AI agents now take real actions. One wrong one costs real money.
Agents issue refunds, move money, send emails, write code, touch customer data. Verun.ai authorizes what your agents do — before they do it — and learns what's normal for each one, catching the abnormal even when no rule is broken.
Deterministic · No LLM judge in the decision path · Runs inline with negligible overhead
Watch Verun hold a $400 refund — then execute it.
A hosted support-desk agent proposes a refund above its limit. Verun holds it, a human approves, and Verun issues the refund itself — with a signed receipt for the whole path. Click through it yourself.
Agent proposes
Support agent issues a $400 refund
Verun holds
Above the auto-approve limit — paused
Human approves
Reviewer clears it in the console
Verun executes
Verun issues the refund itself
Signed receipt
Tamper-evident record of the whole path
Start in shadow mode. Then let Verun draft your first policy.
Adopt Verun the safe way — watch first, enforce when you're ready, and never write rules from a blank page.
Start in shadow mode. Zero risk.
Turn Verun on in observe mode and it changes nothing. It records what it would have allowed, held, or blocked on your real agent traffic — so you see the evidence before you enforce a thing.
Then it drafts your first policy for you.
From what it observed, Verun mines a policy — deterministic, explainable, every rule backed by the traffic that justifies it. Nobody writes rules from a blank page; you review and approve.
A rules engine — or your own code — catches what you thought to forbid.
Verun learns what's normal for each agent, and catches the abnormal — even when no rule is broken.
Others govern actions. Verun remembers behavior.
The three questions, asked on every action
Policy
Can it?Allowed tools, thresholds, environment restrictions, mandatory approvals. Default-deny — undefined actions don't proceed.
Behavior
Does it usually?A per-agent baseline. Flags velocity bursts, first-seen targets, cross-domain behavior, unusual sequences. UEBA for agents.
Mission
Should it?A deterministic off-topic tripwire that holds actions drifting from the agent's declared purpose for human review. Robust intent verification is on our roadmap.
AI moved from generating content to taking action.
Agents now issue refunds, message customers, export data, run infrastructure commands, move money. The moment an AI system can act, a single wrong action carries a real, immediate, often irreversible cost.
Yet between an agent's decision and its execution, almost nothing independent checks: should this actually happen, right now?
Observability is after the fact
Monitoring platforms explain what happened after execution. Too late to prevent the action.
Guardrails screen content, not actions
LLM guardrails check prompts and outputs — not consequential tool-calls — and rely on a probabilistic model that can be bypassed.
In-code rules only cover the imagined
An `if amount > 10000` check covers the failures a developer anticipated, needs a deploy to change, produces no independent audit, and can't express "this is abnormal for this agent."
The agent proposes. Verun authorizes.
Every consequential action is evaluated against three layers of signal — each answering a different question, escalating only when needed.
Deterministic Controls
Can it?Policy: allowed tools, thresholds, environment restrictions, allow/block lists, mandatory approvals. Default-deny — undefined actions don't proceed.
Behavioral Controls
Does it usually?A per-agent behavioral baseline. Flags velocity bursts, first-seen targets, cross-domain behavior, unusual sequences, and outliers. Is this normal for this agent?
Mandate Controls
Should it?Anchors each agent to its declared mission using deterministic semantic representations. Flags drift off the assigned purpose — without an LLM judge.
Human Review
Do we approve?Held actions enter a review workflow with full context — proposed action, target, reason, anomalies, policy version. Approve once, or turn it into a rule. Escalation can fire instantly to Slack, Teams, or webhook.
Signed Receipts
Can we prove it?Every decision produces a signed, tamper-evident, replayable record — which layer decided and why. Exportable as Governance Evidence Packs for audit and compliance.
No LLM on the decision path.
Using an AI to judge another AI's actions reintroduces the exact problems you're trying to govern: bias, non-determinism, latency, bypassability. Verun's decisions are deterministic and explainable — the same input always yields the same decision and the same stated reason.
It's the only stance defensible to a regulator. An LLM may help a human understand a decision — it never makes one.
ALLOW
Proceed
HOLD
Pause for review
BLOCK
Refuse
ESCALATE
Push to a human
Behavioral & mission signals hold — they never silently block. A false positive costs one human review, not a broken workflow.
Why the cloud giants won't own this.
AWS, Microsoft, Kong and Palo Alto will own the gateway and policy plumbing. They're not structurally positioned to own per-agent behavioral memory. That's Verun's lane — the CrowdStrike + UEBA for agents.
Deterministic
No LLM in the decision path. Same input, same verdict — explainable and auditable. We don't use one AI to police another.
Behavioral, not just permissioned
Per-agent baselines flag the action that's allowed but abnormal. UEBA for agents.
Neutral & cross-stack
Governs agents on OpenAI, Anthropic, or local models. Not locked to one cloud.
Compounding decision data
Every allow, hold, block, and human ruling makes the baselines sharper. The data advantage grows with usage.
Designed to support — not replace — leading AI governance frameworks.
Verun provides the operational controls and evidence that map to the requirements in today's emerging governance standards.
See the framework alignment →Others govern actions.
Verun remembers behavior.
Deploying agents for clients? Verun is the governance layer you offer them.
Now onboarding design partners and pilot customers in e-commerce & customer-support automation.