How is this different from observability tools?

Observability tools show you what happened. Fruxon controls what happens next — versioning, evals, guardrails, deploy, and rollback from one platform. Knowing isn't enough. You need to act.

How do you handle agent unreliability?

We don't try to make it never wrong. We make it able to notice it's unsure, and ask. Everything else — evals before deploy, human signature on high-stakes calls, one-click rollback — is there for the times it doesn't notice.

What if my team doesn't want to be interrupted by an agent?

It only asks the person whose lane it is, and only when it's actually stuck — a relevance gate means it stays quiet in busy channels. Every consult has an urgency cap and a timeout: no answer, and it falls through to the next person on the ladder, then to a safe default. Nobody is on call for an agent.

Why not build our own agent infrastructure?

You can. Most teams spend 3–6 months on it — alone, while trying to ship everything else. That's not a plan. That's a tax on every feature you're not building.

Do I need to rebuild my agents in Fruxon?

Agents are built in Fruxon rather than imported. That means declaring what the agent is — model, tools, knowledge, guardrails — instead of writing and maintaining the code around it. What carries over is the thinking: your prompts, your datasets, the systems it needs to reach. What you don't rebuild is everything underneath.

Is my data safe with Fruxon?

We don't train on your data. It's encrypted at rest and in transit and isolated per organization, and your agents, prompts, and datasets belong to you. What a model provider does with what reaches it is governed by their terms rather than ours — so if that matters to you, bring your own keys and the relationship is yours to set.

What if I want to leave Fruxon?

Your agents, your data, your call. Export configs, prompts, datasets, and traces — no exit fees, no lock-in contracts. We keep customers by being useful, not by making it hard to leave.

The ops platform for AI agents

The org chartfor AI agents.

Even a good model doesn't know who it answers to, who it can ask, or what it's allowed to decide. Fruxon puts agents on the chart — with a lane, people they can ask when they get stuck, and a remit that widens as they earn it.

Watch it ask a human

One — the stakes

Building the agent took an afternoon. Trusting it is the hard part.

You already know it can draft the reply. The question is what it does at 2am when a customer asks about a policy exception nobody ever wrote down. Alone, it has one move: answer confidently and hope. One made-up refund policy and it's back in the sandbox for a quarter.

No model knows what your ops lead decided on Tuesday. That gap doesn't close by buying a better one.

Two — the turn

It wasn't underpowered. It was alone.

A new hire doesn't know your return policy either. On day one they turn around and ask someone — and nobody thinks less of them for it.

That's the whole difference.

Three — what changes

Fruxon gives an agent the one thing every competent new hire has.

An organization around it: a directory of real people, what each of them owns, who it may ask, in what order, through which channel — and what happens when nobody answers.

Not a smarter model. A colleague instead of a loner.

Alone, with no one to ask — answering anyway

Act

It does the work — in the systems you already run.

Not “sends a notification for someone else to action.” It issues the refund in Shopify, closes the ticket in Zendesk, writes the note to Salesforce, runs the query against your Postgres, and replies on the channel the customer actually used. Connect a system once for the whole org; every agent can use it from then on.

Read-only until you say otherwise.

Sandbox credentials by default — it can look at production before it's allowed to touch it.

Wherever your customers already are.

Slack, Teams, WhatsApp, Telegram, email, or embedded in your product.

It listens more than it talks.

Drop it into a busy channel and it follows along without joining in — until a message is actually for it.

Integrations

Connects to your stack.

Pre-built tools for the services your team already uses.

Slack

GitHub

Jira

Google Drive

Salesforce

MongoDB

Notion

Linear

Grafana

Discord

Google Chat

Confluence

PostgreSQL

HubSpot

Airtable

Shopify

Stripe

Datadog

Sentry

GCP

BigQuery

Slack

GitHub

Jira

Google Drive

Salesforce

MongoDB

Notion

Linear

Grafana

Discord

Google Chat

Confluence

PostgreSQL

HubSpot

Airtable

Shopify

Stripe

Datadog

Sentry

GCP

BigQuery

Google Calendar

Gmail

Mixpanel

Monday

MySQL

SAP

Zendesk

Zoho CRM

Google Maps

Google Ads

Coralogix

Apollo

Mailchimp

Calendly

Redis

Supabase

GCP Logging

gVisor

Loops

Typeform

Google Calendar

Gmail

Mixpanel

Monday

MySQL

SAP

Zendesk

Zoho CRM

Google Maps

Google Ads

Coralogix

Apollo

Mailchimp

Calendly

Redis

Supabase

GCP Logging

gVisor

Loops

Typeform

Don't see yours? Import tools from any MCP server or request an integration.

Ask

When it's unsure, it asks the person who'd know.

You introduce the agent to the team: who's on it, which calls are theirs to make, and where to reach them. When something lands outside what it knows, it doesn't guess — and it doesn't dump the whole thing on you.

It works out who'd actually know.

Pricing exceptions go to Dana. Anything touching infra goes to Maksim. Returns and exceptions are Maya's lane — so that's who gets the Slack DM, not whoever is on the ticket queue.

And it waits for the answer before it replies.

No answer inside the window? It falls through to the next person on the ladder, then to a safe default. Nothing hangs on someone's lunch break — and the gap it just found is now something you can close.

A knowledge search in a Fruxon trace: the query is gift return promotional window after 30 days, and the result reads that no matching passage was found for gifts purchased during a promotional period.

Confidence below threshold — not answering yet

The real thing

The run above is one conversation on order HV-40219. What made it possible is the roster behind it — and the four rules the agent follows when it reaches the end of what it knows.

Where Maya came from — every person this workspace can reach, and the Slack handle or address that reaches them.

Routed by expertise, not by ticket queue.

Pricing exceptions go to Dana. Anything touching infra goes to Maksim. Set once on the person — not re-tagged on every agent that might need them.

Nobody answers? It falls through.

Next person on the ladder, then a safe default. Nothing hangs on someone's lunch break.

Out of its depth? It hands over the conversation.

A person takes the thread mid-sentence with the full history, and hands it back when it's resolved.

Strangers don't get served.

An unknown sender becomes an access request you approve — not a silent new user.

Learn

What it asked about last month, it handles this month.

Every question it had to escalate is a gap you can now see. Close it — add the document, approve the action, widen the lane — and it stops asking. You're never choosing between “fully autonomous” and “useless” on day one.

You decide what it decides alone. Per action: handle it, ask first, or just tell someone afterwards.

Prove the next version is better before it ships. Every change runs against your own dataset; evaluations gate the deploy.

Wrong call? One click back. Every save is an immutable revision.

Intervention narrows as the lane widens — and stops narrowing where a human signature is the design, not a limitation.

Watch

Everything it did is on the record. Including what it cost.

Every run traced end to end — what it saw, which tools it called, what it decided, how long it took, what it spent. Set a monthly ceiling per agent and a runaway loop becomes a line item instead of an incident.

Nobody else offers this

Running agents for your own customers?

Tag each run with the account it served and get cost, error rate, and p95 split per customer, side by side.

Tag the run

Every execution already carries the environment it ran in.

Split the view

Cost, error rate and p95 per environment — your customers, side by side.

Cap the spend

A monthly ceiling per agent turns a runaway loop into a line item.

Executions, in production. Every run: duration, cost to four decimal places, the agent version that served it, and the environment it ran in. The 26 m 7 s row is Elin's conversation from the Ask section.

That row, opened — the policy search that found nothing, the match to Maya, 26 minutes waiting on her, then Shopify, Zendesk and the reply to Elin.

Trust

Least privilege, by default.

Trust Center

Sandboxed with minimum permissions. You name every tool it can reach.

Verification at every boundary. Prompt-injection checks, output validation, a human signature on high-stakes calls.

iii

Every action attributable. Every tool call logged, traceable, exportable.

Secrets stay secrets. Referenced by name, never pasted into a prompt.

We don't train on your data. What a model provider does with what reaches it is governed by their terms, not ours — bring your own keys and that relationship is yours to set.

Where to start

Two queues. Start with the one that's already backed up.

The split isn't by department — it's by who's waiting. A customer on the other end of a conversation, or a record sitting in a queue until someone gets to it. The agent works the same way in both. It just escalates to a different person, and it can afford to wait a lot longer for one of them.

Support · Success · Onboarding

Front Office

Someone is waiting on the answer, and a wrong one costs you the relationship.

Finance · IT · Fulfillment · Vendor review

Back Office

Nobody is waiting on the phone. That is exactly why it slips.

Start it narrow. Widen it when it earns it.

One channel, one job, and permission to ask about everything. You'll know inside a week whether it deserves more — and so will it.

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Questions we hear

Have more questions? Contact us