The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis A Blueprint for Enterprise Agent Adoption
160 snips
Jan 31, 2025 
Swami Chandrasekaran, a partner at KPMG and an expert in AI and data, discusses the future of AI agents in enterprises. He breaks down the TACO framework for agent types, offering insights into which strategies work best for organizations. The conversation reveals the rapid evolution from basic AI interactions to autonomous agents and stresses the importance of human collaboration. Chandrasekaran also emphasizes conducting an agent readiness audit to ensure effective adoption, addressing both challenges and opportunities for businesses.
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Agent vs. LLM
- AI agents differ from LLMs by their ability to plan and execute actions based on instructions.
- This involves reasoning, task breakdown, tool selection, and execution, going beyond simple prompt interactions.
True Agency
- Current agent frameworks like Langchain allow deterministic chaining of tasks, but true agency involves independent reasoning.
- LLMs are improving in reasoning and understanding longer instructions, enabling more complex agent behavior.
TACO Framework
- The TACO framework categorizes agents into Taskers, Automators, Collaborators, and Orchestrators based on their complexity.
- Taskers handle single goals, Automators manage cross-system processes, Collaborators focus on human interaction, and Orchestrators involve multi-agent systems.