orcha
Parallel model orchestration as an execution workflow.
A coordination layer for distributing work across multiple AI agents and forcing their outputs through structured synthesis.
Tasks are routed across specialist agents with bounded responsibilities.
Outputs return to a synthesis path that compares evidence, resolves disagreements, and decides follow-up work.
The operator sees the shape of the work rather than receiving a single opaque answer.
Context
Different models fail differently. That makes parallel model work valuable only if the workflow can preserve context, assign roles, and synthesize results deliberately.
orcha treats model diversity as an execution resource instead of a novelty.
Constraints
Parallelism can multiply noise if roles are unclear.
Context handoff needs discipline or every agent solves a slightly different problem.
The final synthesis must be stronger than the loudest individual model response.
Tradeoffs
Designed around explicit workflows instead of hiding orchestration behind one chat surface.
Favored traceable decisions over maximum automation.
Kept human review in the loop for high-impact choices.
Outcomes
Created a concrete agentic-systems proof point with product and infrastructure dimensions.
Showed practical thinking about orchestration, critique, and model specialization.
Lessons
Multi-agent systems need boundaries more than they need more agents.
The UX of disagreement is part of the architecture.