Agent-development infrastructure
Flowplane
A workflow engine for AI-assisted software development with isolated execution, durable work tracking, and persistent knowledge.
- Audience
- AI-assisted developers and technical teams
- Role
- Architect and principal builder
The problem
Autonomous development needed stronger boundaries and memory.
Long-running coding work required more than a prompt loop. Teams needed explicit workflows, isolated execution, durable plans, and knowledge that could distinguish decisions, patterns, lessons, and superseded context.
The solution
A workflow engine joined orchestration, isolation, and knowledge.
I built a development platform with workflow orchestration, work tracking, isolated container execution, a terminal interface, and persistent knowledge. Decisions and lessons could be searched, tagged, related, and superseded as the work evolved.
Outcomes
The result was a change in operating capacity.
These outcomes describe what this implementation made possible. They are not generic product promises.
Created an end-to-end operating surface for AI-assisted development workflows.
Isolated execution from the host environment while preserving work state.
Retained decisions, patterns, learnings, context, and plans for future sessions.
How it worked
The architecture followed the work.
The system was organized around an operating sequence people could understand, govern, and improve.
Orchestrate
Coordinate work as explicit, durable workflows.
Isolate
Run implementation inside controlled container boundaries.
Track
Keep plans and work state visible through execution.
Learn
Preserve and evolve the knowledge produced by the work.
My contribution
I built Flowplane as infrastructure for responsible autonomy.
The architecture reflects a consistent principle in my work: give agents enough structure to move quickly while keeping execution, evidence, and decisions understandable to people.
Return to all work