Software, built by agents.

A factory of agents does the work. Your experts set the direction and decide what matters.

See pricing

Product

spec from intent

Plan

task decomposition

Implement

code against spec

tests, regressions

Security

adversarial probing

Verify

proof of (code, spec)

Deploy

staged rollout

Agents do the work

hundreds running in parallel

People direct it

they make the decisions that matter

People and agents in sync

one smooth flow across the company

§ The shift

The bottleneck was always the people.

Every AI tool so far still puts a person in the loop, so the whole line waits on them. Take people off the line and nothing waits anymore.

Human-centric

every other tool

People are the line and the bottleneck.

Everything waitson a person at every step

Agent-centric

AgentSey

Agents are the line. The bottleneck is gone.

Nothing waitsagents never stop

humanagenttask

§ How it works

Four ideas the factory is built on.

Many agents at once

Every step runs a whole swarm of agents in parallel, each coming at the same work from a different angle.

Nothing ships unchecked

Before any change ships it gets checked, and whatever does not hold up goes straight back to be redone.

The whole codebase in reach

Agents can pull from your entire codebase and its history, without the cost of holding all of it in memory at once.

Fixes that compound

Fix something once and it becomes a rule the whole factory follows from then on, on every project after.

§ Console

What a run looks like.

One screen for the whole run. You watch the work move, and step in wherever a decision needs you.

agentsey.ai/run/****58

Run · rn-3358 · vega-pay/checkout-api

Idempotent retries for webhook delivery

running · 11:38

elapsed

11:42

started 19:02

spent

$4.23

est. $14-18 total

agents

186

across 7 stations

verify q.

d1 cycling · d2 probing

Product

Plan

Implement

Security

Verify

Deploy

FX2

PR8

RX1

DB4

verify gate · holding d1 · cycling under mr-184

§ Quality

Same task. We get all of it done.

doneleft undonethe only difference is how many agents run

Other tools

one model on the task

done

0 / 47

0% done

AgentSey

many agents on the same task

done

0 / 47

0% done

Give one model a real task and it only ever gets through part of it. There is more there than it can hold at once. Split the same task across many small agents, each on its own piece, and the whole thing gets done. Same task, far more finished.

// inference

358 agents against your codebase, for less than a single frontier session.

Running this many agents at once only makes sense if each one is cheap to run. So we built our own inference stack for it: most of the work runs on small models we train in-house, and the frontier model gets called in only where it actually pays off.

357

small specialized models

sizes from 1.5B to 13B

frontier model

called in sparingly

10-100x

cheaper to run

vs one frontier model at the same coverage

// cost to cover the whole codebase

Compared with one frontier model at the same coverage.

illustrative

Frontier-only$1.00

Hybrid (1 + small)$0.38

AgentSey$0.04

The same GPU hour that serves one chat response serves hundreds of agent steps in our scheduler.