Agent Gate

agent-gate

An MCP server that lets an AI agent gate its own work before it claims "done": deterministic checks, then an independent refute-first review, then a tamper-evident honest receipt.

Agents that grade their own homework ship low-quality output. agent-gate turns that discipline into tools an agent must actually pass: a fail-closed checklist and an append-only, hash-chained receipts ledger. It is Fleet Mode, an agent-orchestration doctrine, made into a runnable tool. Receipts over hype, enforced by the data structures.

🧩 One layer of a five-repo cost-governance stack for operating AI agents cost-efficiently; bow is the flagship that runs every layer in production.

agent: "done!"  ->  verify_gate(evidence)  ->  { passed: false, blocking: ["independent_refute_review", "no_secrets"] }

Why

The expensive failures in agent systems are the silent ones: a model update degrades output, a change quietly breaks a workflow, an agent declares success while the work is wrong. The fix is not a smarter model. It is a gate the agent cannot talk its way past:

• Fail-closed. A check counts as satisfied only if it is explicitly true. Missing proof is not proof. (Mirrors a promotion gate, not an informal check.)
• Tamper-evident receipts. Every decision is recorded as (decision, metric, value, verdict) linked into a sha256 chain. Edit or delete any past receipt and verify_chain() returns false. The honest log is enforced by the structure, not by good intentions.
• Human-gated by default. "Any irreversible/outward act got human approval" is a required check. Agents draft, humans approve.

Tools (over MCP)

The default ship gate encodes Fleet Mode: deterministic_checks_pass, independent_refute_review, no_secrets, human_gated_if_irreversible, honest_receipt_logged.

Install & wire into an MCP client

pip install mcp-agent-gate   # or: pip install -e . (from source)

Add it to your MCP client (Claude Desktop / Claude Code) config:

{
  "mcpServers": {
    "agent-gate": { "command": "python", "args": ["-m", "agent_gate.server"] }
  }
}

Now your agent can call verify_gate(...) before it tells you it is finished, and you get a tamper-evident trail of what it decided. Receipts persist to ~/.agent-gate/receipts.jsonl (override with AGENT_GATE_LEDGER).

Use it directly (no MCP client needed)

from agent_gate.gate import DEFAULT_SHIP_GATE
from agent_gate.ledger import Ledger

res = DEFAULT_SHIP_GATE.evaluate({
    "deterministic_checks_pass": True,
    "independent_refute_review": True,
    "no_secrets": True,
    "human_gated_if_irreversible": True,
    # honest_receipt_logged missing  ->  fail-closed
})
print(res.passed, res.blocking)   # False ['honest_receipt_logged']

led = Ledger("receipts.jsonl")
led.append(decision="ship v0.1", metric="tests", value="pass", verdict="shipped")
print(led.verify_chain())         # True  (until someone edits the log)

Design

• Tested, stdlib-only core. agent_gate/gate.py (fail-closed checklist) and agent_gate/ledger.py (hash-chained receipts) are pure stdlib: fast to read, fast to trust. agent_gate/server.py is a thin MCP adapter over them (the one runtime dependency: mcp).
• Tests pass on Python 3.11-3.13 (see CI). The MCP tools are tested by calling them, not just importing.

Tests

pip install -e ".[dev]" && python -m pytest -q

Demo

Run it yourself: PYTHONPATH=. python3 examples/demo.py

------------------------------------------------------------
1. Agent claims done — but two checks are missing
------------------------------------------------------------
{
  "passed": false,
  "blocking": [
    "human_gated_if_irreversible",
    "honest_receipt_logged"
  ]
}

------------------------------------------------------------
2. Agent satisfies all five checks
------------------------------------------------------------
{
  "passed": true,
  "blocking": []
}

------------------------------------------------------------
3. Record a hash-chained receipt
------------------------------------------------------------
{
  "seq": 1,
  "decision": "ship v0.1",
  "verdict": "shipped",
  "hash": "015202a168512f15..."
}
{
  "seq": 2,
  "decision": "deploy",
  "verdict": "approved",
  "hash": "9533d304d4dd07e5..."
}

------------------------------------------------------------
4. Verify the chain — edit receipts.jsonl to see this flip to False
------------------------------------------------------------
chain_intact: True

This repo gates itself

agent-gate is about not shipping unverified work, so the repository holds itself to the same bar:

• Coverage-gated test matrix — ci.yml runs pytest on Python 3.11–3.13 and fails the build if line coverage drops below the threshold (currently 97% covered).
• CodeQL — static analysis (security-extended) runs on every push, PR, and weekly; findings surface in the Security tab.
• Pinned supply chain — every GitHub Action is pinned to a full commit SHA; Dependabot keeps those pins and the Python deps current.
• Branch protection — main requires the CI and CodeQL checks to pass before a merge.
• Disclosure policy — see SECURITY.md.

Contributing

See CONTRIBUTING.md.

About

Built by Jeff Otterson (Jott2121). agent-gate operationalizes the gating discipline from bow (an autonomous all-Claude chief-of-staff agent) and the Fleet Mode doctrine. Siblings in the same line: rag-guard and agent-cost-attribution. MIT licensed.

Companion instrument

sabot is the measurement side of this idea. agent-gate adds a fail-closed gate to an agent workflow; sabot plants controlled faults inside running LangGraph, CrewAI and AutoGen pipelines and measures whether gates and reviewer stages like these actually fire. Median own-check detection across three production frameworks: 16.7%, with a pre-registered spec, an Apache-2.0 harness, and every raw trace published.