SOLUTIONS · GOVERNMENT
Governance that survives an audit.
The public sector cannot run on AI it cannot account for. Agent Hive is an AI-native org where every agent action is approved, budgeted, and logged to a record a reviewer can read, with a human answerable for every consequential decision.
the thesis
Why govtech is our flagship industry.
Most AI in government today is a chat assistant bolted onto a process a person still drives by hand. It can draft a letter, but it cannot be held to account: there is no budget on it, no approval gate in front of it, and no durable record of why it did what it did. For consequential public-sector work, that is the whole problem, not a detail.
Agent Hive starts from the opposite premise. The unit of work is an org of specialist agents with roles, budgets, and a chain of command, not a single prompt box. Every action a worker agent takes runs inside an approval and budget envelope, and lands in an append-only record with a rationale and a named approver. The result is a system you can put in front of an auditor, a procurement officer, or a FOIA request without flinching.
We lead with government because it sets the highest bar for governance, and that bar lifts everything else we build. The same controls that let a public agency trust the platform are the controls a fund, a financial-services desk, or any regulated operator wants too.
the pressure
What the public sector is up against.
The record is non-negotiable
Public-sector work is reviewed, FOIA'd, and audited. A decision without a documented rationale and a named approver is a finding waiting to happen.
Procurement moves slowly
New tooling clears security review, accessibility review, and budget approval before it touches a citizen. Black-box AI rarely clears any of them.
Volume spikes are seasonal
Filing deadlines, benefit windows, and application seasons create surges that fixed staffing either over-provisions for or drowns under.
Accountability cannot be delegated to a model
A human must remain answerable for every consequential action. Automation that removes the human from the loop is a liability, not a feature.
how an ai-native org answers
The four engines, mapped to public-sector work.
CEO
One agent you talk to. It turns a directive into work across the org and keeps a human answerable for everything consequential.
Org
An org chart of specialist agents with per-agent budgets, mandatory approvals on risky actions, and a complete audit trail of every task, decision, and dollar.
Workflows
Your intake, review, and escalation procedures become versioned workflows that run the same way every time, with every step logged.
Hivemind
Policy, prior determinations, and case context live in a governed, self-hosted hivemind that never leaves your colony's runtime.
the record, by design
Every case runs the same governed path.
A case is not a single prompt. It moves through intake, review, and a mandatory human approval before anything consequential happens, and each step lands in an append-only record an auditor can read.
the org
The team a public-sector program would run.
CEO agent
SonnetOwns the program, sets priorities, keeps a human accountable for consequential actions.
Intake Specialist
SonnetClassifies and routes incoming applications and requests.
Case Reviewer
SonnetWorks each case against policy and drafts a determination for approval.
Compliance Logger
HaikuStamps every action with rationale, policy citation, and approver to an append-only log.
Evidence Collector
HaikuAssembles the audit pack on a schedule so review is a download, not a scramble.
Citizen Support
HaikuAnswers status and how-to questions within the hour, escalating edge cases to a human.
proof, not promises
What it looks like in production.
We will not pad this page with logos we have not earned or quotes we cannot back. Here is our own company running under exactly these controls, and the open foundation it is built on. Any numbers are real or flagged as pending verification.
Agent Hive
We run our own company on the platform we sell.
We are our own proof. Marketing, engineering, finance, and support run as a colony of agents under the same approval, budget, and audit-trail controls a public-sector program would rely on. The homepage console is that live colony.
Read the storyOpen foundation
Four open engines, credited in full.
The platform is assembled from four permissively-licensed open-source engines, self-hosted and pinned inside your isolated colony. A reviewer can read the code and the licenses before they ever talk to us.
Read the storyopen source
Built on an open foundation you can inspect.
Agent Hive is assembled from four permissively-licensed open-source engines, self-hosted and pinned inside your own colony. For a public-sector buyer that means the substrate is inspectable: you can read the engines, their licenses, and how they run before you sign anything.
Nothing here asks you to take a vendor's word for it. The foundation is open, the isolation and approval controls are architectural, and the record of every agent action is yours to export and review.
ai-native vs bolted-on
An org, not an assistant.
Most AI tools bolt a chat box onto work a human still drives. An AI-native business is structured the other way around: agents do the work, you set the direction.
AI-native business
AI bolted on
Unit of work
An org of specialist agents with roles and a chain of command.
A single chat window you prompt one task at a time.
Who is in charge
You are the Chairman. The CEO agent runs the company and reports back.
You are the operator, copy-pasting between the model and your tools.
Governance
Per-agent budgets, approvals, and a full audit trail by default.
Whatever guardrails you remember to add to each prompt.
Hivemind
One shared mind across the colony, carried between every run.
A context window that forgets when the tab closes.
Where it runs
Your own isolated colony, provisioned in under a minute.
A shared multi-tenant assistant you do not control.
Built on open source
Four open engines. One platform.
This is the only platform you need to run an AI-native company. We take the best open-source AI engines and run them as managed infrastructure that's efficient, scalable, and reliable. It's what Databricks did for its open-source projects and Supabase did for Postgres: you get the open foundation, we run the hard parts.
Star counts are live from GitHub where available, refreshed hourly; otherwise a verified point-in-time figure.
Agent Hive runs on Agent Hive. We run our own company — marketing, engineering, finance, support — on the same platform we sell. Every colony you'd run, we run too.
proof, not promises
The proof is the architecture.
No borrowed logos. We run our own company on the platform, and the engines under it are open and attributable.
- Runs on itself
- Agent Hive runs its own company on Agent Hive.
- 4
- Open engines under one governed platform.
- < 60s
- From sign-up to a provisioned, isolated colony.
Agent Hive runs on Agent Hive. Every colony you would run, we run too: marketing, engineering, finance, and support.
Bring us your hardest compliance requirement.
We will show you the record before you commit to anything. No DevOps, no black box.