AI Agents: Key Concepts and How They Overcome LLM Limitations
As large language models (LLMs) become more powerful, a new breed of software known as “agents” has arisen to augment and enhance the capabilities of LLMs. This article introduces the key concepts of agents and how they complement LLMs.
Since the initial release of ChatGPT, which was based on GPT 3.5, large language models have evolved and matured. Some of the recent releases — like GPT-4o , Gemini Pro , and the Claude Opus models — have even demonstrated advanced reasoning abilities. The open language model landscape has also been rapidly evolving in recent times. Several variants of these LLMs have been released for use in private environments. In terms of reasoning and answering complex questions, some open language models — like Mistral and Llama 3 — are on par with commercial models. This has all been a driver of the AI agents trend.
What Is an AI Agent?
An agent is an autonomous software entity that leverages the language processing capabilities of LLMs to perform a wide range of tasks beyond simple text generation and comprehension. These agents extend the functionality of LLMs by incorporating mechanisms to interact with digital environments, make decisions and execute actions based on the language understanding derived from the LLM.
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Janakiram MSV? is an analyst, advisor, and architect. Follow him on?Twitter ,??Facebook ?and?LinkedIn .
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4 个月Insightful!
Building Generative AI , Single and Multiple Agents for SAP Enterprises | Mentor | Agentic AI expert | SAP BTP &AI| Advisor | Gen AI Lead/Architect | SAP Business AI |Joule | Authoring Gen AI Agents Book
5 个月How do these agents determine which additional capabilities are most beneficial for augmenting LLMs?
Applied AI/ML Expert | I help organizations from AI Strategy & Solutioning to Execution | Generative AI Consultant | 2X Founder, 2 Exits with $40MM+ M&A valuation
5 个月Quickly read through the article Janakiram MSV. Here are my takeaways : ?? AI Agents: Enhance and augment LLMs capabilities. ?? Memory: Agents can retain context from past interactions, unlike stateless LLMs. ? Asynchronous Processing: Agents handle multiple tasks simultaneously, improving efficiency. ?? Real-Time Data Access: Agents can access and verify information online to prevent hallucinations. ?? Math Skills: Agents integrate with specialized tools to perform complex calculations accurately. ??? Consistent Outputs: Agents ensure standardized output formatting for reliable use in applications. ?? Persona-Driven Interactions: Agents personalize responses, maintaining coherent and engaging user experiences. ?? LLM Evolution: Despite advancements, LLMs like GPT-4o and Gemini 1.5 have limitations in memory, processing, and accuracy. ??? Augmentation: Agents address these limitations, making AI more efficient and reliable across various tasks.
Gen-AI | Technical Leadership
5 个月Nice article, mate!