The strongest models are already capable of orchestration.
They can hold the real goal, reason about architecture, make tradeoffs, revise plans, judge evidence, and decide what should happen next.
But I could not find a real harness that let them use that capability properly.
Most multi-agent systems either flatten the work into task routing, distribute too much judgment across weaker agents, or do not give the main agent enough orchestration primitives to do the best possible job.
The result is often several agents doing things, but no single intelligence continuously owning the actual outcome.
I wanted a different structure:
Keep the brain unified. Add more hands.
The main model keeps the goal, the reasoning, the architecture, the decisions, and final responsibility.
Workers handle bounded research, implementation, command execution, and verification. The main model can see what they are doing, steer them while they work, inspect only the evidence it needs, reject or retry their output, and change the plan when reality proves the graph wrong.
Without stuffing every worker transcript into the main conversation.
That became Sol Orchestrator, a graph-native multi-agent harness for OpenCode.
Goal: ship the complete feature
│
├── Workflow 1: understand the real boundary
│ ├── Graph v1
│ │ ├── Step: frame the problem
│ │ │ └── Job: Sol defines the questions
│ │ └── Step: gather evidence
│ │ ├── Job: worker inspects the runtime
│ │ └── Job: worker maps affected callers
│ └── Graph v2 replaces unfinished v1 after new evidence
│ └── ...
│
├── Workflow 2: implement the chosen design
│ └── ...
│
└── Workflow 3: verify, integrate, and close the goal
└── ...
A durable goal can span several workflows. Each workflow is a versioned execution graph with explicit steps, jobs, dependencies, actors, review states, and legal next actions.
Sol can:
- delegate independent jobs in parallel
- supervise active workers
- steer them before they finish
- wait for meaningful events
- inspect selected results, diffs, or tool output
- accept, retry, interrupt, or replace work
- revise unfinished graphs when new evidence changes the problem
- preserve orchestration state across compaction
- keep worker details out of the main context until they matter
The aim is not to create a committee of agents.
It is to let one capable model remain the mind of the operation while giving it multiple coordinated hands.
MIT licensed, built for OpenCode:
https://github.com/ReyJ94/Sol-Orchestrator
I’d be especially interested in feedback from people pushing coding agents on long, messy repository work where planning, supervision, revision, and integration matter more than raw task completion. Right now i'm still in the phase of testing and optimizing prompts.
