Everything an agent needs to hold a real job.
A model can talk. A worker shows up, does the task, reports back, asks when unsure, and improves. Agenthub is the operations layer that turns one into the other — organized here the way you'll meet it: build, operate, safeguard, govern, collaborate, and the economics underneath.
If you can describe the work, you can run an agent.
No prompt engineering, no setup tax. Tell the platform what done looks like and it sorts out the specifics with you — tools, steps, and where it should stop and ask. Agents live where your work lives: they report, ask, and get approved right in Slack.
Lost in the product? Just say so. You can talk to the platform itself, and it takes you where you need to go.
01From a sentence to a working agent.
State the end goal in plain language. The builder helps you sort out the specifics — steps, tools, and when to stop and ask. If you can describe the work, you can build an agent.
A product researcher lives inside the builder: it interviews you about edge cases you'd forgotten before they become incidents.
Don't hunt through menus. Ask the platform itself — it explains, navigates, and does the clicking for you.
Agents live where work lives. They report progress, ask for approvals, and take instructions in Slack — no new tab to remember.
Integrations for the common systems, plus generic connectors — SQL and friends — for everything else. Your data reaches the agents where it already lives.
Indexers read and organize your data ahead of the runs, so agents start from sense — not from raw exports.
Agents can operate a real browser: grant a login and they work your business systems the same way a person would — supervised, approved and logged like everything else.
See every agent. Trust what you see.
One board shows what every agent is doing, how often it succeeds, and where it struggles. Agenthub tracks success rates, spots failure patterns automatically, and agents improve from their own track record — so good agents earn more work, and shaky ones don't.
When something needs a human, it comes to you — not the other way around.
02Run agents like a team, not like experiments.
Every agent, every run, live on one board: what it's doing, how long it's been at it, and what's waiting on a human.
Every run is verified and scored, so “does it work?” has a number — per agent, per task, over time.
The platform clusters what goes wrong across runs and names the pattern, instead of leaving you to read transcripts at midnight.
Agents learn from their own track record and from your feedback — the fix for a failure pattern becomes part of the agent.
Agents that succeed earn more work; shaky ones earn less. Operating leverage compounds where it's deserved.
Autonomy you can take back at any moment.
The reason teams freeze after one bad AI experience isn't the mistake — it's not knowing what else might happen. On Agenthub, nothing irreversible happens without a named human behind it.
- Simulate firstChange an agent, then watch what it would have done on real work before you enable it.
- AI supervisorsWatch every run and stop an agent to ask permission when something looks off.
- Approvals where they matterCompany-wide rules — like a manager's sign-off before an agent contacts anyone outside.
- Version history & rollbackEvery change to an agent, and every piece of work it produces, is versioned. Roll back either.
03Nothing irreversible without a named human.
Change an agent, then watch what it would have done on real work — before you enable it. Ship the change when the dry run convinces you.
Independent supervisors observe every run and stop an agent to ask for permission when something looks off. The stop is the feature.
Put a human yes in front of the actions that deserve one — sending externally, spending money, touching records. One tap in Slack, back to work.
Both kinds of history, kept: every change people make to an agent, and every piece of work the agent produces.
An unintended agent change — or an unintended result — rolls back in one click. Mistakes become moments, not incidents.
04Company-wide rules, answered questions.
Admins set the lines once, for every agent: topics that are off-limits, audiences that require review — for example, no agent talks to anyone outside the company without a manager's approval.
At any point, about anything: which agents were involved, and which people set them up. One question, one answer, always.
Decide who can build, edit, publish — or simply use — each agent. IT and your AI experts get the tools to serve everyone else.
Every agent action and every human change, logged and reviewable. The record exists before anyone asks for it.
See which teams run what, and where agents actually stick — so the rollout is managed, not hoped for.
Multiplayer, so adoption actually spreads.
Your AI-savvy people get leverage; everyone else gets agents that just work. Admins serve the whole company from one place, and any agent can be worked on together — live, like a document.
- Real-time collaborationEarly adopters build alongside colleagues live — coaching happens in the work, not in a slide deck.
- Team-owned agentsEveryone sees what the agent was told, by whom, and what changed — a live audit log.
- “Who did this?”Always answerable: which agents were involved, and which people set them up.
- Adoption you can measureMetrics show which teams use what, so you invest in what's working.
05Agents are a team sport here.
Admins and early adopters work on agents with colleagues live, like a shared document — coaching happens inside the work.
Teams own agents together: everyone sees what the agent was told, who changed what, and who's working on it right now — a live audit log.
You never pay for useless attempts.
Pricing is based on outcomes. We take responsibility for throttling agents that aren't succeeding — failed experiments are our cost, not yours.
Outcome-based pricing
Pay for work that lands, not for attempts. Our incentives point the same way yours do.
Self-throttling
Agents that stop succeeding are automatically slowed and flagged — before the bill grows, not after.
Open models, in Europe
Run open-source models on our EU cloud: better unit economics, and your data stays in Europe.
06Any model. Your data in Europe. Our incentives aligned.
Run any model and swap freely. Tasks route to the model that earns its cost — frontier where it matters, efficient where it doesn't.
Open-source models, hosted by us in Europe: better unit economics, and your data never leaves the EU.
Pay for work that lands. Failed attempts are our problem — which is exactly why we built the operations layer this well.
Agents that stop succeeding are automatically slowed and flagged. You never bankroll a flailing loop.
Hard limits per agent, visible burn, no surprises at the end of the month.
The enterprise layer.
Run any model and swap freely — frontier or open-source, routed per task.
Hosted in the EU, with open-model inference on our own European cloud.
Every action an agent takes is logged and reviewable — and so is every change a person makes to an agent.
Decide who can build, edit, publish — or simply use — each agent.
Thirty minutes. Your workflow. On the board.
The fastest way to evaluate Agenthub is to watch your own work run on it.