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.

AI Agent"Issue $150 refund"
GreenLightzPolicy evaluation
VerdictAMBER — reduce to $75

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.

Three steps to governed AI

No agent framework dependency. No training data required. Pure policy evaluation at sub-millisecond latency.

Step 01

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.

POST /gateway/evaluate
{
  "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"
}
Step 02

Define Policies

Declare per-agent authority limits, aggregate exposure thresholds, and blocked categories in YAML. Policy packs are version-controlled, tenant-scoped, and hot-swappable without downtime.

policy.yml
# acme_corp policy pack
max_single_refund_cents: 7500
max_daily_per_customer_cents: 20000
agent_authority_limit_cents: 7500
blocked_categories:
  - data_handling.commit
escalation:
  channel: webhook
  timeout_seconds: 3600
acceleration_profile: balanced
Step 03

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.

200 OK — Response
{
  "verdict": "AMBER",
  "band": "amber",
  "reasons": [
    "rule: amount $150 exceeds agent limit $75",
    "aggregate: customer 30d total $420"
  ],
  "evidence_ref": "ev-8a3f...",
  "evidence_hash": "sha256:b94d...",
  "signed": true,
  "intervention_plan": {
    "action": "MODIFY_AND_RETRY",
    "safe_degrade_actions": ["reduce_amount"],
    "retry_guidance": {
      "max_retries": 3,
      "stop_condition": "verdict == ALLOW"
    }
  }
}

Every commitment type, governed

GreenLightz ships with 6 built-in action 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.

Shipping Upgrade

Expedited or free shipping commitment. Cost impact calculated and policy-bounded.

Deadline Extension

Postponement of agreed timelines. Checked against SLA constraints and contractual limits.

Warranty Extension

Coverage beyond standard terms. Evaluated for liability exposure and precedent risk.

Access Grant

Granting access to restricted resources. Hard-blocked by default unless policy explicitly allows.

6 governance invariants. 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

Not a wrapper on an LLM. A deterministic governance engine with cryptographic evidence trails, built through 93 hardening sprints.

< 1ms
Evaluation latency
P99 under 1 millisecond
6
Architectural invariants
Always enforced, never toggleable
1,400+
Governance test cases
Deterministic, run on every deploy
0
External runtime dependencies
Core engine works fully offline

Enterprise security, not enterprise complexity

Every security property is verified by automated tests on every deploy. Not a checklist — a runtime guarantee.

End-to-End Encryption
HTTPS + HMAC-signed evidence
Tamper-Evident Audit Trail
Cryptographically signed verdicts
Deterministic Verdicts
Same input, same output — always
Privacy-First Architecture
All IDs hashed, zero PII in logs
Fail-Closed by Default
On error, actions are blocked — not allowed
Offline-First Engine
No LLM dependency for core governance

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