Define what your agents do, step by step — in a UI, not a prompt. Any agent runs it. Every step's output is captured as structured data, every run is tamper-evident by default.
Most AI agents work most of the time, fail invisibly, and can't be governed, audited, or trusted with anything important. Six reasons:
The "what the agent does" is encoded as free-form text buried in a system prompt. No structure, no version history, no way for a non-technical person to edit it, and no way to know whether the agent actually followed it.
Agents return text. The next step in any workflow has to re-parse that text to figure out what happened. Brittle, expensive, lossy — and impossible to query later.
Prompts drift. Different teammates tweak them. The agent that books a customer today doesn't behave the same as it did last month, and no one knows what changed.
For operational work, it's a bug. The same trigger should produce the same procedure run, every time. Most agent platforms can't promise that.
You have a Zapier history, an n8n log, three humans' memories, and a vague feeling. There's no single place that says "here is what was supposed to happen, here is what did happen, here is the evidence."
Three agents become ten. Ten become thirty. Each one is its own siloed wall of text. By the time you notice, untangling it is a project no one wants to own.
Hque sits in front of your agent infrastructure. You define the work; agents run it; Hque keeps the record.
Drag and Drop editor for procedures. Full version control, difference viewer and procedure management. Use our pre-built templates, ask AI, or build your own from scratch.
Your custom procedure definitions, runs, structured step outputs, tamper-evident audit trail. The system of record — the layer that doesn't change when your agent stack does.
MCP server, REST API, and native integrations. Any agent — voice, chat, Zapier, n8n, custom code — calls Hque to read the latest procedure definition and report what it did.
Hque is to AI agent operations
what Stripe is to payment methods —
a neutral coordination layer
in a fragmenting ecosystem.
The common thread isn't size or industry — it's that you have real work that should run the same way every time, and you've realised prompts alone won't get you there.
Three or four agents in, you've noticed the drift. The Friday-night fix that broke something else. The output you can't query. Hque is the structure you've been mentally drafting.
You need to deliver something reliable, versionable, and visible to the client — not a black-box prompt only you can edit. Hque makes the work auditable for them and maintainable for you.
You need the procedure-runner story and the audit-trail story bundled. Hque was built for businesses where "we usually do it this way" isn't a defensible answer.
One-person business, five agents, and a growing suspicion that they're slowly diverging. Hque is the system of record for the work — so you can visually see exactly what they are all doing.
Hque is deliberately neutral about which platforms you use. The agent layer churns; the system of record doesn't.
Crisp positioning is easier when we say what's not in scope.
Start with one procedure. Add the agent of your choice. See your operations as repeatable, structured data — not as a chat transcript.