The workflow is shifting: teams parallelize for speed and exploration. Agents decompose specs into subtasks, subagents execute in parallel with coordination, developers provide isolated environments, humans review and merge preferred options, and knowledge gets reused throughout.
The analogy to DevOps is striking. Automation existed before cloud — but cloud infrastructure enabled true parallelism that unlocked transformative power. Similarly, parallel agents are changing the development process itself, not just speeding up individual tasks.
What people are actually doing
- Running up to 15 different agents per project, each with specialized skills
- Managing multiple Claude Code sessions across git worktrees with diff viewing and testing
- Finding they enjoy agent-assisted development more than traditional coding because of increased speed and progress
Why parallelism matters
Token costs keep dropping. That makes it economically viable to explore multiple solution paths simultaneously rather than betting on one approach. You run three agents on three approaches and pick the best result. The cost of exploration has collapsed.
This connects to the broader pattern: AI doesn’t just make individual developers faster — it changes the economics of exploration itself.