Summary of AI Agents: Rethinking the Concept of Agency
Over the past few months, I have explored the intricacies of creating AI agents that not only possess beliefs but also adapt and learn from evidence. This journey raises pivotal questions about what truly constitutes an AI agent:
-
Definition of Agency:
- Beliefs: Quantifiable representations of truths, not mere hunches.
- Goals: Objective functions aimed at maximizing outcomes.
- Decisions: Informed choices based on beliefs and goals rather than random prompts.
-
Experiment Insights:
- I compared a traditional LangChain ReAct agent to a Bayesian agent called Credence.
- The LangChain performed better in accuracy (63.7%) but scored lower overall due to inefficient query management.
- The Bayesian agent, while slightly less accurate, outperformed in point accumulation due to effective cost-benefit analysis.
As we analyze the concept of “agentic AI,” it’s crucial to demand systems that uphold true agency. The industry must evolve past simplistic definitions of agents.
🔍 Explore these insights further and consider the implications for the future of AI! Share your thoughts below!
