University of Kwazulu-Natal

Ai Agents Examples Types And Use Cases Aisera Com

Bonisiwe Shabane
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ai agents examples types and use cases aisera com

AI agents are a big leap forward in agentic AI in many areas. They can do tasks on their own without a human to help. They can understand data, make good decisions and take action. That’s how we work and how businesses operate. These intelligent agents are versatile and can be applied in many areas, including robotics, finance, healthcare, and customer service. Automating complex tasks and learning from experience make them efficient and effective across industries.

AI agents are autonomous software programs that perceive their environment through sensors and act on it using actuators, running independently without human intervention by using advanced technologies like machine learning and large language models... They are great at solving complex problems by breaking down queries, planning task sequences, and using a reasoning process to handle ambiguity, often using tools like web searches and APIs to find a solution. From the AI assistants on our phones to the complex systems managing financial trades, these agents are already a game changer and in this article, we’ll show you 24 real-world examples to see how... Enterprise-grade AI platform delivering autonomous AI agents and generative AI solutions for customer, employee, and operational experiences. Aisera is a comprehensive AI platform that empowers enterprises with autonomous AI agents and generative AI capabilities to automate workflows, enhance customer and employee experiences, and improve operational efficiency. Leveraging domain-specific large language models (LLMs) customizable with proprietary data, Aisera integrates deeply with enterprise SaaS applications and communication channels to provide real-time, contextual AI-driven assistance and automation.

The platform supports multi-channel conversational AI, neural search, and workflow automation, enabling rapid deployment and significant cost savings across industries. Domain-specific AI agents that automate tasks, deliver answers, and transform workflows with minimal human intervention. Advanced conversational AI providing ChatGPT-like interactions across multiple domains, languages, and channels. Ability to buy, build, or bring your own large language models, customized with enterprise data for domain-specific accuracy. Build AI agents for the enterprise in minutes and scale your workforce Revolutionize the workplace with Aisera’s AI Agents by automating routine tasks, allowing employees to focus on creative, complex, and innovative work.

Enterprise AI agents can be triggered via user requests or automatically triggered based on specific events, ensuring seamless, efficient operations across the organization. By leveraging agentic AI and GenAI-powered agents, companies can achieve substantial ROI, enhance productivity, and unlock significant business value, positioning themselves for long-term success. Build conversational automation in natural language, which completes complex tasks by identifying what APIs to call and then dynamically orchestrating their execution in sequence on the fly. This reduces development times and bridges workflow gaps, enhancing self-service capabilities. Aisera’s hyperflow catalog provides out-of-the-box automation defined in natural language and can be deployed with a few clicks, enabling you to realize benefits even faster. AI Agents Explained: definition, 5 types, top use cases & real examples to see how they boost efficiency and drive growth.

AI is no longer a distant concept—it’s a team member. From chatbots that resolve support tickets at midnight to scheduling assistants that juggle calendars in seconds, ai agents are transforming how work gets done. In fact, 79% of businesses now use these autonomous programs to tackle routine tasks, and 66% report measurable productivity gains. So, what exactly are ai agents? At their core, they perceive data from the world, reason over it, store context in memory, and plan actions to hit specific goals. Some follow simple if-then rules like a thermostat, while others learn from feedback to optimize complex decisions in real time.

Behind the scenes, they call external tools, collaborate in multi-agent workflows, and continuously adapt to new information. In this article, we’ll demystify ai agents by defining their essential components and walking through the five main types—simple reflex, model-based reflex, goal-based, utility-based, and learning agents. You’ll discover which agent fits each business need and how to harness them in marketing, finance, healthcare, customer service, and beyond. Finally, we’ll bring these concepts to life with real-world examples and show you the top frameworks and libraries—like LangChain, AutoGPT, and watsonx—that can help you build your own ai agents today. Ready to see how autonomous AI can boost efficiency and drive growth? Let’s dive in.

In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex... Agentic AI tools prioritize decision-making over content creation and do not require human prompts or continuous oversight.[1] AI agents possess several key attributes, including complex goal structures, natural language interfaces, the capacity to act independently of user supervision, and the integration of software tools or planning systems.[2] Their control flow is... Researchers and commentators have noted that AI agents do not have a standard definition.[3][5][6][7] The concept of agentic AI has been compared to the fictional character J.A.R.V.I.S..[8] A common application of AI agents is the automation of tasks—for example, booking travel plans based on a user's prompted request.[9][10] Prominent examples include Devin AI, AutoGPT, and SIMA.[11] Further examples of agents released... Companies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents.[21]

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