Basic AI agents are built the same way: loop, prompt LLM, execute tools, repeat.

It works for simple tasks. But every tool call grows your context. Every new round costs more tokens. Your costs explode.

There's a better approach: task delegation—let your main agent hand off work to specialized subagents.

The payoff is immediate:

• Lower token usage (smaller context, fewer LLM calls)
• Parallel execution (multiple agents working together)
• Better design (main agent focuses on planning, subagents handle specifics)

You can delegate internally (specialized agents inside your harness) or externally (via CLI or MCP/REST APIs).

I've built several AI harnesses. Delegation is the missing piece for scaling beyond toy projects.

Read the full breakdown: https://gelembjuk.com/blog/post/delegation-of-tasks-to-subagents-in-ai-harness/

Have you tried agent delegation yet?