Agentic workflows that have AI models iterate, revise their work, and leverage tools deliver significantly better results than typical non-agentic prompting approaches.

Even using an earlier model like GPT-3.5 with an agentic workflow can outperform a more advanced model like GPT-4 without an agent architecture. There are four key emerging design patterns for AI agents that can provide productivity boosts:

Reflection: Having the model check its own work, spot problems, and iteratively revise it

Tool Use: Leveraging external tools for analysis, information gathering, taking actions

Planning: Enabling the agent to autonomously plan a sequence of steps to accomplish a goal

Multi-Agent Collaboration: Having multiple agents with different roles collaborate together

Planning algorithms can enable impressive autonomous behavior where agents can route around failures. Research agents are already proving useful for automating information gathering.

Multi-agent systems with different agents playing roles like CEO, engineer, designer, etc. collaborating can generate surprisingly sophisticated outputs, like complex software programs. Multi-agent debate between different models also enhances performance.

Agentic reasoning will dramatically expand the tasks AI can tackle, but requires patience to let agents work for minutes or hours rather than expecting instant responses. Fast token generation to rapidly iterate agents is important.

We will explore how to go about Building AI Agents and Applications with the same.