Argum
A large open-plan office floor at night. Rows of empty desks with monitors still switched on, overhead lights off, a lit strip along the far wall.

FOR TEAMS SELLING AI AGENTS INTO REGULATED INDUSTRIES

Your agents are doing real work.Govern every action before it runs.

Argum is the control plane for the agents you run: one screen for the fleet, rules that decide each action, and a record of every verdict. Any window of that record becomes a receipt your customer can check without seeing your data.

Four colleagues around one end of a conference table in daylight. One leans forward holding a pen, mid-question; the others listen.

THE QUESTIONS YOU CANNOT ANSWER

You have no controls on your own fleet.

Say your agent pays insurance claims. It runs on real policyholders, and the only thing restraining it is written instructions. Two questions you should be able to answer in a second:

"Which of my agents are enforcing a rule right now, and which are only logging?"

"What would this rule change have done to last week's traffic?"

Read the prompt

An instruction is not an enforcement point. Nothing checked that it held.

Grep the logs

Logs record what already happened. They stop nothing.

Your customer's security review asks the same two questions. That is where vendor revenue dies.

THE PRODUCT

See the fleet. Decide every action. Change the rules safely.

SEE

Every agent on one screen, its coverage read from the evidence flowing for it rather than from what the agent claims. Governed, logging only, or nothing arriving at all.

DECIDE

Rules run in code before the action does: allow it, deny it, strip a field, or hold it for a human. Every verdict is recorded with the rule version that produced it.

CHANGE

A rule change is a version someone reviews and approves before it publishes. Running agents pick it up on their next action, with no redeploy.

The agents screen of the Argum dashboard: eight agents with per-agent action charts, five governed, two recording, one ungoverned, and a policy publishing to the fleet.
PRODUCT CONCEPT
Every agent on one screen, and the coverage state each one is actually in.

An agent can be bound to a policy and still only be logging it. The fleet view shows that as its own state, never as governed.

POLICY CONTROL

A rule change is a review, not a redeploy.

Rules live as versions in Argum, not in a prompt and not in your application code. A version is written, reviewed, and approved before it can publish, and on a dedicated deployment the approver has to be someone other than the author.

Before it publishes, replay it against decisions you already recorded and see exactly what changes: what becomes allowed, what becomes denied, which rules never fire, and which fire wider than you meant.

WHAT A CHANGE PASSES THROUGH

Drafta new version, fixed once written
Reviewreplay against recorded traffic, then an approver signs off
Publishrefused until that approval exists
Rolloutagents converge on their next action, stragglers named

Every publish is recorded against the person who made it.

A person alone at a desk at dusk, seen from behind, reading something on a laptop. A notebook and a cup of coffee on the desk, city lights through the window.

THE VERIFIER ROOM

What your customer opens.

You pick a window and an agent, and you get a link. Your customer opens it and reads one sentence in plain English about what that agent did, and under which rules.

One button checks that claim against the sealed record. No account, no access to your systems, nothing leaves their laptop.

Every above-limit payout had human sign-off. No denied action (payout or record read) completed.

312/312 approved14,203 decisions VERIFIED ✓
sample receipt · product concept · proof in R&D

WHAT THE ROOM CONTAINS

The claimin plain English, with the window it covers
Policy hashpaste your own policy, the hashes must match
Accessexpires on a date you set

Change one entry after the fact and no valid proof can be produced.

A room covers recorded evidence for the window it names, and says so on its face. It supplements audit sampling rather than replacing it.

DEPLOYMENT

It runs where your data already lives.

The default deployment is your own cloud, because regulated buyers reject a vendor ingesting their agent logs. Evidence never crosses your boundary. A hosted instance exists for teams without that constraint.

Prompts, tool arguments, and customer records default to hashed or withheld. You choose the handling per field: send it, hash it, encrypt it, or keep it local so only a hash ever leaves.

prompt_textlocal only
tool_argshash
customer_recordencrypt
decision_verdictsend

Integration today is a small enforcement hook in the action path of agents you run, plus an API. It sits beside your framework instead of replacing it.

We hold no compliance certifications yet. SOC 2 comes after the first production deployments.

An annual subscription per deployment, paid by the vendor. Verification is free for the party checking.

WHERE THIS GOES

Every consequential action an AI agent takes should be decided before it happens, and provable after.

Agents will negotiate, pay, prescribe, and file on our behalf. "Prove it" should be a link, not a lawsuit.

Building now: fleet coverage, the decision record, policy review and rollout, the first complete proof. Screens above are concept designs.

Book a 30-minute call

Know a team selling AI agents into regulated industries? That intro helps most.

aryansingh1009@gmail.com