Discover ActionKit by Paragon, a game-changing tool that enhances your product’s AI agents with over 1,000 integration actions with just one API call. Perfect for streamlining workflows across popular platforms like CRMs, ticketing services, and Slack, ActionKit is trusted by leading AI companies such as AI21 and Copy.ai. With its ability to connect effortlessly, ActionKit ...
Dapr Agents is a powerful framework that helps developers create robust AI agent systems that can scale effortlessly. Built on the reliable Dapr project, it enables the development of AI agents capable of reasoning, acting, and collaborating, using advanced Large Language Models. Key features include the ability to run thousands of agents efficiently on minimal ...
As AI technology advances, developers are using large language model (LLM) agents to perform complex tasks more effectively. This article highlights the difference between workflows and agents, explaining how they function within AI systems. It presents various design patterns, such as Chain Workflow, Routing Workflow, and Orchestrator-Workers, showcasing their applications in a Spring AI project ...
In today’s businesses, workflows play a crucial yet often overlooked role, acting as the backbone of operations. They help manage processes effectively, and when enhanced with AI agents, they become even more powerful. AI agents can identify when to intervene in workflows, streamline tasks, and automate decision-making. This shift not only improves efficiency but also ...
Zapier’s new AI agents revolutionize automation workflows by allowing users to create intelligent assistants that adapt to various situations without writing code. Unlike traditional Zapier automations, which follow rigid paths and complex branching logic, AI agents interpret plain English instructions, analyze data, and respond appropriately. This means you can easily build automated workflows for tasks ...
In this blog post, the author, an LLMOps engineer, shares insights on creating effective AI-powered applications. They emphasize the importance of simplicity and composability in developing LLM agents, which are evolving quickly. The article explores key learnings about the distinction between workflows and agents. Workflows are structured processes that follow specific paths for consistency, while ...
In this insightful blog, an LLMOps engineer shares key lessons on creating effective AI agents, emphasizing the importance of simplicity and composability. The writer explores the rapid evolution of LLM agents, highlighting when to use agentic systems and best practices for their development. A crucial distinction is made between workflows—structured processes for predictable tasks—and agents, ...
The article discusses the potential of integrating agentic AI into enterprise architectures to revolutionize business processes. While generative AI mainly focuses on customer engagement, the future lies in agentic AI, which empowers systems to operate autonomously and improve workflows. By utilizing AI agents, organizations can replace outdated processes with dynamic automation that adapts to changing ...
In the world of AI, many use the term “agents” inaccurately, referring to basic calls to language models that just respond to prompts. True agents should think independently and employ intelligent reasoning, resembling human decision-making. Current systems, often termed workflows, are helpful but predictable and lack true autonomy. Real agents would reason and adapt to ...
This article discusses the distinction between real agents and simple workflows in the context of AI and language models. Many systems labeled as agents are often just basic API calls that respond to prompts without true independence or decision-making abilities. The author argues that a genuine agent should be able to reason, reflect, and make ...
