Introducing operum.ai: AI Agent Orchestration for Builders
6 specialized AI agents. One orchestrator. Ship products faster with autonomous development, marketing, and community workflows.
For the past six months, I’ve been running an experiment: what if instead of using AI as a coding assistant, I structured it as a development team?
Not one agent doing everything. A team of specialists—PM, Architect, Engineer, Tester, Marketing, Community—each with clear responsibilities, coordinating through defined protocols.
The result is operum.ai, now available for download.
The Problem With Single-Agent Workflows
Most AI coding workflows follow the same pattern:
- Developer has a task
- Developer opens AI chat
- Developer provides context
- AI generates code
- Developer reviews, fixes, iterates
- Repeat
The developer remains the bottleneck—routing every request, maintaining all context, coordinating all work. AI assists, but doesn’t autonomously drive.
As I wrote in Closing the Delegation Gap, Anthropic’s research shows developers delegate only 0-20% of tasks fully to AI. The architecture is the constraint, not the model capability.
The operum.ai Approach
operum.ai structures AI as a team rather than a tool:
| Agent | Focus | Responsibilities |
|---|---|---|
| PM | Coordination | Manages pipeline, coordinates agents, reports to founder |
| Architect | Design | Reviews requirements, designs systems, guides implementation |
| Engineer | Implementation | Writes code, creates PRs, implements features |
| Tester | Quality | Reviews code, runs tests, validates changes |
| Marketing | Growth | Creates content, manages positioning |
| Community | Support | Handles user issues, engagement |
Each agent has:
- Defined scope — Clear boundaries on what they handle
- Persistent context — Memory that survives sessions
- Communication protocols — Structured handoffs between agents
- GitHub integration — Direct interaction with issues and PRs
How It Works
- Connect your repository — operum.ai integrates with your GitHub projects
- Define goals through PM — Describe what you want to achieve
- Agents execute autonomously — Work happens without constant oversight
- Review and approve — You merge PRs and approve marketing content
The founder becomes the approver, not the executor. Strategic decisions remain human. Routine implementation becomes autonomous.
Dogfooding: operum.ai Built With operum.ai
The agents are currently building operum.ai itself. Current stats from the dashboard:
- 17 issues triaged and managed
- 10 PRs created and merged
- 254,000 tokens of autonomous work
When I wake up, I check what shipped overnight. The mental model shifts from “what should I work on today” to “what did the team accomplish while I was away.”
What Makes This Different
Local-first architecture. Agents run on your machine, not in the cloud. Your code stays local. No data residency concerns.
Specialized over generalist. Six focused agents outperform one agent trying to do everything. Expertise emerges from constrained scope.
Coordination built-in. Agent-to-agent handoffs are first-class. Engineer finishing triggers Tester automatically. No human routing required.
Transparent operation. Every agent action is logged. You can see exactly what happened, why, and when.
Download
operum.ai is available now. Built for:
- Solo founders building products who want to ship faster
- Small teams who need more capacity without more headcount
- Developers interested in multi-agent architectures
If you’re building something and want an AI team working alongside you, download operum.ai — available now for macOS and Linux.
What’s Next
Over the coming weeks, I’ll be documenting:
- How agents coordinate on real tasks
- Metrics from production usage
- Patterns that work (and don’t) for multi-agent development
This is the direction I believe development is heading—from AI-assisted to AI-augmented teams. operum.ai is my bet on that future.
Building with AI agents? Follow the journey on LinkedIn or Twitter.