no approvals, no admin functions
  .allowSelectors([
    "0x38ed1739", // swapExactTokensForTokens
    "0x7ff36ab5", // swapExactETHForTokens
    "0x12aa3caf", // 1inch swap
  ])

  // Block dangerous selectors explicitly
  .denySelectors([
    "0x095ea7b3", // approve
    "0x39509351", // increaseAllowance
  ])

  // Max 0.5 ETH per transaction (small trades only)
  .maxValue(parseEther("0.5"))

  // Daily spending limits
  .spendLimit(USDC, parseUnits("5000", 6), 86400)  // 5k USDC/day
  .spendLimit(USDT, parseUnits("5000", 6), 86400)  // 5k USDT/day

  // 10 minute cooldown — prevents rapid trading
  .cooldown(600)

  // Strict slippage (0.3%)
  .maxSlippageBps(30)

  // Must simulate before executing
  .requireSimulation(true)

  // Never allow transactions if Lit is down
  .setFailMode("closed")

  .build();

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Key Design Decisions

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Why strict target allowlisting?

AI agents can be manipulated through prompt injection or adversarial inputs. A strict allowlist ensures the agent can only interact with known, trusted contracts — even if the agent’s reasoning is compromised.

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Why deny approvals?

ERC-20 approve calls are one of the most dangerous operations. A malicious or confused agent could approve a drainer contract for unlimited tokens. By explicitly denying approval selectors, this attack vector is eliminated.

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Why cooldowns?

If an agent enters a bad loop or is manipulated into rapid trading, the cooldown prevents it from draining the wallet through many small transactions. A 10-minute cooldown limits damage.

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Why closed fail mode?

For an AI agent, security trumps availability. If Lit Protocol is unreachable and off-chain rules can’t be checked (slippage, simulation), it’s better to block the transaction than risk executing without guardrails.

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Deployment

const pk = new PolicyKit({
  publicClient,
  walletClient,
  engineAddress: POLICY_ENGINE_ADDRESS,
  ipfsBackends: [{ type: "pinata", jwt: process.env.PINATA_JWT }],
  litConfig: {
    network: "naga",
    litActionCID: process.env.LIT_ACTION_CID,
  },
});

await pk.deployPolicy(policy);

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Monitoring

After deployment, use the CLI to inspect the active policy: 
policykit inspect --account 0xAgentWallet --chain base-sepolia --full
You can also simulate transactions the agent might attempt: 
policykit simulate \
  --policy ./agent-policy.json \
  --target 0x68b3465833fb72A70ecDF485E0e4C7bD8665Fc45 \
  --value 0.1 \
  --data 0x38ed1739... \
  --verbose