#REDIRECT [[AI Agents]]
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#REDIRECT [[AI Agents]]
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{{#ask:[[Category:Agents]] [[Available::Yes]]
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...es developers with an array of modular components and pre-built chains and agents.
==Pre-built Chains and Agents==
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|Description = KalendarAI sales agents generate revenue with potential customers from 200+ million companies globa
#Analyze the performance of my sales agents using KalendarAI insights.
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{{#ask:[[Category:Agents]] [[Available::Yes]]
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==Types of Agents==
...consists of two primary types of agents: reactive agents and deliberative agents.
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</noinclude>'''[[AI Agents]]'''
'''[[LangChain]]''' - [[Prompt templates]], [[Agents]], [[Memory]], [[Parametric knowledge]], [[Source knowledge]], [[Context wi
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</noinclude>{{#ask:[[Category:Agents]] [[Developer::ChatGPT]] [[Available::Yes]]
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</noinclude>{{#ask:[[Category:Agents]] [[has category::DALL·E]] [[Available::Yes]]
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</noinclude>{{#ask:[[Category:Agents]] [[has category::Productivity]] [[Available::Yes]]
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</noinclude>{{#ask:[[Category:Agents]] [[has category::Programming]] [[Available::Yes]]
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</noinclude>{{#ask:[[Category:Agents]] [[has category::Education]] [[Available::Yes]]
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</noinclude>{{#ask:[[Category:Agents]] [[has category::Lifestyle]] [[Available::Yes]]
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|Description = The AI council assesses queries through various agents, offering insights from many perspectives.
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...nments, like playing a game or navigating a maze. The equation helps these agents learn the best way to behave in order to reach their goals.
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...g, with researchers being able to plug any application into Universe so AI agents have a common way of interacting with the applications. <ref name="”2”
...a Virtual Network Computing (VNC) remote desktop. The more interaction the agents have with the environment, the better they become at a specific task. <ref
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...applying and building CrewAI for orchestrating role-playing, autonomous AI agents.
- It knows that it's using any LangChain tools for AI agents so it should set it up accordingly.
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...ers—making them hard to identify or remove. Research, including "[[Sleeper Agents]]: Training Deceptive LLMs that Persist Through Safety Training" by Anthrop
...resisting [[Safety Training]].<ref name="”2”">E. Hubinger et al., "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training," Anthropic,
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Machine learning involves agents taking actions based on observations of their environment. The goal is for
...by the success of reinforcement learning algorithms. These programs train agents to make decisions based on feedback in the form of rewards or punishments.
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