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▎Common Agentic AI Terms

1. Agentic AI: AI systems designed to act autonomously, perceive their environment, make decisions, and take actions to achieve specific goals with minimal human intervention.

2. Autonomous Agent: An AI that can operate independently, make choices, and execute tasks without direct human command for each step.

3. Perception: The ability of an agent to interpret sensory input from its environment (e.g., text from a user, data from a system, visual information).

4. Action: The output or execution performed by an agent based on its perception and decision-making process (e.g., writing text, calling an API, performing a calculation).

5. Goal-Oriented: Agents designed with specific objectives or tasks they are programmed to achieve.

6. Planning: The process by which an agent determines a sequence of actions to achieve its goal, often involving breaking down complex tasks into smaller sub-tasks.

7. Reasoning: The cognitive process an agent uses to process information, draw inferences, and make logical deductions to inform its actions.

8. Memory: The agent's ability to store and recall information from past perceptions, actions, or conversations to inform future decisions.

9. Tools: External functions, APIs, or services that an agent can leverage to perform actions beyond its core capabilities (e.g., a calculator, a web search API, a database query tool).

10. Tool Use: The capability of an agent to identify, select, and invoke appropriate tools to gather information or execute tasks required to achieve its goals.

11. ReAct (Reasoning and Acting): A framework that combines thought processes (reasoning) with actions, allowing agents to iteratively plan, act, and observe the environment's response.

12. Self-Reflection / Self-Correction: The agent's ability to evaluate its own past actions and reasoning, identify errors or suboptimal steps, and adjust its strategy.

13. Multi-Agent Systems: Systems composed of multiple AI agents that can interact with each other to collaborate, compete, or achieve complex, distributed goals.

14. Task Decomposition: The process of breaking down a large, complex goal into a series of smaller, manageable sub-tasks that an agent can execute sequentially or in parallel.

15. State Management: Keeping track of the current situation, context, and progress of an agent throughout its execution of a task or interaction.

16. Goal Setting: The ability of an agent to define, refine, or adapt its own goals based on context or external feedback.

17. Environment Interaction: The agent's ability to perceive changes in its operating environment and react accordingly.

18. Human-in-the-Loop (HITL): A system design where human feedback or intervention is incorporated at specific points in the agent's decision-making or execution process.

19. Prompt Chaining: A technique where the output of one prompt or agent's action becomes the input for the next, creating a workflow of sequential tasks.

20. Agent Orchestration: The management and coordination of multiple agents or multiple steps within a single agent's workflow to ensure tasks are completed effectively and efficiently.


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𝟭. 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

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𝟮. 𝗟𝗘𝗘𝗧𝗖𝗢𝗗𝗘 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

https://t.me/leetcode_cp

𝟯. 𝗖𝗢𝗗𝗘𝗙𝗢𝗥𝗖𝗘𝗦 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

https://t.me/codeforces_cp

𝟰. 𝗖𝗢𝗗𝗘𝗖𝗛𝗘𝗙 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

https://t.me/codechef_group

𝟱. 𝗖𝗢𝗗𝗜𝗡𝗚 𝗡𝗜𝗡𝗝𝗔𝗦 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗿𝗼𝘂𝗽:

https://t.me/coding_ninjas_discuss

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