Building the Visual Layer for Multi-Agent AI Teams

There's a gap in the AI agent ecosystem that nobody seems to want to talk about: the tooling is terrible.

Not the models - those are extraordinary. Not the frameworks - OpenClaw, CrewAI, AutoGen, LangGraph all do interesting things. The gap is what happens between "I have a multi-agent system that works on my machine" and "this is running in production, making money, and I can see what it's doing." That middle layer - configuration, deployment, monitoring, iteration - is where most agent projects quietly die.

We've been building Claws to close that gap.

The problem we kept running into

When we started working with OpenClaw internally, we noticed a pattern. Getting a multi-agent team running locally was the easy part. The hard part was everything else: managing API keys across providers, wiring up tool connections, deploying containers, figuring out why Agent 3 was burning through tokens on a loop, understanding the actual flow of delegation between an orchestrator and its workers in real time.

Every team we talked to had the same experience. They'd get a proof of concept working, then spend weeks building bespoke infrastructure around it - deployment scripts, monitoring dashboards, configuration management - before they could put it in front of a customer or use it in production. The agent framework was maybe 20% of the work. The other 80% was plumbing.

So we asked the obvious question: what if the plumbing was the product?

What Claws actually is

Claws is a web platform for building, deploying, and monitoring multi-agent AI teams visually. The mental model is something like Webflow for AI agents - you work with a drag-and-drop canvas instead of YAML files, you deploy to managed cloud instead of wrangling Docker yourself, and you monitor agent activity through a real-time dashboard instead of tailing container logs.

The technical stack is Next.js 14 on the App Router with TypeScript throughout. The visual builder is built on React Flow - each agent is a node on a canvas, and connections between agents represent delegation flows with configurable conditions (on completion, on approval, on rejection, on schedule). The data layer is Prisma ORM over PostgreSQL on Neon. Auth is NextAuth with OAuth and magic links. Payments run through Dodo Payments for both one-time template purchases and monthly platform subscriptions.

Every agent team follows a universal architecture: one Orchestrator that manages delegation and human communication, a set of specialized Workers that execute domain tasks, and one Critic that reviews all worker output against a quality threshold. The Critic scores work on a 1–10 scale. Below the threshold, work gets sent back with actionable feedback - up to three revision rounds before escalating to a human. This pattern enforces quality without requiring the user to babysit the system.

20 templates across 5 categories

We didn't want to ship a blank canvas. Agent systems are hard to design from scratch - the orchestration patterns, the skill assignments, the model selection per agent, the quality thresholds - there's a lot of domain knowledge baked into a well-designed team.

So we built 20 complete Claw templates spanning five categories: money-making (trading, freelancing, e-commerce), creator tools (YouTube, Twitter/X, newsletters, podcasts), business operations (B2B sales, agency management, SEO, founder's chief of staff), vertical solutions (real estate, recruiting, medical practice, law firm, home services), and lifestyle (fitness, studying, job hunting, developer productivity).

Each template is a fully configured workspace - agents with defined roles, skill assignments, model selections, heartbeat schedules, and operating procedures. Users purchase a template, walk through a five-step setup wizard (configure AI model keys, connect tools, deploy, connect messaging channels, go live), and they're running.

The interesting one is the Quant Trader. It runs seven agents across market scanning, signal analysis, risk management, backtesting, and portfolio tracking. It operates in two modes: Alert Only (the default, where it surfaces trade signals for human decision) and Autonomous (opt-in, with hard-coded risk limits enforced at the container level - not by the LLM). The risk gate is deterministic. We were very deliberate about that. When real money is at stake, you don't let a language model decide the guardrails.

The marketplace model

Templates and skills are only part of the picture. We built a full marketplace layer where anyone can publish their own Claw templates and agent skills. Creators enroll through a profile system, go through a publish wizard, set their own pricing (free, or $19 minimum for paid templates), and keep 100% of their revenue. Zero commission - we only charge the platform fee.

Skills go through a three-layer vetting pipeline: automated security scanning, sandbox execution testing, and manual review for verified status. Community templates get scanned and reviewed before appearing in the marketplace. The trust system is tiered - unreviewed, scanned, verified, official - so users can make informed decisions about what they're installing.

The bet here is that the best agent configurations will come from domain experts, not from us. A recruiter who's been refining their hiring pipeline for six months will build a better Recruiter OS than we ever could. The marketplace gives them a distribution channel with no revenue penalty.

What we're thinking about

A few things we're actively working through:

Container infrastructure. Each deployed Claw runs in its own Docker container with a persistent volume for the OpenClaw workspace. We're building on DigitalOcean for the managed container layer. The orchestration - health checks, log streaming, restart policies, resource limits - is where most of the current engineering effort sits.

Cost transparency. Every API call from every agent gets tracked and attributed. Users see per-agent cost breakdowns on their dashboard: which model, how many calls, what it cost. When you're running seven agents with a mix of Haiku, Sonnet, and Opus calls, the bill can surprise you if you're not watching. We'd rather surface that clearly than have users discover it on their API provider's invoice.

The skill ecosystem. We imported and security-audited over 4,000 skills from the OpenClaw ecosystem. 177 got automatically unpublished for security concerns. The curation layer matters - an agent skill with filesystem access or network calls needs more scrutiny than a text-processing utility.

Building in public

Claws is a small team build. The entire stack - frontend, backend, infrastructure, design, product - is 7 folks. That constraint forces a kind of discipline: every architectural decision has to be simple enough to maintain solo, every component has to be reusable, every abstraction has to earn its complexity.

We're not pretending this is a research breakthrough. The models and the agent framework already exist. What we're building is the missing layer between "agents work" and "agents work in production, reliably, visibly, for people who aren't going to write deployment scripts." That's an infrastructure problem, not an AI problem. And infrastructure problems are solved with good engineering and stubborn attention to detail.

If you want to follow the build or try the platform: app.buildclaws.ai