Introducing SwarmClaw: Your AI Department, Ready to Deploy

We built AgentAwake to solve a specific problem: AI amnesia. Every time you opened a new chat, your agent had no idea who you were, what you were building, or what decisions you had already made. You had to start over every single time.

We fixed that. The memory architecture in AgentAwake works. Agents that know your business, remember your preferences, and compound intelligence over time instead of resetting to zero.

But then we hit a new wall.

A single agent, no matter how good its memory is, can only do so much. It can't research something and write about it and review its own writing and ship a deliverable all at the same time. It works sequentially. It gets tired in the middle of complex tasks. And there's no one checking its work.

Real work doesn't happen like that. Real work happens in teams.

Here is what a capable solo AI agent looks like in practice: you ask it to research competitor pricing, write a positioning document, draft three email sequences, and review all of it for consistency. It does each task. Some of them are good. Some drift. The research from step one starts to feel disconnected from the copy in step four. And because the same agent wrote everything, it cannot spot its own blind spots.

This is not a model problem. GPT-5 will not fix it. The problem is structural. A single agent reviewing its own work is like a writer editing their own manuscript the hour after finishing it. The mistakes are invisible because they made sense when they were written.

The other problem is specialization. A general-purpose agent is pretty good at a lot of things. A specialized agent, tuned for a specific job with relevant context, is much better at that one thing. You would not want your accountant doing your copywriting. Same principle.

SwarmClaw is what happens when you take everything we learned building AgentAwake and apply it to teams instead of individuals.

SwarmClaw is a multi-agent orchestration platform where AI agents work together on real tasks. Not just in parallel, but collaboratively, with quality control built into the process.

Persistent Memory That Compounds

Each agent in your swarm carries its own memory. A research agent that has been working on your industry for three months knows things a fresh agent does not. That knowledge compounds. Every task it completes adds to what it knows next time. You are not just paying for compute; you are building institutional knowledge.

A 3-Tier Quality Review System

This is the part that changes everything. When any agent completes a task, it does not ship directly to you. It goes to a chief agent first, one with broader context and explicit review instructions. The chief flags issues, requests revisions, and only passes work when it meets the standard you set. For high-stakes output, it escalates to a second review tier.

To put it concretely: your copy agent drafts a launch email. The chief copy reviewer checks it against your brand voice and the brief. If the hook is weak, it goes back for revision. You only see the version that passed review. That is very different from getting a first draft and having to catch every mistake yourself.

Pipeline Blueprints for Multi-Step Workflows

Research, then plan, then build, then review. These are templates for how agents hand work to each other. You define the workflow once and run it whenever you need it. A research-to-report pipeline runs research agents against a topic, hands off to an analyst agent for synthesis, passes to a writer agent for a readable document, and routes to a reviewer before delivery. You trigger one pipeline and get a finished report, not ten tabs of raw research you still have to process yourself.

Real-Time Kanban Dashboard

You can watch your swarm work. Each task lives on a board. You can see what is in progress, what is in review, what is done, what is blocked. If something goes sideways, you see it before the deadline, not after. This is how you manage a team you cannot tap on the shoulder.

Discord and Slack Native

Notifications land where your team already lives. An agent finishes a draft and messages the relevant channel. A task needs your approval before it can move forward and it pings you directly. You approve or reject from within Slack or Discord. No new tool to live in, no dashboard you have to remember to check.

BYOK Pricing

You bring your own API keys. We do not mark up LLM costs. You pay us for the platform and you pay OpenAI or Anthropic directly for the compute. This keeps costs honest and predictable.

Plans start at $49 per month for small swarms and go up to $399 per month for larger teams with more concurrent pipelines and priority support. Every plan is BYOK. You pay for the platform, not a markup on AI usage.

SwarmClaw is launching Q2 2026. We are opening a waitlist now. Early waitlist members get beta access and locked-in pricing before public launch.