Planning
The planner analyzes the task, creates an architecture plan and distributes subtasks to specialized agents.
Project example · Orchestrator Auto
Not all on one agent.All towards one goal.
Lurus Code writes complete features autonomously - from planning to implementation to testing. The orchestrator distributes tasks to specialized agents working in parallel.
01 Planning
/orchestrate 02 Specialized work
03 Quality checks
04 Shared context
View from Lurus Code
Several roles. One traceable work state.
See planning, specialized work and checks in context. Parallel work helps only when the shared result remains reviewable.
The Solution
A team of specialized AI agents.
Specialized work
Explorer, shell and coder work in parallel - not sequentially. This accelerates development by multiple factors.
Quality checks
QA gates check the code after each step - bugs are detected and fixed immediately, not at the end.
Shared context
Session memory preserves context throughout the entire development - the orchestrator forgets nothing.
The scope of this example
From idea to finished feature - autonomously.
These figures describe the example scope. Timing and results depend on the task, repository and verification.
- 5x
- Faster than manual development
- 4+
- Specialized agents per task
- 0
- Context switches needed
The Problem
Feature development is slow and fragmented.
Autonomous Coding with Orchestrator Auto
- Planning, implementation and testing run sequentially - each step waits for the previous one.
- Context switching between files, terminals and documentation costs time and focus.
- Individual AI assistants lack the big picture - they solve partial problems but cannot complete an entire feature.
- Quality assurance always comes at the end - bugs are discovered late and expensive to fix.
Distribute the work. Not the responsibility.
Choose a task with clear subtasks. Let planning, implementation and verification work towards one shared, reviewable goal.