NEW ElevenLabs Agent Workflows Update Is INSANE! 🤯

This new 11 Labs agent workflows update is insane. Today, I'm going to show you how 11 Labs just dropped something absolutely game-changing. This is their brand new agent workflows update, and it's going to completely transform how you build AI voice agents. I'm talking about a visual drag and drop editor that lets you create insanely complex multi-step agents without writing a single line of code. You can chain different AI personalities together, call your own tools and APAs, and even hand conversations off to real humans when things get tricky. This is the kind of stuff that was impossible just a few weeks ago, and now it's available to everyone. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below and let me know what you think. So, 11 Labs just launched agent workflows, and this is honestly one of the biggest updates I've seen in the AI voice space all year. Before this update, you could build voice agents, but they were pretty basic. You give them a prompt and some tools and hope for the best. Now, you can literally program conversation flows like a flowchart. You drag nodes around, connect them with arrows, and create these branching decision trees that handle real business logic. The crazy part is that this solves one of the biggest problems with AI agents. They were unpredictable. You never knew if they'd actually call the right tool at the right time or if they just ramble on. With agent workflows, you can force specific actions at specific points in the conversation. You can say, "Hey, when the customer asks about billing, switch to this specialized billing agent personality. When they want to schedule a call, hit this web hook that connects to my calendar. and if they're angry or confused, transfer them to a real human on my team. Let me break down exactly what's new here because there are seven major features you need to understand. First up is the visual workflow editor. This is drag and drop. You're literally building flowcharts. Instead of writing one big prompt and crossing your fingers, you map out the entire conversation path. You drop nodes onto a canvas, draw lines between them, and define what happens at each step. It looks like those workflow builders you see in Zapia or N, but it's specifically designed for conversation AI. The cool thing is you can zoom out and see your entire agent logic in one view. You can spot problems before they happen. You can see if there's a path where the agent gets stuck in a loop or doesn't have an exit strategy. Before I continue, if you want to scale your business and save hundreds of hours with AI automation, check out my AI profit boardroom at the link in the description. It's the best place to learn how to use AI to get more customers and grow faster. Second feature is sub agents. This is huge. A sub agent is basically a different personality or configuration that takes over at a specific node in your workflow. So imagine you're building a customer service agent. The greeting part might use a friendly casual voice and a simple system prompt. But when the conversation moves to the billing department, boom, you switch to a sub agent that has a different voice, a more professional tone, and access to different tools. You can even switch which language model it uses. Maybe you use a cheaper, faster model for basic questions and a more powerful model when handling complex technical support. This is how you build agents that actually feel professional and specialized instead of just one confused AI trying to do everything. Third is dispatch and tool nodes. These are dedicated nodes in your workflow that guarantee a tool gets called. Before agent workflows, tools were optional. The agent would decide whether to use them. Sometimes it would forget or hallucinate instead of calling your API. With a dispatch node, you're telling the system, hey, at this exact point in the conversation, call this web hook. No exceptions. And here's the best part. You can define success and failure paths. If the API returns a 200 status code, route the conversation this way. If it fails or times out, route it that way. So you can build real error handling into your voice agents. You can say if the payment API fails, apologize to the customer and offer to transfer them to a human. That's production level reliability. Fourth feature is LLM and expressionbased edges. This is how you add conditional logic to your workflows. An edge is just a line connecting two nodes. But now you can make those edges smart. You can add an LLM evaluation that says, "Hey, if the user sounds angry, take this path. If they sound happy, take that path. Or you can use deterministic expressions. These are like simple if then rules. If the user said the word cancel, route to the retention team. If they mentioned a specific product name, route to the specialist for that product. This is way more powerful than just hoping the AI figures it out. You're giving it explicit routing rules. Fifth is agent transfers and human handoffs. This is critical for any real business use case. You can transfer a conversation from one agent to another. Maybe you have a general purpose agent that figures out what department the customer needs, then transfers them to a specialist agent. Or

you can transfer to a real human. There's a transfer to number node that literally calls a phone number and bridges the conversation. The human picks up, they can hear the full conversation history and they take over. This is how you build hybrid systems where AI handles the easy stuff and humans step in for the complex edge cases. The sixth feature is testing and guardrails. 11 Labs built in a testing harness right into the workflow editor. You can run test conversations through your flow before you deploy it. You type in what the user would say, the agent responds. You keep going, and you watch which path it takes through your workflow. You can see exactly which nodes fire, which tools get called, and where it ends up. This is how you catch bugs before your customers do. You can also set up guard rails. These are safety checks that prevent the agent from doing dumb stuff like you can block certain paths if the conversation is looping or if the agent is about to make a call it shouldn't make. And seventh is enterprise controls and MCP behavior. MCP stands for model context protocol. This is more advanced stuff but basically 11 Labs added controls for enterprises to manage how their agents behave at scale. You can set workspace level overrides. You can control interrupt behavior. You can manage latency and cost by choosing which models run in which scenarios. This is the kind of stuff that matters when you're running hundreds of concurrent agent conversations. And you need to keep your cost predictable. Now, let me tell you why this matters. Before agent workflows, building a reliable voice agent was hard. You'd spend hours prompt engineering, testing, and fixing edge cases. And even then, your agent would do weird stuff in production. It would forget to call tools. It would get confused and ramble. it would transfer people to the wrong department. Basically, it was a mess. With agent workflows, you're building deterministic conversation paths. Deterministic means predictable. You know exactly what's going to happen at each step. If a customer asks about pricing, they go to node B. Node B calls the pricing API. If the API returns data, route to node C and read it out. If the API fails, route to node D and apologize. There's no guessing. There's no hoping the AI does the right thing. You've mapped it all out. This is also way faster to iterate on. If you realize your agent is messing up in one specific scenario, you don't have to rewrite your whole prompt. You just edit that one node or that one edge. You test it. You deploy. Done. This is how you go from prototype to production in days instead of months. And here's something most people aren't talking about yet. You can use agent workflows to build multimodal experiences. What I mean is your agent doesn't just live in one place. You can deploy the same workflow as a phone agent, a web chat widget, and a voice interface on your app. The logic is the same. The conversation flow is the same. You just change the input and output channels. That's insane scalability. Here's what I want you to do right now. First, go to the 11 Labs website and check out agent workflows. Sign up if you haven't already. Start playing with the editor. Build your first simple workflow. Second, think about one process in your business that you could automate with a voice agent. Write it down. Map out the conversation flow. Figure out what APIs you need to call. Third, join my AI money lab. Inside you get 50 plus free AI tools and 200 plus chat GPT SEO prompts. You'll learn how to make money with AI agents. Get access to more than 1,000 plus free N8N workflows, plus a full blueprint to generate thousands of leads free with AI. You also get a free AI community, free AI course, and proven AI case studies. Everything you need to start making money with AI today. Link is in the description. Fourth, if you're serious about scaling your business with AI, join my AI profit boardroom. It's the best place to learn how to get more customers and save hundreds of hours with AI automation. We go way deeper than these YouTube videos. You get access to advanced training templates and a community of people actually building with AI. Link is in the description for that