Control for AI actions

Stop risky AI actions before they become real business mistakes.

AI agents are starting to update CRMs, send messages, issue refunds, and make changes across business systems. Argos pauses risky or unclear actions, asks the right person to decide, and checks the result.

See how Argos worksTalk to us about your workflow

The problem

AI agents can now take real actions. That also means they can make real mistakes.

An AI agent might choose the wrong customer, send the wrong message, repeat an action twice, or think something worked when it did not.

01

Wrong customer

Two records look similar. The agent picks the wrong one.

02

Wrong action

The agent is about to refund, delete, send, or update something it should not.

03

Wrong result

The system says the action worked, but the expected change never actually happened.

Most tools help you understand what happened after the mistake. Argos helps you step in before it becomes one.

How it works

A simple control layer for AI actions.

Argos gives your team one clear way to pause a risky action, decide what should happen, and check the result.

  1. 1

    Pause

    If an action looks risky or unclear, Argos holds it before the external system is changed.

  2. 2

    Decide

    The right person sees what the AI is trying to do and chooses what should happen.

  3. 3

    Check

    After the action runs, Argos checks the external system to make sure the right result happened.

That is the core of Argos: pause, decide, check.

One customer example

Here is what that looks like.

An AI agent wants to update a customer subscription. It finds two customers with the same name. Instead of letting the agent guess, Argos pauses the update and asks a person to choose the right customer.

Customer subscription update

  1. 1AI proposes an update
  2. 2Two customer records match
  3. 3Argos pauses the action
  4. 4Human selects the correct record
  5. 5One update is released
  6. 6Argos checks both records

What success means

The right action happened. The wrong action didn't.

Argos is not successful just because an API returned 'success'. We want to know that the intended business result actually happened.

  • The risky action was stopped before a mistake.
  • The right person made the decision.
  • The approved action happened once.
  • The wrong record stayed untouched.
  • The final result was checked.

If Argos cannot confirm the result, it does not pretend everything worked.

Fits your workflow

Keep your existing AI agents and tools.

You do not need to rebuild your agent inside Argos. Add an Argos checkpoint before the actions you care about controlling.

  • Your agent keeps doing the work.
  • Argos steps in when human judgment or verification is needed.
  • You choose which actions Argos controls.
See how integration works

Your AI agent

Keeps doing the work

Argos

Pauses, routes, and checks

Your business system

CRM, billing, support, or internal tool

Technical detail

Argos works through explicit SDK or API checkpoints placed before selected consequential actions. It does not monitor or control arbitrary uninstrumented agents.

Security

Your systems stay yours.

Argos is designed to receive only the information needed to control the action. Credentials stay server-side, access is limited to the relevant workflow and workspace, and uncertain results are not blindly retried.

  • Send only the context Argos needs.
  • Keep credentials server-side.
  • Scope access to the relevant workflow.
  • Leave uncertain outcomes unresolved until they can be checked.
How Argos protects your data

Founder-led pilot

Start with one AI action you don't want going wrong.

You do not need to roll Argos out everywhere. We start with one workflow where an AI agent can make a meaningful change.

  1. 1Identify the risky action.
  2. 2Add the Argos checkpoint.
  3. 3Decide who should step in when something is unclear.
  4. 4Define how the result should be checked.
  5. 5Review what happened together.

Pricing

Let's understand your workflow first.

Every AI workflow is different. The work involved depends on what your agent does, which systems it connects to, and how you want actions reviewed and verified.

Founder-led engagement

One clearly scoped workflow, built with your team.

Contact us with the action you want more control over. We will assess fit, define the pilot scope, and explain what the engagement requires before discussing commercial terms.

Talk to us about your workflow

No fixed public package. Scope depends on the workflow, systems, and verification required.

What our team brings

Practical support from workflow definition through pilot review.

  • Workflow and risk mapping

    Identify the consequential action, its failure modes, and the moment Argos should step in.

  • Checkpoint integration support

    Work with your technical contact to place Argos before the selected business action.

  • Decision workflow design

    Define who reviews an exception and what each reviewer choice is allowed to release.

  • Result-check design

    Agree how the external business result should be reread and confirmed after execution.

  • Pilot validation

    Exercise contained, approved, denied, and uncertain outcomes with your team before rollout.

  • Direct founder involvement

    Work directly with the Aurevon team through scope, setup, review, and next-step planning.

Have an AI workflowyou wouldn't leave completely unattended?

Show us what the agent does, what could go wrong, and which system it changes. We'll show you where Argos can fit.

Talk to us about your workflow