MandateGuard
Deterministic, auditable payment policy for autonomous AI agents.
MandateGuard is a pre-action enforcement layer that sits between an agent and its tools/wallet. Every tool call that moves money is evaluated by a pure, deterministic engine — budgets, allowlists, denylists, rate limits, and signed mandates — before anything executes. No LLM is ever in the decision path, which is exactly what makes every verdict reproducible and every ledger entry verifiable.
It ships with an MCP server so any agent (Claude, Cursor, or your own harness) can mount it as a guardrail in minutes.
Why
The 2026 agentic-economy reality:
- OWASP LLM08 — Excessive Agency is one of the top LLM app risks. Agents given wallets are getting drained: the SoK on agentic commerce documents $40M+ in real losses (drain attacks, memory poisoning, tool abuse).
- Payment standards (Google AP2's Intent/Cart/Payment mandates, Coinbase x402, ERC-8004) define what a mandate is — but none of them ship the enforcement layer that actually blocks an agent mid-flight.
- Gartner: 40% of enterprise apps will embed agents by end of 2026. Those agents will move money. They need rails.
The market gap: a deterministic (non-LLM) policy engine + audit trail + MCP distribution. That is this repo.
Features
- Deterministic engine — same inputs, same verdict, always. Auditable by replay, no model sampling in the decision.
- Per-actor scopes — allowed tools, allowed destinations, per-call max, currency, per-window call limits.
- Global guards — total budget caps, destination allowlist/denylist.
- Signed mandates (Ed25519) — short-lived, nonce-bound, issuer-signed authorizations in the AP2 / x402 style. An agent cannot widen its own scope.
- Tamper-evident ledger — every decision is append-only and SHA-256 chained. Any edit, reorder, or deletion is detected by a linear scan.
- MCP server — mount as a guardrail; tools for policy, authorize, mandate issuance, and ledger health.
- Zero deps in the decision path —
cryptographyonly for mandates; the core rules run on the stdlib alone.
Install
# from this repo (works today; also on the official MCP Registry)
git clone https://github.com/ezequiellich44-cmd/MandateGuard.git
cd MandateGuard
python -m pip install -e .
# or directly from the source:
python -m pip install "git+https://github.com/ezequiellich44-cmd/MandateGuard.git"
Note:
mandateguardon PyPI is pending Trusted Publisher setup; until then the repo URL is the canonical install path. The MCP bundle is live on the official MCP Registry (io.github.ezequiellich44-cmd/mandateguard), so MCP-aware clients can install it without any Python step.
Quickstart
from mandateguard import Intent, Policy, PolicyEngine, Scope
policy = Policy(
scopes={
"wallet-agent": Scope(
tools=("pay",),
destinations=("0xGOOD",),
max_amount=1000, # per call
currency="usd",
max_calls_per_window=5,
)
},
global_max_amount=2000, # per actor
allowlist=("0xGOOD",),
denylist=("0xSCAM",),
)
engine = PolicyEngine(policy)
decision = engine.authorize(
Intent(tool="pay", destination="0xGOOD", amount=800, actor="wallet-agent")
)
print(decision.status) # DecisionStatus.APPROVED
Denied calls are blocked with structured reasons; state (spend/rate) commits only on approval, so replays are deterministic.
MCP server
The package ships an installable MCP server entrypoint:
python -m pip install -e ".[mcp]"
mandateguard-mcp # stdio server, ready for Claude/Cursor/harness
For Claude Code:
claude mcp add mandateguard -- mandateguard-mcp
MandateGuard is published on the official MCP Registry:
io.github.ezequiellich44-cmd/mandateguard (version 1.0.0, mcpb bundle,
active). MCP-aware clients that sync the registry can discover and install it
directly. The bundle ships the same stdio server and 14-tool surface.
Exposed tools: set_scope, set_global_policy, authorize, init_ledger,
ledger_status, create_mandate_signer, issue_mandate, check_mandate,
activate_license, license_status, reset_state, plus Pro-gated
revoke_mandate and persist_state behind a signed Pro license (USDT
purchase — see the Buy section).
Mandates
from mandateguard import Mandate, MandateSigner, verify_mandate
issuer = MandateSigner()
m = Mandate(actor="wallet-agent", max_amount=500, currency="usd",
tools=("pay",), destinations=("0xGOOD",),
not_before="2026-01-01T00:00:00+00:00",
not_after="2099-01-01T00:00:00+00:00", nonce="abc", issuer="you")
sig = issuer.sign(m)
verify_mandate(issuer.public_key_bytes, m, sig) # True
Architecture
See docs/ARCHITECTURE.md for the decision flow and state model, docs/THREAT_MODEL.md for what this does and does not protect against, and docs/LAUNCH.md for the commercial pitch and go-to-market kit.
Agent intent ──> authorize(intent) ──> PolicyEngine
│ scope? allowlist? denylist?
│ budget? rate limit? mandate?
▼
APPROVED / DENIED / REQUIRES_APPROVAL
│
▼
append-only SHA-256 ledger (audit)
Tests
python -m pytest -q
Buying MandateGuard Pro
Fully automated, self-service:
- Open buy.html, pick a plan (Pro monthly 99 USDT launch price (was 149) / annual 1,430 USDT) and chain (Solana or Ethereum).
- Send the exact USDT amount to the displayed address (QR provided).
- Click I've Paid — a pre-filled GitHub issue opens; paste your transaction hash and submit.
- A bot verifies your payment on-chain and replies with your signed Ed25519 license within minutes. No humans in the loop.
Free 14-day Pro trial: open a trial issue and the bot delivers a license automatically.
Enterprise / custom terms: ezequiellich44@gmail.com
License
MIT core. Pro features require a signed license — see Buying MandateGuard Pro.
Security Model
Fail-closed: If the policy engine is unreachable or errors, payment calls are denied. No silent failures.
Key storage: Signing keys loaded from environment variables or file, never hardcoded. Recommended: use OS keychain or vault for production.
Policy mutation: Policy files are read-only at startup. Hot-reload requires signed policy update (Ed25519). Rollback: keep previous policy hash, revert to last known-good.
Recovery: If authorization fails, the agent receives a denial with reason. The agent can retry with different parameters or escalate to human approval.