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 decisions, much like human thinking. While current language models can execute workflows effectively, they are not truly independent agents. The article also highlights examples like Devin and Anthropic’s AI, which aim for this autonomy but currently face challenges. Understanding this difference is crucial for developing more advanced AI systems in the future.
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In the world of artificial intelligence, the term “agent” has gotten somewhat muddled. A recently published article by Anthropic highlights this confusion and makes the case for a clearer definition of what constitutes a true agent in AI. Simply put, many so-called agents are just glorified API calls to a language model. They’re often little more than a few lines of code that produce text responses, but they lack true independence.
What we need are real agents—systems that can think, act, and make decisions on their own. The evolution of AI is shifting toward this ideal, as current models are beginning to explore more sophisticated reasoning capabilities. For example, systems that can use SQL queries to access databases through integrated workflows showcase the utility of AI in corporations. However, despite their advantages, these workflows still rely heavily on predetermined processes and do not possess genuine agent-like qualities.
Understanding the difference between what we term a “workflow” and a genuine agent is crucial. Workflows execute tasks in predictable ways, leveraging the capabilities of language models and tools to generate useful outputs. For instance, applications that use routers to direct processes can perform complex tasks efficiently, but they are not truly independent agents.
So what does a “real agent” mean? In essence, a real agent operates independently, using reasoning and reflection to influence its actions. The latest AI models aim to combine aspects of both System 1 and System 2 thinking—that is, they gather information, reason through problems, and engage in conversations to clarify their tasks. This approach holds promise for creating agents capable of thoughtful decision-making, which can ask for more data instead of generating random or irrelevant replies.
Despite the progress, we are still not at the stage where AI can consistently function as genuine agents. Current attempts like Devin, which aims to be an autonomous software engineer, still face challenges with complex tasks. While engaging and promising, these systems often struggle with unpredictability in their solutions.
In conclusion, understanding the distinction between workflows and true agents helps set realistic expectations for AI applications. For practical problems, simple solutions that align with the current state of technology remain the most effective path forward.
For more insights on AI agents and workflows, check out these resources:
– Anthropic’s blog on agents: https://www.anthropic.com/research/building-effective-agents
– Anthropic’s take on computer use: https://www.anthropic.com/news/3-5-models-and-computer-use
– Hamel Husain’s insights on Devin: https://www.answer.ai/posts/2025-01-08-devin.html
Tags: AI, Artificial Intelligence, Agents, Workflows, Language Models, Anthropic, Devin, Technology
What are agents in a workflow?
Agents in a workflow are people or tools that help complete specific tasks. They can automate processes or assist human workers. Think of them as helpers that keep everything running smoothly.
How do I create a workflow?
Creating a workflow is easy. You start by identifying the tasks you need to do. Then, you outline the steps in the order they should happen. Finally, you can use tools or software to set up and manage your workflow.
What tools can I use for workflows?
There are many tools available for managing workflows. Some popular ones are Trello, Asana, and Monday.com. These tools help you organize tasks, assign agents, and track progress easily.
How do workflows improve productivity?
Workflows improve productivity by providing clear steps for tasks. They reduce confusion and ensure everyone knows their role. With a defined workflow, teams can work faster and make fewer mistakes.
Can I customize my workflows?
Yes, you can customize your workflows. Most workflow tools allow you to change steps, add new tasks, or modify agents. This flexibility helps you adjust to your team’s needs and improves overall efficiency.
