The infrastructure behind 20x companies.
The best startups aren't just using AI — they're deploying AI agents across every function. Code, support, sales, ops. AllAgents is the runtime that makes that possible: persistent memory, sandboxed execution, tool access, and full control. So a 12-person team can outship a 200-person competitor.
The Shift
One-off prompts don't build 20x companies.
3–8
AI agents per engineer at top teams
4x
revenue growth, zero new hires
20x
smaller teams beating incumbents
See what 20x companies look like
How tiny teams are using AI agents to outship companies 20x their size.
What agents actually need
The real efficiency gain isn't asking AI for help. It's agents that start and execute workflows on their own — triggered by an incoming email, a scheduled time, a Slack message, a Sentry alert, or a human saying "go." This is what separates a chatbot from an AI employee. Everything below makes this possible.
Agents that remember past conversations, customer context, and decisions across sessions. Your company knowledge compounds over time, not resets every thread.
Agents don't just talk — they act. Execute shell commands, browse the web, update your CRM, create tickets, push code. Sandboxed and auditable.
Not "here's a suggestion." Agents that run the full loop: observe → decide → act → verify → report back. Autonomously, with human-in-the-loop when it matters.
Agents see Slack conversations, Sentry errors, GitHub activity, CRM data, and internal docs. They connect the dots employees would miss.
Every agent runs in its own container. No data bleed between employees, teams, or tenants. Enterprise-grade isolation by default.
Frontier models for hard reasoning, fast models for summaries and triage. Automatic model selection, fallback chains, and per-employee cost controls.
Every LLM call, every tool execution, every decision — traced and logged. When the agent updates a CRM record or creates a ticket, you know exactly why.
Anthropic, OpenAI, DeepSeek, Gemini — swap models without changing a line of config. The runtime is provider-blind; your agents aren't tied to one vendor's roadmap.
The problem we solve
Claude Code / Copilot / Co-work?
Great for individual productivity — but the agent runs on your employee's laptop. They can't close it, walk away, and let the agent finish. One person, one machine, one task at a time. That's not an AI employee, that's an AI pair programmer that blocks your hardware. You need agents running in their own environment while your team orchestrates them — not agents eating up their MacBook's CPU.
Self-host OpenClaw?
Closer — but then every team sets up their own VPS? Your sales lead doesn't know what a VPS is. Your ops person isn't going to SSH into a server and run CLI commands to configure an agent. And even for engineers, managing Docker containers, MongoDB, embedding pipelines, and model routing per-employee is a full-time DevOps job, not a side project.
ChatGPT / Claude.ai accounts for everyone?
Copy-paste in, copy-paste out. No shared memory, no tool access, no audit trail, no automation. Employees paste company data into personal accounts. Nothing compounds. Every conversation starts from zero. The future is agents with true agency that own entire workflows end-to-end — starting from the event that triggers them.
How it works
01
Define your agents
What tools can they access? What's their budget? What do they remember? What events trigger them? Configure per role, per team, or per employee.
02
Connect your stack
Slack, GitHub, Sentry, CRM, email, internal APIs. One-click integrations. Agents get read and write access to the tools your team already uses.
03
Deploy and stay in control
Every agent action to the outside world goes through human approval first. Granular permissions — read-only email, write access to Linear, no access to billing. A single dashboard shows every agent running across your org. No black boxes per employee. You see what they see, and nothing happens externally without your sign-off.
Who this is for
Stop rebuilding agent infrastructure. Focus on business logic, not sandboxing and retry loops.
Deploy agents that handle the boilerplate so your team can focus on what humans do best — relationships, strategy, judgment.
The jump from "we have a chatbot" to "every employee has an AI teammate" is an infrastructure problem. That's what we solve.
The 20x company isn't a theory. It's an infrastructure choice.
Your team is already using AI. Give them the infrastructure to do it right.