AI agents make promises
your company has to keep.
GreenLightz evaluates every AI-generated commitment against your policies in real time — before it reaches the customer.
The problem with autonomous AI
AI agents are making financial commitments on your behalf. Without governance, every interaction is an uncontrolled risk.
Unauthorized Commitments
Your AI agent promised a full refund, free shipping, and an extended warranty — all in one conversation. Nobody approved it.
Revenue Leakage
Discounts and credits pile up silently. By the time finance notices, thousands have leaked through unapproved agent actions.
Compliance Risk
Regulators ask: 'Who authorized this?' With no audit trail, you can't answer. Every untracked commitment is a liability.
What changes when governance is real-time
Your AI agents stay fast. Your finance team stays confident. Your compliance team gets evidence they can actually use.
Block unauthorized exposure
Every agent action is evaluated against financial ceilings, velocity limits, and aggregate patterns before it reaches your customer.
Zero approval bottlenecks
Routine actions auto-approve in under a millisecond. Borderline actions auto-adjust within policy bounds. Only hard violations need a human.
Audit-ready from day one
Every verdict produces a signed evidence packet — what was evaluated, which rules fired, and what the agent did next. Immutable and queryable.
Policy changes, not code changes
Swap a YAML file to change governance behavior. Different thresholds per tenant, per action type, per approval mode — no deployment required.
Three steps to governed AI
No agent framework dependency. No training data required. Pure policy evaluation at sub-millisecond latency.
Integrate
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"
}Define Policies
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)Govern in Real-Time
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"
}
}
}Judgment, not just thresholds
Every action is evaluated across four dimensions simultaneously. Each dimension is a separate veto gate — and borderline actions get modification guidance, not a wall.
Financial thresholds
Per-action-type ceilings, pre-authorization limits, and daily caps.
Velocity patterns
Per-agent and per-customer frequency tracking across rolling windows.
Aggregate exposure
Cumulative value tracking per customer, per agent, and per agent-customer pair.
Behavioral analysis
Stacking detection, escalation patterns, and precedent tracking across turns.
Three possible outcomes
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.
Most actions resolve as green or auto-adjusted amber. Only hard violations reach red.
Why existing approaches fall short
Common approaches each solve part of the problem. None address all dimensions at once.
Simple rule engines
Single-threshold checks. Amount > $100? Block.
- No cross-action memory — can't see $420 aggregate this month
- No velocity tracking — 5 refunds in an hour looks the same as 5 in a year
- Binary outcome — block or allow, nothing in between
LLM-based guardrails
Ask another LLM whether the action is safe.
- Non-deterministic by design — same input may produce different verdicts
- Model inference adds latency to every evaluation in the agent's critical path
- Verdicts are opinions, not signed evidence — harder to audit at scale
Manual approval queues
Route every flagged action to a human reviewer.
- Doesn't scale — 100 agents generating 1,000 actions/hour
- Introduces hours of delay into real-time conversations
- Reviewer fatigue increases risk of inconsistent decisions over time
GreenLightz is a different category
Every commitment type, governed
GreenLightz ships with 9 built-in commitment types. Each one is evaluated against your policy pack with full audit trail.
Refund
Full or partial monetary return. Evaluated against per-agent limits and aggregate thresholds.
Discount
Percentage or fixed-amount reduction. Policy-checked for stacking and cumulative exposure.
Delivery Promise
Shipping and delivery date commitments. Velocity-limited and deadline-bounded per policy.
Subscription Extension
Extension of subscription periods. Duration caps and cumulative exposure tracking.
SLA Commitment
Service level agreements on uptime, response time, or resolution targets. Duration and scope governed.
Data Handling
Data retention and processing commitments. Retention period ceilings and compliance-bounded.
Support Response
Response time commitments for support tickets. Floor-bounded to prevent impossible promises.
Policy Exception
Override grants and policy exceptions. Scope-limited with mandatory justification tracking.
Goodwill Remedy
Replacement, credit, or gesture of goodwill. Value-capped and precedent-tracked.
6 built-in guarantees. Always enforced.
These aren't features you toggle on. They're architectural guarantees baked into every code path.
Fail-Closed
Any error, timeout, or ambiguity results in BLOCK — never silent pass-through. The default state is denial.
Escalate-Only
Verdicts only move toward stricter enforcement. A green can become amber or red mid-evaluation, never the reverse.
Deterministic
Identical inputs produce identical verdicts across every run. No sampling, no temperature, no stochastic paths.
Offline-First
The core evaluation engine has zero external dependencies. LLM enrichment is optional and non-blocking.
Privacy-First
Every identifier is HMAC-hashed with per-tenant keys before storage. Zero PII in logs, evidence, or API responses.
Tamper-Evident
Every verdict produces a signed evidence packet with a deterministic content hash. Mutations are cryptographically detectable.
Built to be trusted
A deterministic governance engine with cryptographic evidence trails, built through 93 hardening iterations.
What a pilot looks like
2-4 weeks from first API call to production decision. Start in shadow mode with zero risk to live operations.
Setup
1-2 hours
Configure your policy thresholds, connect your AI agent with one API call, and validate with a test evaluation.
Shadow mode
Observe
GreenLightz evaluates in parallel but doesn't block your agents. See what would be caught — zero impact on live operations.
Live governance
Govern
Enable real-time governance. Tune thresholds based on Week 1 observations. Monitor approval queue and aggregate patterns.
Review
Decide
Structured review: blocked exposure, approval patterns, false positive rate. Decide on production rollout together.
Enterprise security, not enterprise complexity
Every security property is verified by automated tests on every deploy. Not a checklist — a runtime guarantee.
Stop your AI from writing checks
you can't cash.
See how GreenLightz brings governance to your AI agents. 30-minute demo, no commitment.
Book a Demo