When I first started playing around with AI, I thought I had it all figured out. I had a client, a friend of a friend actually, who ran a small business and needed a way to automatically draft social media posts. The project seemed simple enough. I’d build an AI agent, give it some basic instructions, feed it some information about the company, and boom, instant social media content. My consulting career was about to take off like a rocket. I was so confident, I told him I’d have the first version ready in a day or two. Then, I sat down, started typing, and within an hour, my grand plan went up in smoke. It wasn’t a small problem, it was a complete, utter, and total failure. The AI agent I built didn’t just fail to create good posts, it created gibberish, and it even tried to make posts for a competing company. I was embarrassed and a little ashamed. I felt like I had promised the world and delivered nothing but hot air. But that’s when a funny thing happened. Instead of giving up, I realized something important. My failure was actually the beginning of the real work, the most important part of the entire process. It was my first, invaluable lesson in the most critical aspect of creating anything with AI: learning to fail well. I learned that building an AI agent isn’t about getting it right the first time, it’s about embracing the fact that you won’t. I have heard it many times from someone I worked with recently. It’s a lot like the timeless cartoon rivalry between Wile E. Coyote and the Road Runner.
Think about it, Wile E. Coyote isn’t just a cartoon character, he’s a master of planning, a true visionary. His plans are always meticulously drawn out. He’ll sketch out blueprints, diagram complex pulley systems, and even calculate the trajectory of a falling anvil. He’s always got a goal in mind, one singular, all-consuming objective: catch that blasted Road Runner. Every episode is a fresh attempt, a new blueprint, and a novel contraption. The coyote’s plans, however brilliant on paper, always fail in spectacular and hilarious fashion. The rocket-powered roller skates backfire, the giant slingshot snaps, and the meticulously painted fake tunnel ends up on a real wall, with a train barreling through it. And what does he do? He dusts himself off, checks his ACME catalog, and immediately starts drawing up a new plan. He never gives up, he just adapts. That, my friends, is exactly how I’ve learned to approach building AI agents. My first failure with the social media client wasn’t the end, it was my first glorious anvil-to-the-head moment, a critical piece of feedback. It taught me that the process isn’t about succeeding, it’s about trying, failing, and learning.
Every new project starts with the blueprint, what I call the planning stage. This is where I sit down with a client or friend and we define the goal, the rules, and the tools. My goal with the social media project was clear: an AI agent that generates a week’s worth of posts based on a few key facts about the business. My tools were the prompts, the instructions I’d give the AI. I thought I knew what I was doing. I wrote a prompt that was short and to the point, something like, “Create 7 social media posts for [Company Name] about their [Product].” It seemed straightforward enough. I thought the AI would just understand the nuance, the tone, and the type of content the company needed. In my mind, I was Wile E. Coyote sketching out the perfect plan for an ACME spring-loaded hammer to catch my prey. I had the vision, the concept, and the confidence to make it happen. I was so ready to get started.
Once the blueprint is drawn, it’s time for the execution, the part where you write the prompts that bring your plan to life. This is the fun part, where I’m translating my ideas into the language the AI understands. With the social media agent, I started writing prompts that told the agent what to do. I’d write one prompt for a promotional post, another for a customer testimonial, and a third for a fun, engaging question. I was loading up my ACME box with all the fancy gadgets and gizmos I thought I needed. I thought my prompts were genius, each one a perfectly crafted tool for a specific job. I was so proud of them, I could almost hear the little “beep beep” of the Road Runner getting ready to be caught in my digital trap. I was convinced that my genius prompts would lead to a perfect, flawless result.
And then came the big test, the moment of truth. I pushed the button, so to speak, and set my AI agent loose on the world. I waited with bated breath for the results, expecting a perfect set of social media posts. Instead, what I got was pure chaos. The agent started creating posts that were wildly off-brand, one post was for a totally different company, another was a bizarre poem about a coffee mug, and a third was just a string of random words. I looked at the results and laughed out loud, because it was so utterly ridiculous. My brilliant plan, my meticulously crafted prompts, had all gone horribly wrong. My spring-loaded hammer had just launched me backward into a wall painted to look like a canyon. That’s when I realized my mistake wasn’t in the execution, but in the planning. My prompts, which I thought were so clear, were actually too vague. They left too much up to the AI’s imagination, and the AI filled in the blanks with nonsense. It didn’t have enough context, enough rules, or enough specific instructions to know what I really wanted. It wasn’t the AI’s fault, it was my fault. It had done exactly what I asked it to do, but what I asked was just plain wrong.
That failure, that moment of looking at my terrible AI-generated content, was the most important part of the entire process. It was my chance to learn and try again. Instead of giving up, I went back to the drawing board. I started asking myself, “Why did it fail? What did the AI misunderstand? Where were my instructions unclear?” I realized I needed to be more like a teacher and less like a boss. I had to guide the AI, not just give it a simple command and expect perfection. I started adding more context to my prompts. I included a detailed description of the company’s tone, their target audience, and specific examples of posts they liked. I added rules, like “Never use slang” or “Always end with a question to encourage comments.” I was learning from my failures, just like Wile E. Coyote learns from his. He never gives up on the idea of catching the Road Runner, he just keeps refining his methods, adjusting his blueprints, and trying a new, different approach. He’s a master of iteration, and that’s exactly what I was becoming. I was improving the blueprint and the tools based on the feedback of my previous failures.
When I finally ran the agent with the new, more detailed prompts, the results were night and day. The posts were on-brand, relevant, and engaging. They were exactly what the client needed. My biggest success came from my biggest failure. The initial embarrassment of failing was replaced by the satisfaction of having solved the problem. In the end, I’ve come to believe that building AI agents isn’t about getting it perfect on the first try. It’s about the journey of planning, executing, and most importantly, testing and failing. Just like Wile E. Coyote, we must embrace our failures, analyze what went wrong, and use that knowledge to build something better. My failures as an AI consultant, and believe me, there have been many, have taught me more than any success ever could. They’re the moments that force me to ask better questions and build more effective solutions. They’re the glorious, comical moments when the anvil falls on my head and I’m forced to rethink my entire approach. So, the next time you’re building something with AI, or trying anything new for that matter, don’t be afraid to fail. Plan big, execute with confidence, and then, if it all goes wrong, just like Wile E. Coyote, dust yourself off, learn from the spectacular explosion, and start drawing up your next brilliant plan. The biggest successes are often built on a foundation of glorious failures.
Now I’d love to hear about your own “Wile E. Coyote” moments. What have you learned from a spectacular failure? Share your thoughts and tag @iamcezarmoreno on social media. And for more insights on the creative side of technology, don’t forget to follow, subscribe, or join the newsletter at https://cezarmoreno.com.
