Concurrent coding agents
Firebender offers simple primitives to do this:
If done well, your overall throughput of good changes increases dramatically:
Zone of productivity.
Common pitfalls
Parallelization overhead
Problem: Conflicts, higher error rates, and context-switching costs can eat into productivity gains. Solution:- Use Worktrees for isolated changes that auto-heal merge conflicts from base
- Use Subagents when you want the main agent to delegate focused work in parallel
- Prefer smaller, narrowly-scoped tasks: they have the highest merge rate and lowest context-switching cost
- Subagents: create focused callable agents for operational tasks like verification, PR review, or doc sync
Focused subagents scoped to specific tasks dramatically improve accuracy. Create your own with
/agent or see Subagents.Staying in flow
Problem: Managing AI agents can feel like being an engineering manager: frequent context switches, waiting on slow responses, and forgetting your original intent (“doorway effect”). Solution:- Write, Ask, and Plan modes: switch between implementation, read-only exploration, and planning as the task evolves
- Plan mode: AI researches approaches and asks clarifying questions before coding
- GLM 4.7 and GPT-5.2: fast agentic models for quick iteration
- Commands: quickly insert pre-written prompts or task descriptions you use often
Use
/help to quickly create a command based on a previous chat. See Commands.