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Private Agentic Cloud

As much Super Intelligence
as possible.

Put a fleet of AI agents to work in your own private cloud. Each one takes a job, works your tools and your documents with every frontier model behind it, and reports back when it is done.

Design custom agents

Build the agent your work needs, then fine-tune it.

General intelligence answers anyone’s question; it does not know your work. A BusyBot agent is intelligence you shape — the brain it thinks with, the mind it carries, the tools it may reach, the way it grows — fine-tuned to your workloads, not a one-size harness you cannot open.

Or start with a brief Describe the agent you want and the Builder Assistant proposes a role, a skill, tools and knowledge — you accept the proposal part by part.

How an agent works →Agent, definedAgent mode

Agent Brain

Any catalog chat model from OpenAI, Anthropic, Google or xAI runs the main loop, at a thinking level you set — off to max. The loop’s supporting slots run on one second provider you choose.

Agent Mind

Four instruction fields, written in plain language and edited in place: Role, Skill, Context, Format. Who the agent is, how it works, what it knows about your business, how it answers.

Agent Reach

Tools from 452 nodes, each with its own credential, called when the agent decides to. Saved workflows attach as single tools — a whole pipeline in one step. Knowledge bases are searched mid-run, whole documents read and cited.

Learned over time

Runs leave lessons: a per-tool Do / Don’t ledger of real outcomes, Growth Reviewer proposals you apply with a diff, Training against a plan you review first. Growth off means stop changing, never forget.

Workflows

Automation that runs as a job, exactly as drawn.

Drag 452 nodes onto a canvas and connect them. If you have built workflows on a canvas before, you already know this one. Independent branches run in parallel; every run executes as an isolated Kubernetes Job on its own node pool. Start one by hand, on a schedule, from a trigger — or let an agent in your fleet call the whole workflow as a single tool.

  • 452 nodes in 11 categories — integrations, model calls, data transforms, files, and the core routing nodes.
  • 85 triggers — webhook, polling, manual. A webhook gives the workflow a URL; a polling trigger keeps state so nothing runs twice.
  • Schedules — Trial 2 at ≥ 60 min · Starter 10 at ≥ 15 min · Pro 100 at ≥ 1 min · Enterprise unlimited at ≥ 1 min. “Run now” and the scheduled fire are one code path.
  • Isolated by construction — each run is its own Kubernetes Job; code nodes run sandboxed in V8 isolates or containers.
  • Credentials — AES-256-GCM encrypted at rest, never logged, checked against the owning account on every use.
  • Workflows as tools — an agent attaches a saved workflow and calls a whole pipeline as one step. Groups keep them organised.

Browse all 452 nodes → Workflow, defined Trigger Schedule

Knowledge bases

Your documents, read whole and cited.

Upload PDFs, Office files, code, text, audio and video. Each file is extracted to markdown, summarised in passes — per document and per knowledge base — and indexed. An agent searches it through a dedicated search subagent that reads whole documents mid-run, follows references, and cites what it used.

Documents → knowledge base → search subagent PDF Master services agreement XLSX Pricing 2026 DOCX QBR notes CODE export_handler.py MP4 All-hands Q3 M4A Discovery call Knowledge base 6 files · grouped · health checks Extracted to markdown Summarised per document, per KB Indexed draft → processing → ready Search subagent a ReAct loop with its own tools Search the index Read the whole document Follow a reference Cite what it used reads whole documents · cites what it used
  • Status you can watch — draft → processing → ready, with grouping, health checks and a changelog per knowledge base.
  • Grounded answers — the source passage is available rather than implied; the subagent is a ReAct loop with its own tools.
  • In workflows too — the knowledge-base tools are also nodes: Knowledge Base and Search Knowledge Base.

Knowledge base, defined →

Every frontier model

Every frontier model, every conversation.

One workspace with every frontier model behind it. Chat with the model you want and switch mid-thread with your context intact, or burst one prompt to all of them at once — the same models your agents choose their brain from.

4 frontier providers · 67 models · one workspace

One thread. Any model, mid-thread.

Chat

Switch from GPT to Claude to Gemini to Grok inside one conversation and your context travels with you — each model picks up exactly where the last one left off. Conversations, files and instructions live in your private cloud, and Model Radar probes the providers daily so no listed model is a dead one.

One prompt. Every model, at once.

Burst

Send one prompt and every model answers together — GPT, Claude, Gemini and Grok streaming side by side in one place. Pick a Lite, Regular, Reasoning or Custom squad, compare the takes as they arrive and keep the one you like best.

Chat, definedBurst, defined →

Integrations

Browse all 452 nodes →

Each one is a workflow step and an agent tool: wire it after a trigger, schedule it, or hand it to an agent that decides when to call it. Every page lists the real operations, parameters and outputs.

How to connect each service — 295 credential types How workflows and agents fit together From the blog

Pricing

Trial

No subscription — try it on your own terms

$20prepaid, minimum

Full-priced tokens

  • Credits good for 12 months
  • Workflow compute at $0.10 per hour
  • Storage at $0.40 per GB-month
  • 2 scheduled workflows, as often as every 60 min
  • BusyBot’s model keys, metered per call
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Recommended

Starter

Monthly, cancel anytime

$20per month

50% off tokens

  • $15 a month in usage credits
  • 10 workflow compute hours a month, then $0.10/hr
  • 10 GB storage included
  • 10 scheduled workflows, as often as every 15 min
  • Bring your own model keys
Create an account

Pro

Your own private cloud, at full speed

$100per month

75% off tokens

  • $70 a month in usage credits
  • Unmetered workflow compute
  • 100 GB storage included
  • 100 scheduled workflows, as often as every 1 min
  • Bring your own model keys
Create an account

Enterprise

Teams, with a dedicated URL

$40per seat / month · 5 seats minimum

90% off tokens

  • Pooled org credits
  • Unmetered workflow compute, org pool
  • 1 TB storage, pooled across the org
  • Unlimited schedules, as often as every 1 min
  • Bring your own model keys
Create an account

See full pricing →

Questions

Frequently asked questions

  1. Can one agent think with two providers?

    Yes. An agent’s brain — the model that runs its main loop — is any catalog chat model from OpenAI, Anthropic, Google or xAI, at a thinking level you set from off to max. The loop’s supporting slots (plan, replan, reflect, strategy, extract) run on one second provider you choose, which may differ from the main loop’s. Two frontier providers, one agent.

  2. What happens when a provider withdraws a model?

    Model Radar probes all four providers’ model APIs every day, so the catalog never lists a dead model: whatever you can pick in chat, in a Burst squad or as an agent’s brain is a model that answers. The fleet at the foot of this page is that live catalog, by name.

  3. Can an agent run while I sleep — and who can stop it?

    Your workflows can: a workflow runs on a schedule (Trial 2 at ≥ 60 min · Starter 10 at ≥ 15 min · Pro 100 at ≥ 1 min · Enterprise unlimited at ≥ 1 min) or from one of 85 triggers, every run an isolated Kubernetes Job, and an agent can call a saved workflow as a single tool. An agent run starts when you hand it a job; it then works on its own — plans, calls tools in batches, reflects, asks only when it needs you — and streams into the sidebar’s live-run feed, where you can stop it mid-run from anywhere. Every run is metered and credit-gated against your balance.

  4. Can an agent ask me before it acts?

    Yes. Mid-run an agent may ask you a question — with choices or a short form — and pause until you answer, then carry on with your answer in hand. Before each batch of tool calls it shows a preamble of what it is about to do; after each batch it reflects and may write down a finding; it may replan. All of it streams live into the conversation.

  5. What does an agent carry from one run to the next?

    Lessons. Each tool keeps a Do / Don’t ledger of outcomes on your account — what worked, what failed and why — written by the agent in flight and by a Notekeeper after the run. A Growth Reviewer proposes new versions of the Skill and Role into a suggestion queue with evidence and a diff; you apply, edit or dismiss each one, and every version is kept — any restorable, any freezable. Training sessions let the agent practise its tools against a plan you review first. Growth off means stop changing, never forget.

  6. Does an agent know what day it is?

    Yes. A temporal header gives every iteration of the loop the current date and time in your time zone, so “this week”, “by Friday” and “since the last run” mean what you mean, whenever the run starts.

  7. Where does my data live?

    In your own private workspace on BusyBot’s Kubernetes cluster in the US. Each customer’s data is isolated to its own account (hash-tag isolation); credentials for the 295 credential types are AES-256-GCM encrypted at rest, never logged, and checked against the owning account on every use; every workflow run executes as its own isolated Job on its own node pool, and code nodes run sandboxed. Conversations, files and instructions stay in that workspace as you move between models.

  8. How does pricing work?

    Tokens at your plan’s markup over provider list price, workflow compute by the hour beyond what your plan includes, and storage measured nightly, all settling into one credit balance. Trial is prepaid from $20 with credits good 12 months; subscriptions start at $20/month. Bring your own model keys on Starter, Pro, Enterprise.

Every frontier model

The whole frontier, by name.

4 frontier providers · 67 models · one workspace

OpenAI 35 Anthropic 10 Google 16 xAI 6
  • chat GPT-5.5
  • chat GPT-5.5 Pro
  • chat GPT 5.4
  • chat GPT 5.4 Pro
  • chat GPT-5.4 Mini
  • chat GPT-5.4 Nano
  • chat GPT 5.2
  • chat GPT 5.1
  • chat GPT 5
  • chat GPT 5 Pro
  • chat GPT 5 Mini
  • chat GPT 5 Nano
  • chat GPT 4.1
  • chat GPT 4.1 Mini
  • chat GPT 4.1 Nano
  • chat GPT 4o
  • chat GPT 4o Mini
  • reasoning o4-mini
  • reasoning o3
  • reasoning o3-mini
  • reasoning o1
  • image GPT Image 2
  • image GPT Image 1.5
  • image GPT Image 1
  • image GPT Image 1 Mini
  • audio GPT-4o Transcribe
  • audio GPT-4o Mini Transcribe
  • audio Whisper
  • audio GPT-4o Mini TTS
  • audio TTS-1
  • audio TTS-1 HD
  • chat GPT 5.6 Luna
  • chat GPT 5.6 Terra
  • chat GPT 5.6 Sol
  • chat GPT 6 Astra
  • chat Claude 4.5 Sonnet
  • chat Claude 4.5 Haiku
  • chat Claude 4.5 Opus
  • chat Claude 4.6 Sonnet
  • chat Claude 4.6 Opus
  • chat Claude 4.8 Opus
  • chat Claude 4.5 Haiku (2025-10-01)
  • chat Claude 5 Opus
  • chat Claude 5 Sonnet
  • chat Claude Fable 5.1
  • chat Gemini 2.5 Flash Lite
  • chat Gemini 2.5 Flash
  • chat Gemini 2.5 Pro
  • chat Gemini 3.1 Flash Lite
  • chat Gemini 3 Flash
  • chat Gemini 3.1 Pro
  • chat Gemini 3.5 Flash
  • image Nano Banana Pro
  • image Nano Banana 2
  • music Lyria 3 Pro Preview
  • music Lyria 3 Clip Preview
  • audio Gemini 3.1 Flash TTS Preview
  • audio Gemini 2.5 Pro Preview TTS
  • audio Gemini 2.5 Flash Preview TTS
  • chat Gemini 3.8 Flash
  • chat Gemini 3.5 Flash Lite
  • chat Grok 4.3
  • image Grok Imagine Image
  • image Grok Imagine Image (Quality)
  • video Grok Imagine Video
  • chat Grok 4.5
  • chat Grok 4.6

Ride the frontier

Your private cloud, built for agents.

See pricing → Create an account