---

name: governed-agents description: "Deterministic verification + reputation scoring for AI sub-agents. Prevents hallucinated success via 4 code gates (files, tests, lint, AST) and a 3-layer pipeline (Structural → Grounding → LLM Council) for open-ended tasks." install: {"kind": "script", "script": "install.sh"} filesystem_writes: ["~/.openclaw/workspace/.state/governed_agents/"] capabilities: ["persistent_db_writes", "external_cli_execution", "network_requests"] network_access: true metadata: { "openclaw": { "emoji": "🛡️", "type": "executable / with-install-spec", "requires": { "bins": ["codex", "openclaw", "git", "pytest", "ruff", "flake8", "pylint"] }, "install": [ { "id": "script", "kind": "script", "command": "bash install.sh", "label": "Install governed-agents (copy to workspace)", }, ], }, } capability_flags: network-capable: true subprocess-capable: true

Governed Agents

Deterministic verification + reputation scoring for AI sub-agents. Prevents hallucinated success ("I did it!") by verifying claims independently before updating the agent's score.

Pure Python stdlib — zero external dependencies.

Capabilities

Spawns external CLIs (codex, openclaw, git, pytest) and makes HTTP HEAD requests.

When to Use

Use this skill when you need to:

• Spawn sub-agents and verify their output automatically
• Score agent reliability across tasks (EMA-based reputation)
• Detect hallucinated success — agent claims "done" but files are missing or tests fail
• Verify open-ended tasks (research, analysis, strategy) via LLM Council
• Enforce supervision levels based on agent track record

Quick Start

Coding Tasks (Deterministic Verification)

from governed_agents.contract import TaskContract
from governed_agents.orchestrator import GovernedOrchestrator

contract = TaskContract(
    objective="Add JWT auth endpoint",
    acceptance_criteria=["POST /api/auth returns JWT", "Tests pass"],
    required_files=["api/auth.py", "tests/test_auth.py"],
    run_tests="pytest tests/test_auth.py -v",
)

g = GovernedOrchestrator(contract, model="openai/gpt-5.2-codex")
# After agent completes:
result = g.record_success()  # runs gates, updates reputation

Open-Ended Tasks (3-Layer Pipeline + LLM Council)

contract = TaskContract(
    objective="Write architecture decision record for auth module",
    acceptance_criteria=["Trade-offs documented", "Decision stated"],
    verification_mode="council",
    task_type="analysis",
    council_size=3,
)

g = GovernedOrchestrator(contract, model="openai/gpt-5.2-codex")
prompts = g.generate_council_tasks(worker_output)
result = g.record_council_verdict(raw_reviewer_outputs)
# → "Council: 2/3 approved (score=0.67, PASS ✅)"

CLI Spawning (Codex / OpenClaw)

from governed_agents.openclaw_wrapper import spawn_governed

contract = TaskContract(
    objective="Build a REST API for todos",
    acceptance_criteria=["CRUD endpoints work", "Tests pass"],
    required_files=["api.py", "tests/test_api.py"],
)

# Uses Codex 5.3 CLI by default
result = spawn_governed(contract, engine="codex53")
# Or via OpenClaw agent CLI:
result = spawn_governed(contract, engine="openclaw")

Verification Modes

Deterministic (Coding Tasks)

4 gates run automatically — all must pass:

Gate	Check	Signal
Files	Required files exist and are non-empty	Hard fail
Tests	Test command exits 0	Hard fail
Lint	No lint errors	Hard fail
AST	Python files parse without SyntaxError	Hard fail

If agent claims SUCCESS but any gate fails → score override to -1.0 (hallucination penalty).

Council (Open-Ended Tasks)

3-layer pipeline with short-circuit:

1. Structural Gate (<1s) — word count, required sections, no empty sections
2. Grounding Gate (5–30s) — URL reachability, citation checks
3. LLM Council (30–120s) — N independent reviewers, majority vote

If Layer 1 fails → no LLM calls, instant result, zero cost.

Reputation System

R(t+1) = (1 − α) · R(t) + α · s(t),   α = 0.3

Score	Meaning
+1.0	Verified success (first try)
+0.7	Verified success (after retry)
+0.5	Honest blocker report
0.0	Failed but tried
−1.0	Hallucinated success

Supervision Levels

Reputation	Level	Effect
> 0.8	autonomous	Full trust
> 0.6	standard	Normal supervision
> 0.4	supervised	Checkpoints required
> 0.2	strict	Model override to Opus
≤ 0.2	suspended	Task blocked

Task-Type Profiles

Pre-configured gate combinations:

task_type	Layer 1	Layer 2	Min words
research	word_count, sources_list	url_reachable, citations	200
analysis	word_count, required_sections	numbers_consistent	150
strategy	required_sections, word_count	cross_refs_resolve	100
writing	word_count	—	50
planning	required_sections, has_steps	dates_valid	50

Installation

bash install.sh
# → Copies governed_agents/ to $OPENCLAW_WORKSPACE/governed_agents/
# → Runs verification suite (37 tests)

Tests

python3 -m pytest governed_agents/test_verification.py \
                   governed_agents/test_council.py \
                   governed_agents/test_profiles.py -v
# 37 passed