Concepts: Agentic Agents Flashcards
(14 cards)
What are the core components of AI agents?
AI agents typically include:
* LLM for reasoning
* Perception modules for input
* Planning capabilities
* Memory (short and long-term)
* Ability to use tools
* Learning/adaptation mechanism
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What is the Perceive-Reason-Act-Learn Cycle?
The fundamental operational loop of an AI agent:
* Perceive environment
* Reason about it
* Act based on reasoning
* Learn from outcomes
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What role does LangChain play in agent building?
LangChain provides modular tools and abstractions for building LLM applications, including agents, by simplifying the integration of models, prompts, data, and tools.
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What are the advantages of LangGraph for complex workflows?
LangGraph extends LangChain to enable:
* Creation of stateful, multi-actor agentic systems
* Cyclical workflows
* Human-in-the-loop capabilities
* Persistent state
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What is the significance of the Model Context Protocol (MCP)?
MCP aims to standardize how AI models interact with external resources, improving interoperability and simplifying tool integration for agents.
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What is the importance of the Agent-to-Agent (A2A) Protocol?
A2A enables secure and structured communication between different AI agents, fostering the development of interoperable multi-agent systems.
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What is the business value proposition of agentic AI for C-Level Executives?
Agentic AI can:
* Drive strategic advantage
* Reduce costs
* Improve operational efficiency
* Enable better, faster decision-making
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What is the business value proposition of agentic AI for Engineering & IT Leaders?
Agentic AI facilitates:
* Scalable solutions
* System integration
* Faster development
* Modernization of legacy systems
* Management of technical debt
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What is the business value proposition of agentic AI for Data Scientists & AI/ML Teams?
Agentic AI allows:
* Leveraging advanced AI capabilities
* Building complex workflows
* Unlocking new data insights
* Rapid experimentation
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What is the business value proposition of agentic AI for Business Division Leaders?
Agentic AI can:
* Solve specific business problems
* Optimize processes
* Enhance productivity
* Create competitive differentiation
* Empower employees
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What is the importance of agentic AI in regulated environments?
Agents can:
* Automate compliance monitoring
* Enhance auditability
* Proactively manage risks
* Improve data governance and security
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What are key challenges in developing and deploying AI agents?
Challenges include:
* Ensuring reliability and predictability
* Managing security risks
* Complex evaluation
* Integration hurdles
* Data quality and bias
* Ethical concerns
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What are best practices for building and managing AI agents?
Best practices include:
* Comprehensive development lifecycle
* Effective state management
* Secure tool integration
* Rigorous evaluation
* Prioritizing security and ethics
* Fostering human-agent collaboration
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What future trends are expected in agentic AI?
Expect:
* Continued market growth
* More powerful LLMs
* Improved agent architectures
* Standardization efforts
* Enhanced tooling
* Focus on reliability
* Verticalization of agents
* Deeper enterprise integration
* Rise in human-agent collaboration
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