A difficult task may be handled by a team of specialized AI agents rather than one general-purpose system. Multi-agent coordination organizes their roles, information exchange, and actions around shared or related goals. section 8.26
The approach divides labor across agents that can plan, reason, remember, use tools, or provide oversight. Orchestration frameworks configure these roles and connect their memory, tools, and communication, shifting development from isolated prompts toward dynamic systems. section 8.26
Coordination can offer practical advantages, but it also introduces additional ways to fail:
- Parallel or modular work can outperform a single-agent baseline on complex tasks requiring divided labor. section 8.26
- Failures may arise when agents exchange incorrect information, retain corrupted context, misuse tools, or pursue conflicting actions. section 8.17
- Greater autonomy makes coordinated workflows harder to predict, audit, secure, and control, especially when agents act across external systems. section 8.17
Coordinated software development
In software development, agentic tools can divide or chain tasks such as locating bugs, updating deprecated APIs, generating patches, writing tests, and debugging across files. IDE-based systems such as Cursor and Windsurf maintain context while agents plan and execute multistep work, leaving developers to supervise the collaboration. section 8.6
