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<\/a><\/figure>\n\n\n\nWhat Is a Chatbot?<\/h2>\n\n\n\n
A chatbot is a conversational interface that replies to user prompts.<\/strong> Early chatbots followed decision trees \u2014 basically, a graph of paths that the bot traverses by evaluating user intent. <\/p>\n\n\n\n Modern bots, on the other hand, use natural language processing (NLP) to parse intent and retrieve answers. Think interactive FAQ with manners and lots of patience.<\/p>\n\n\n\n Chatbots are good at answering commonly asked questions at scale. They are also well-suited to collect structured inputs and route requests to resources or humans that can address the user\u2019s needs, creating a buffer of sorts that ensures only more complex requests are seen by employees. <\/p>\n\n\n\n Additionally, chatbots work well at triggering simple actions in a single system, helping streamline customer service pipelines. They\u2019re also designed to maintain a consistent voice and flow, so you know exactly how they\u2019ll respond \u2014 and that users won\u2019t be met with unpleasant surprises.<\/p>\n\n\n\n Chatbots struggle with complexity, particularly when implemented in multi-step workflows<\/a> that span many applications. When a process requires reasoning or third-party tools, the chatbot will often fail to provide consistent results. <\/p>\n\n\n\n Chatbots also fall short when it comes to handling ambiguous requests that lack context. If a user\u2019s query doesn\u2019t fit neatly into a predefined path, the chatbot might guide the user in the wrong direction, leading to frustration.<\/p>\n\n\n\n An AI agent is a goal-oriented software that can understand context, reason, and take actions autonomously based on its training. <\/strong><\/p>\n\n\n\n Typically, AI agents don\u2019t stop at replies. They also plan, interact with tools, execute steps, and check results, sometimes even collaborating with other agents to achieve their objective.<\/p>\n\n\n\n The core traits of AI agents include:<\/p>\n\n\n\n When an organization deploys a customer service solution, it\u2019s not just buying a bot. It\u2019s commissioning an outcome. <\/p>\n\n\n\n When it comes to choosing the right tool for the intended outcome, it\u2019s important to understand the differences between chatbots and AI agents, as these distinctions directly impact cost, risk, and ROI.<\/p>\n\n\n\n Perhaps the biggest difference is that chatbots are rules-based or narrowly trained, while AI agents use broader AI intelligence. As mentioned before, a chatbot often follows a script or decision tree; if the user says X, the bot will respond with Y. <\/p>\n\n\n\n An AI agent, on the other hand, uses generative AI, large language models<\/a> (LLMs), and advanced NLP<\/a> to actually think about the request and respond as a human would.<\/p>\n\n\n\n Building a chatbot vs. building an AI agent requires different approaches and vastly different scales of resources. Traditional chatbots often need a lot of manual setup \u2014 think dialog flows, scripts, and examples. In contrast, AI agents depend on one or more LLMs, which take tremendous resources to build and run. <\/p>\n\n\n\n Additionally, getting an AI agent up and running is more like onboarding<\/a> a new hire: You need to provide resources along with the business and user context it needs to do its job. A chatbot, on the other hand, often requires extensive upfront work to define rigid paths and scripts before it can function properly.<\/p>\n\n\n\n When it comes to chatbots, you know what you\u2019ll get; it\u2019s a predefined outcome machine that will respond in one of several defined ways. This gives you tight control over what the bot will say or do \u2014 nothing more, nothing less. <\/p>\n\n\n\n An AI agent offers you more flexibility, combining creativity and autonomy. Agents can adapt to requests and sometimes even generate novel solutions to problems not yet conceived. That flexibility, however, comes with the cost of unpredictability, the potential that it\u2019ll stray from intended use, and risks to governance and security.<\/p>\n\n\n\n To give you a better idea about how these two technologies are different, let’s look at some real-world use cases for each of them.<\/p>\n\n\n\n Chatbots are excellent for straightforward, high-volume tasks. If you\u2019ve ever encountered a customer service FAQ bot on a website, you know what we\u2019re talking about. Chatbots can easily and efficiently answer questions like \u201cWhat are your business hours?\u201d or \u201cHow do I reset my password?\u201d without any human intervention. <\/p>\n\n\n\n Another common use is lead generation on marketing sites. Chatbots are great at engaging users while they\u2019re on the site and collecting information that can lead to sales. <\/p>\n\n\n\n On top of this, chatbots also excel at internal helpdesk tasks, providing useful guidance and quick shortcuts to repeatable workflows.<\/p>\n\n\n\n AI agents excel in complex processes where answering a question alone isn\u2019t enough and completing a task is required. AI agents are becoming more common in sales, where they can qualify leads and execute tasks that can increase the potential for sales. <\/p>\n\n\n\n In the IT and DevOps<\/a> domain, AI agents can monitor and respond to scenarios that need immediate attention, like alerts or failures. Furthermore, AI agents can be exceptionally useful in multiple areas of business, including HR and security, by screening candidates or even detecting threats before they can take action.\u00a0<\/p>\n\n\n\n AI agents can also be indispensable in your finance department, validating invoices, flagging anomalies, and reconciling records \u2014 freeing your team to focus on high-level strategy.<\/p>\n\n\n\n Now that you have a good idea of what the most common chatbot and AI agent use cases are and the key differences between the tools, how do you decide what is best for your needs? <\/p>\n\n\n\n Well, it all comes down to what those needs are.<\/p>\n\n\n\n Follow these simple steps to figure out whether your organization should invest in a chatbot or whether an AI agent is the better option. <\/p>\n\n\n\n Write one sentence: \u201cSuccess is ____.\u201d Make sure the goal you choose is measurable.<\/p>\n\n\n\n List the systems, data, and approvals involved. Count the steps.<\/p>\n\n\n\n If it’s a single-step, single-system, repeatable workflow, a chatbot is your best choice. However, if it’s a multi-step, multi-system, judgment-based workflow, an AI agent is the way to go.<\/p>\n\n\n\n Users may prefer Slack or Teams. See how the chatbot or agent behaves inside the tools they\u2019re going to operate in.<\/p>\n\n\n\n Define your scopes, roles, rate limits, human-in-the-loop points, and audit needs.<\/p>\n\n\n\n Automate one outcome. Measure it and add branches and handoffs over time.<\/p>\n\n\n\n To increase the chances your chatbot or AI agent investments return serious dividends, follow these best practices:<\/p>\n\n\n\n Chatbots and AI agents each play important roles in the automation landscape. A chatbot might be your go-to for quick, structured interactions, while an AI agent is more like a skilled assistant that understands user needs on a deeper level and can take action.<\/p>\n\n\n\n If you\u2019re looking to implement either chatbots or AI agents, Workato can help. <\/p>\n\n\n\n Workato\u2019s enterprise automation platform provides the building blocks for both chatbot integrations and advanced AI agent capabilities. <\/p>\n\n\n\n Workato Workbot<\/strong><\/a>, for example, can act as a chatbot interface in Slack or Teams, allowing users to interact with your workflows in a conversational way. Meanwhile, Workato Genie<\/strong> <\/a>embraces agentic AI, enabling you to create AI agents that can reason over your enterprise data and execute powerful actions across your apps. <\/p>\n\n\n\n What\u2019s more, Workato now offers observability tools, policy enforcement, and other governance features that are helping to mitigate the risk of AI chatbots going astray or providing inaccurate information.<\/p>\n\n\n\n To learn more about both of these tools, check them out<\/a>.<\/p>\n\n\n\nWhat chatbots do well<\/h3>\n\n\n\n
Where chatbots struggle<\/h3>\n\n\n\n
What Is an AI Agent?<\/h2>\n\n\n\n
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AI Agents vs. Chatbots: Why the Difference Matters<\/h2>\n\n\n\n
Intelligence and reasoning<\/h3>\n\n\n\n
<\/a><\/figure>\n\n\n\nDevelopment and maintenance<\/h3>\n\n\n\n
Reliability and control<\/h3>\n\n\n\n
Chatbots vs. AI Agents: Use Cases<\/h2>\n\n\n\n
Chatbot use cases<\/h3>\n\n\n\n
AI agent use cases<\/h3>\n\n\n\n
How to Choose Between Using an AI Agent and a Chatbot<\/h2>\n\n\n\n
Step 1: Define the Outcome<\/h3>\n\n\n\n
Step 2: Map the Path<\/h3>\n\n\n\n
Step 3: Classify the Work<\/h3>\n\n\n\n
Step 4: Decide the Interface<\/h3>\n\n\n\n
Step 5: Set Guardrails<\/h3>\n\n\n\n
Step 6: Start Small, Expand Fast<\/h3>\n\n\n\n
Best Practices for Chatbots and AI Agents<\/h2>\n\n\n\n
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<\/a><\/figure>\n\n\n\nChatbots vs. AI Agents: Which Is Right for You?<\/h2>\n\n\n\n
Next Steps<\/h2>\n\n\n\n