1.6 KiB
1.6 KiB
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- Agentic AI: Systems that can plan, reason, and take action to accomplish tasks with minimal human intervention.
- AI agents extend the capabilities of LLMs with respect to the following dimensions:
- Integrating memory, to retain and recall information across interactions
- Tool use, to utilize external tools, APIs, and databases
- Decision-making, to plan and execute multi-step workflows
- AI agents will continue to refine the ability to reason, plan, and act.
- AI agents extend the capabilities of LLMs with respect to the following dimensions:
- The development of agentic-based AI is a natural progression from statistical modeling to deep learning and now to reasoning-based systems
- This evolution marks a shift from predictive models to autonomous systems capable of dynamic decision-making.
- The main challenges with raw LLMs are the following:
- Context window limitations: The context window typically range from 2,000 to 128,000 tokens and this create the following challenges:
- Document processing: long documents must be chunked to deal with context limits
- Conversation history: A proper memory management is required to maintain infromation across extended conversations.
- Cost management: It is necessary to make efficient token use, because most providers charge based on token count.
- Limited tool orchestration: to execute complex workflows a proper infrastructure is needed to discover tools and manage tool interactions across multiple turns.
- Task coordination challenges: structured control mechanisms are require to manage with LLMs multi-step workflows
- Context window limitations: The context window typically range from 2,000 to 128,000 tokens and this create the following challenges:
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