When I split AI tasks across specialized agents instead of dumping everything on one model, latency drops and quality improves. It’s orchestration over conversation.
The story
When I throw one giant task at one AI, it turns into a spaghetti maze. When I split it into chunks, each agent getting a piece, the results are clear, no fuzzy haze.
Problem: Most AI use is still single-threaded. One model tries to research, plan, write, code, and test in a single conversational lane. Latency piles up, context gets muddy, and you get that familiar vibe: “This is impressive, and also… slightly cursed.”
Solution: Agentic AI plus orchestration. I define a high-level goal, then decompose it into subtasks, and delegate each piece to a specialized agent. Research agent gathers inputs, coding agent generates implementation, testing agent validates. You can run these in parallel or sequence, which cuts bottlenecks and improves quality.
Frameworks and other tools like LangChain, LangGraph, LangFlow, AutoGen, CrewAI, and Roo Code help with the orchestration. I’m careful about MCPs for now, as I’ve found they can be context hogs.
My favorite pattern is hierarchical orchestration: a central controller assigns broad work, then agent clusters collaborate autonomously. Add handoff patterns when the “who should do this?” question changes midstream.
Skills I keep practicing: – Define subtasks with explicit goals to prevent overlap – Design modular agents, each with a specialty – Monitor and iterate with dashboards, track KPIs like efficiency gains
What would happen if your next project stopped being “one AI, one chat,” and became a small, well-run team?
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