CONTROL INFRASTRUCTURE FOR AI AGENTS

Give your agents
more autonomy

Govern, execute, and verify consequential actions. Built for teams putting AI agents into production.

Wrap refund.create with Nalvis
app.nalvis.ai / runs / run_01NV•••
⌕   Search actions, runs, policies ⌘ K
NC
RUN_01NV · REFUND.CREATE

Control the action

Verified
Captured

refund.create · $247

Policy

Human approval required

Corrected

$247 → $197

Executed

Stripe refund created

Verified

refund.amount = $197

A controlled effect boundary between agent intent and the outside world.

refund.creategithub.mergedeployment.triggerjira.transitioncalendar.bookemail.sendinvoice.updateslack.postrefund.creategithub.mergedeployment.triggerjira.transitioncalendar.bookemail.sendinvoice.updateslack.post

ONE CONTROL LAYER ACROSS YOUR AGENT STACK

LGLangGraphOAOpenAI AgentsCRCrewAIPYCustom PythonTSTypeScript
Policy checkedbefore external execution
Action identifiedacross retries and restarts
Outcome verifiedagainst the system of record
ONE CONTROL PLANE

Connect every agent to a controlled effect layer.

Agent frameworks and external systems meet at one governed execution boundary.

LangGraph OpenAI Agents CrewAI Custom
effect boundary
~ % nalvis run refund.create

Govern

Apply policy and human authority before consequential work begins.

POLICY CHECKrefund.create

Approval required over $200

Execute

Give each action durable identity across retries, workers, and restarts.

intent identity running

Verify

Query the system of record and preserve proof of the actual outcome.

expected  197observed  197 PASS
DESIGNED FOR PRODUCTION

Confidence at every step

Turn agent activity into a system your team can inspect, correct, and improve.

POLICY EVALUATION
refund.amount$247
approval_threshold$200

Route to billing lead

Authority that matches the action

Set review thresholds by action, risk, environment, or your own business context.

TIMEOUTVERIFYSAFE

Verify before retry

A timeout is an unknown outcome. Check external state before risking a duplicate action.

with nalvis.effect(  action="refund.create",  verify=verify_refund):

Framework agnostic

Add the effect boundary around the agents and tools you already run.

PROPOSED$247HUMAN EDIT$197VERIFIEDPASS

Corrections become structured experience

Preserve context, edits, executed actions, and verified state for evaluation and policy improvement.

ENTERPRISE ARCHITECTURE

Ready for consequential work

Control, deployment, and evidence primitives designed to scale with production agents.

↗

Authority & access control

Model who can approve which actions, under what conditions, and in which environment.

Billing production
↗

Multi-system workflows

Keep one action identity while work crosses agents, workers, and systems of record.

act_refund_72
AgentWorkerStripe
↗

Deployment flexibility

An architecture intended to fit stricter networking and deployment requirements.

↗

Human review paths

Route exceptions and high-impact decisions to the right operator with full context.

↗

Evidence by design

Preserve intent, policy decisions, execution attempts, observed state, and verdicts.

↗

Operational controls

Build toward the audit, escalation, and reliability requirements your workflows demand.

FOR DEVELOPERS

A small boundary.
A complete record.

Keep your orchestration. Nalvis sits around the tool calls that create real-world effects.

from nalvis import effect

with effect(
    action="refund.create",
    risk="financial",
    verify=verify_refund,
):
    stripe.refunds.create(...)
Policy checked Action identified Outcome verified
COMMON QUESTIONS

Control for the actions that matter.

Clear answers for teams moving AI agents from prototypes into production workflows.

01

What is Nalvis?

Nalvis is control infrastructure for AI agents that take consequential actions. It adds policy checks, approvals, execution tracking, outcome verification, and evidence around tool calls.

02

Which agent actions should run through Nalvis?

Use Nalvis for actions that create real-world effects, such as refunds, account changes, deployments, ticket transitions, emails, bookings, and other operations that require control or proof.

03

Does Nalvis replace an agent framework or orchestrator?

No. Teams keep their existing models, agents, and orchestration. Nalvis sits at the action boundary around the tool calls that create effects in external systems.

04

What evidence does Nalvis preserve?

Nalvis is designed to preserve action intent, policy decisions, approvals, execution attempts, observed state, verification results, and the final outcome in one traceable record.

Let your agents do more.
Keep control of what happens.

A personal exploration in controlled execution for AI agents.