Agent Swarm
Four specialised AI agents — Lucy, John, George, and Ringo — that operate as a coordinated engineering team, executing work autonomously around the clock.
Each agent runs in its own isolated container with access to your GitHub repository and codebase. They pick up issues, write code, open pull requests, and hand off when blocked — a full development team, minus the standups and Slack messages.
Meet the Swarm
Each agent has a defined speciality, its own container, and a clear role in the team.
Lucy acts as the swarm's team lead. She handles cross-cutting concerns: merging approved PRs, re-triaging stalled work, resolving merge conflicts, and reassigning tasks when agents are blocked. If something falls through the cracks, Lucy catches it.
John specialises in backend systems. He writes and tests server-side code, handles database schema changes, implements API endpoints, and tracks down production bugs — all by reading your codebase and GitHub issues autonomously.
George owns the frontend. He builds React components, wires up state management, fixes visual regressions, and keeps the UI consistent with design specs — iterating on his own branch until the work is ready for review.
Ringo keeps the infrastructure running. He maintains CI/CD pipelines, manages Kubernetes manifests, configures monitoring and alerting, and hardens security posture — so the platform your agents run on is always production-grade.
Agents at Work
Below is the actual DigiSwarm dashboard, captured during our public AI Storefront Benchmark — four AI models building the same app, side by side, on one platform. See the study →
The Execution Loop
From open issue to merged PR — fully automated.
Architecture
Each agent runs in an isolated Docker container (or Kubernetes pod) with a scoped GitHub token, the AI model of your choice, and read/write access to your repository.