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GreenLightz evaluates every AI-generated commitment against your policies in real time — before it reaches the customer.
Without GreenLightz, the $300 refund goes through. Your company owns the cost.
e.g. $50 refund
Allow instantly. No friction.
e.g. $300 refund (limit: $150)
Adjust to $150 or route for approval.
e.g. $2,000 refund on a $500 order
Block before the customer sees it.
One API call to integrate. Real data from day one. By week 4, you know exactly how much commitment exposure your AI creates.
Every pilot produces real, auditable outputs you can share with your VP Ops, CFO, or compliance team.
Total Commitments Evaluated
1,247
Estimated Blocked Exposure
$34,820
Top Commitment Type
credit_or_refund.issue
Verdict Distribution
Shadow mode: week 1 sample data. No commitments were blocked during this period.
No agent framework dependency. No training data required. Pure policy evaluation at sub-millisecond latency.
One POST request before each agent action. No SDK, no sidecar, no agent framework lock-in. Any language that speaks HTTP can integrate in minutes.
{
"action_type": "credit_or_refund.issue",
"tenant_id": "acme_corp",
"actor_id": "agent-7b",
"target_id": "customer-4492",
"amount_cents": 15000,
"currency": "USD",
"reason": "Product arrived damaged",
"correlation_id": "conv-8812-msg-3"
}Set per-action-type amount ceilings, pre-authorization thresholds, and approval modes in YAML. Policy packs are version-controlled, tenant-scoped, and hot-swappable without downtime.
# Per-tenant policy configuration
max_amount_cents: # Per-action-type ceilings
credit_or_refund.issue: # Refund ceiling
discount.offer: # Discount ceiling
delivery_promise.commit: # Shipping commitment ceiling
pre_authorized_limit: # Auto-approve threshold
approval_mode: # manual | pre_authorized | block
enabled_dimensions: # Active governance dimensions
- financial_impact
- policy_compliance
- behavioral_pattern
policy_version: # Version tag for drift detection
webhook_url: # Approval notification endpoint
webhook_secret: # env-ref (secrets never inline)Every action receives a deterministic verdict with a signed evidence packet. Amber verdicts include an intervention plan — the engine tells your agent exactly how to modify the action and retry within policy bounds, with no human in the loop.
{
"verdict": "REQUIRE_APPROVAL",
"reasons": [
"Amount $150 exceeds pre-authorized limit $75",
"30-day customer aggregate: $420"
],
"evidence_ref": "ev-8a3f...",
"evidence_hash": "sha256:b94d...",
"signed": true,
"intervention_plan": {
"action": "modify_and_retry",
"band": "amber",
"safe_degrade_actions": ["lower_amount"],
"retry_guidance": {
"max_retries": 3,
"stop_condition": "action_allowed_or_max_retries_exhausted"
}
}
}Every action is evaluated against configurable policy dimensions. Each dimension is a separate veto gate — and borderline actions get modification guidance, not a wall.
Per-action-type ceilings, pre-authorization limits, and daily caps.
Per-agent and per-customer frequency tracking across rolling windows.
Cumulative value tracking per customer, per agent, and per agent-customer pair.
Repeat-pressure detection and frequency-based escalation across agent-customer sessions.
Most actions resolve as green or auto-adjusted amber. Only hard violations reach red.
Within policy. Proceed immediately, zero delay.
Exceeds a threshold. Engine returns a concrete modification — agent auto-adjusts and retries.
Hard violation. Signed evidence documents the exact rule. Requires human override.
GreenLightz ships with 9 built-in commitment types. Each one is evaluated against your policy pack with full audit trail.
Full or partial monetary return. Evaluated against per-agent limits and aggregate thresholds.
Percentage or fixed-amount reduction. Policy-checked for stacking and cumulative exposure.
Shipping and delivery date commitments. Velocity-limited and deadline-bounded per policy.
Extension of subscription periods. Duration caps and cumulative exposure tracking.
Service level agreements on uptime, response time, or resolution targets. Duration and scope governed.
Data retention and processing commitments. Retention period ceilings and compliance-bounded.
Response time commitments for support tickets. Floor-bounded to prevent impossible promises.
Override grants and policy exceptions. Scope-limited with mandatory justification tracking.
Replacement, credit, or gesture of goodwill. Value-capped and precedent-tracked.
Common approaches each solve part of the problem. None address all dimensions at once.
Single-threshold checks. Amount > $100? Block.
Ask another LLM whether the action is safe.
Route every flagged action to a human reviewer.
Your AI agents stay fast. Your finance team stays confident. Your compliance team gets evidence they can actually use.
Every agent action is evaluated against financial ceilings, velocity limits, and aggregate patterns before it reaches your customer.
Routine actions auto-approve in under a millisecond. Borderline actions auto-adjust within policy bounds. Only hard violations need a human.
Every verdict produces a signed evidence packet — what was evaluated, which rules fired, and what the agent did next. Immutable and queryable.
Swap a YAML file to change governance behavior. Different thresholds per tenant, per action type, per approval mode — no deployment required.
These aren't features you toggle on. They're architectural guarantees baked into every code path.
Any error, timeout, or ambiguity results in BLOCK — never silent pass-through. The default state is denial.
Verdicts only move toward stricter enforcement. A green can become amber or red mid-evaluation, never the reverse.
Identical inputs produce identical verdicts across every run. No sampling, no temperature, no stochastic paths.
The core evaluation engine has zero external dependencies. LLM enrichment is optional and non-blocking.
Every identifier is HMAC-hashed with per-tenant keys before storage. Zero PII in logs, evidence, or API responses.
Every verdict produces a signed evidence packet with a deterministic content hash. Mutations are cryptographically detectable.
A deterministic governance engine with cryptographic evidence trails, built through 93 hardening iterations.
Currently onboarding design partners. Early access available.
Every security property is verified by automated tests on every deploy. Not a checklist — a runtime guarantee.
Founder, Atlas LLC · Delaware
“AI agents are making commitments — refunds, SLA guarantees, delivery dates — but there's no system governing those promises before they reach the customer. In software engineering, every deployment has evidence-based controls: tests, CI gates, signed artifacts. I built GreenLightz to bring that same discipline to AI agent operations — deterministic checkpoints, signed evidence on every decision, and a clear audit trail that proves exactly what was governed and why.”hyunsukim@greenlightz.com
See how GreenLightz brings governance to your AI agents. 30-minute demo, no commitment.
Video walkthrough coming soon