AI teammates that plan, execute and deliver real work. Connect your tools, assign outcomes, and get work done — autonomously.
Waspid Agents
10:30 AM
Workflow completed
Chip Design Agent
9:45 AM
Floorplan locked
Hardware Control Agent
9:20 AM
GPIO bus mapped
Robotics Code Agent
8:50 AM
Motion control compiled
Workflow Progress
Prompt
Outcome defined
Plan
Steps generated
Execute
Tasks in progress
Verify
Results validated
Complete
Workflow done
Live Logs
10:30:18 Connected to tools
10:30:21 Planning workflow steps
10:30:25 Executing: Clone repository
10:30:32 Executing: Run tests
10:30:41 Executing: Generate report
10:30:50 Workflow completed successfully
Resources
Tokens Used
45,231
Time Taken
02:13 min
Tasks Executed
12
Success Rate
100%
From prompt to production
Describe the outcome. WASPID plans the steps, executes across tools, and when a piece breaks, the Loop Engineer rebuilds it — block by block — until the workflow stands on its own.
Install the CLI in one line:
Requires Node.js 18+ · works on macOS, Linux & Windows. Then run waspid login to connect your account.
One Ecosystem
A live fleet of WASPID agents, constantly shuttling out to the hardware and reporting back to the control center — every agent moving on its own path, at its own speed.





See it run
An always-on look at the AI workforce assembling, executing, and self-healing — no play button, no pausing, just the loop.
The builder turns a prompt into an agent that can choose models, call connected tools, pause for approval, and keep running.
MCP
Tools and data.
Approval
Human control.
Live
Ready to run.

Scroll to zoom · Drag to explore
100%
Live network
From one agent to millions.
One head, every model
One orchestrator routes each stage to whichever connected model fits it best — never a model you haven't actually connected. Build always runs start to finish on one model (a tool-calling run can't safely hop providers mid-stream); Review deliberately uses a different one for a real second opinion.
A reasoning-first model turns your prompt into a short, concrete plan — no code yet.
The unified runtime executes the plan for real — reads, edits, and runs commands in your workspace.
A genuinely different model — not the author — checks the diff for risks or missed requirements.
A reviewed diff, a suggested commit message — you decide when it lands. Nothing auto-commits.
waspid ship "add rate limiting to the API"Built for Enterprise Scale
About WASPID
WASPID isn't just a name — every letter is a layer of the enterprise AI workforce OS, from the humans it augments to the moment it goes live.
A coordinated team of AI workers, not a single bot — organized by operational lane.
Frontier models plan, reason, and execute across your tools with human-grade judgment.
Shared state, memory, and orchestration bind every agent into one reliable system.
One control plane across CLI, VS Code, and the cloud — build once, run anywhere.
Observability, self-healing loops, and cost tracking baked into every execution.
Ship agents to production with RBAC, integrations, and enterprise-grade guardrails.
Observe the Autonomy
Loop Engineer Log
Step 1 Error in component 'Header.tsx': prop 'user' is undefined.
Step 2 Loop Specialist identified missing context provider.
Step 3 Generating patch for './app.tsx'… applied ✓
When an agent hits a roadblock it doesn't just stop. WASPID spawns a Loop Specialist — an agent with a dedicated context window focused solely on debugging the specific error, resolving it, and resuming the original flow.
Learn about Loop EngineeringTraditional Agents vs. WASPID
One Engine, Three Interfaces
Requires Node.js 18+ · works on macOS, Linux & Windows. Then run waspid login to connect your account.

Our Vision
Agents that architect and build applications.
Embedded code for edge devices and controllers.
AI-assisted design of physical systems.
Agent teams orchestrating robotic fleets.
Intelligent control of physical machines via one AI OS.
One unified operating system where autonomous agents collaborate across cloud, edge, enterprise applications, and robotic systems.
Multi-Agent System Vision · 9 live
The platform is intentionally modular so businesses can compose AI workers by function, connect them to systems, and scale them into reusable enterprise workflows. These categories are not isolated bots — they share orchestration, observability, integrations, execution state, and deployment infrastructure.
AI Agents
AI Callers
AI Support Agents
AI Workflow Agents
AI Sales Teams
AI Browser Operators
AI Operations Systems
AI Monitoring Systems
AI Orchestration Systems