Agentic Coding: Waste of Time or Future of Development?

8/10/2025

Is Agentic Coding a Colossal Waste of Time? Let's Bust Some Myths.

Honestly, the hype around agentic coding & tools like Claude Code is getting pretty intense. You see headlines about AI writing entire applications from a single prompt, & developers becoming "conductors of an orchestra" instead of violinists. It all sounds incredibly futuristic & a little bit terrifying, depending on who you ask.

But then you have the other side of the coin. The Reddit threads & blog posts from seasoned developers who tried to go "all in" on an agentic workflow & came out the other side feeling… underwhelmed. They talk about "programming with extra steps," AI generating code that looks convincing but is fundamentally flawed, & the constant fear of the AI silently breaking something critical.

So, what's the real story here? Is agentic coding the revolution we've been promised, or is it just another overhyped tech trend that's a massive waste of time for serious developers?

Here's the thing, I've been digging into this, & it turns out the truth is somewhere in the messy middle. It's not a simple "yes" or "no" answer. Let's get into it & debunk some of the biggest myths flying around.

Myth #1: AI Agents Are Basically Skynet & Will Go Rogue

Okay, let's start with the big, dramatic one. The fear that agentic AI is some uncontrollable, rogue technology just waiting to wreak havoc on our codebases & maybe, you know, the world. 40% of enterprise leaders are apparently worried about AI acting outside of protocol.

The reality is a lot less "I, Robot" & a lot more… structured. Agentic AI, especially in a coding context, isn't some free-thinking entity. It operates within carefully defined parameters & thrives on clear, unambiguous instructions. It's not about to "improvise" in a malicious way. Its "risks" are usually a direct reflection of how clear—or unclear—our instructions are. Think of it less as a wild animal & more as an incredibly powerful but literal-minded assistant. It will do exactly what you tell it to, which can be both a blessing & a curse if you're not precise.

These systems are designed to work with a developer, not in spite of them. You still own the code. You're still the one responsible for the final product. The AI is a tool, a very advanced one, but a tool nonetheless, just like your IDE or a linter. It’s not about abandoning responsibility to a bot.

Myth #2: Agentic AI Will Steal All Our Developer Jobs

This is probably the most common fear, & it's not a new one. Every major technological leap, from the invention of the spreadsheet to the rise of factory automation, has been met with cries that it will lead to mass unemployment.

But here's what the early data & expert opinions are suggesting: agentic AI is less of a replacement & more of a force multiplier. A recent Salesforce study found that a whopping 92% of developers believe agentic AI will actually help them advance in their careers. They see it as a way to offload the tedious, soul-crushing parts of the job—the boilerplate code, the endless debugging, the mind-numbing refactoring—& focus on the more strategic, creative, & high-impact work.

Think about it. A lot of programming work isn't about solving fascinating, complex problems. It's drudgery. It's fixing type errors, updating dependencies, or writing repetitive tests. If an AI can handle that stuff, it frees you up to think about architecture, user experience, & the bigger picture. One developer on Hacker News put it perfectly: by using AI for the drudgery, they have more time to think about the interesting problems.

This is also where the nature of business & customer interaction comes into play. While AI can automate tasks, businesses are also looking to use AI to enhance customer experiences. For instance, companies are increasingly using AI for customer support. This is where a platform like Arsturn comes in. Arsturn helps businesses build no-code AI chatbots trained on their own data. These bots can handle the frontline of customer service 2, providing instant answers & engaging with website visitors 24/7. This doesn't necessarily replace human agents but frees them up to handle more complex, nuanced customer issues that require a human touch, much like how agentic coding frees up developers. It’s about elevating the work, not just eliminating it.

Myth #3: You Can Just Give an AI a Vague Idea & It'll Build a Perfect App

This is a HUGE misconception, likely fueled by marketing demos. The idea that you can just say "Build me a competitor to Instagram" & the AI will spit out a flawless, production-ready application is pure fantasy.

Turns out, context is EVERYTHING.

Armin Ronacher, a respected developer, wrote about his experiences with agentic coding & highlighted that "context engineering & management remain major challenges." AI agents can pull in too much information or, more often, too little. They forget things from the beginning of a long session. This is why many experienced developers find that prompting an AI to generate large, complex features often fails. The AI just can't grasp the nuances of a mature, complex codebase.

A developer on Reddit shared their experience of trying to use an agentic AI on their project & found that even when they provided a style guide & references, the AI "still kinda just does stuff it wants to do." You end up in a back-and-forth prompt session to get it to match what you actually expected.

Effective agentic coding isn't about giving up control; it's about getting REALLY good at providing high-quality, specific context. It's about breaking down large problems into smaller, manageable tasks. It's more like a Socratic dialogue with your codebase than a simple command-and-control relationship. The successful use cases often involve developers who are already experts in their domain & know the codebase inside & out. They use the AI for targeted tasks, not as a replacement for their own expertise.

Myth #4: Agentic AI is Only for Big Tech Companies with Huge Budgets

This is a common myth with any new, powerful technology. The assumption is that it requires massive engineering teams & enterprise-grade infrastructure to leverage. But that's just not the case with agentic AI.

In fact, you could argue that small & medium-sized businesses (SMBs) stand to gain the most. They can use agentic AI to increase their operational efficiency without needing to hire a massive team. A small software startup can use it to accelerate their development cycles, or a small e-commerce site can use it to maintain their web presence.

This is true for AI in general, not just in coding. Consider lead generation & customer engagement. A small business might not have the resources for a 24/7 sales team, but they can still capture & qualify leads around the clock. This is another area where a tool like Arsturn becomes a powerful solution for businesses of any size. By building a custom AI chatbot trained on their website content & product information, they can engage visitors, answer questions, & capture leads automatically. It's a way to punch above their weight class, using smart automation to compete with larger players. It's not about having a huge budget; it's about being smart with the tools available.

So, Does It Actually Boost Productivity? The Jury is Still Out.

This is the million-dollar question, isn't it? And the data is… messy.

On one hand, you have some incredible success stories. Developers reporting they cleared "years of technical debt work" in just six weeks using Claude Code. One developer building a React front-end said they completed a task in 15-20 hours that would have traditionally taken 100-200 hours. That's a potential 10x improvement! Companies like Datadog are reportedly seeing 3-4x productivity improvements for experienced developers.

But then you have the counter-arguments. One study mentioned in a Medium article found that AI tools actually reduced productivity by 19% for experienced developers. Another found that AI increased development time & error rates. A large-scale Stanford study of nearly 100,000 developers found that while there was an average productivity boost of around 20%, some teams actually saw their productivity decrease.

What gives? The Stanford study offers a clue: "AI does increase developer productivity... but it doesn't increase the productivity of developers all the time and equally." The success depends on things like task complexity, codebase maturity, & the developer's own skill in using the tool.

The "convincing but wrong" nature of AI-generated code seems to be a major factor. An AI can produce code that looks plausible at a glance, but contains subtle, hard-to-spot bugs. This leads to what one developer called "code review fatigue." You spend so much time reviewing the AI's work that you might have been faster just writing it yourself. It feels like debugging someone else's code for a feature you weren't a part of.

The Reality: It's a Powerful Tool That Requires a New Skillset

So, agentic coding isn't a waste of time. But it's also not a magic bullet. It's a powerful new tool that, like any tool, requires skill & practice to use effectively. You wouldn't hand a novice a high-powered chainsaw & expect them to carve a masterpiece.

Here’s what seems to be the emerging consensus among developers who are using these tools successfully:

Start Small & Specific: Don't try to automate your entire workflow from day one. Pick small, repetitive, boring tasks. Things like writing unit tests, refactoring a function, or translating code from one language to another.
You Are Still the Expert: The AI is an assistant, not the architect. You need to guide it, review its work meticulously, & own the final output. The most successful users are those who have deep domain knowledge.
Master the Art of the Prompt: This is a new skill. Learning how to provide clear, concise, & context-rich prompts is the key to getting high-quality results. This involves not just text, but providing the right code snippets, file structures, & background information.
Embrace the Hybrid Approach: The most effective workflow isn't "all AI" or "no AI." It's a blend. It's writing some code yourself, using the AI to generate other parts, & then using the AI to help you review & debug.

Ultimately, the goal isn't just to make work faster, but to transform how work happens. Traditional automation makes the assembly line more efficient, but agentic AI has the potential to question why we have an assembly line in the first place. For businesses, this is a profound shift. It's not just about automating a customer service email response; it's about rethinking the entire customer journey.

This is the philosophy behind platforms like Arsturn, which focuses on building conversational AI to create meaningful connections. It’s not just about a chatbot answering a question. It's about providing a personalized, interactive experience that can guide a user, solve their problem, & even help them make a purchasing decision. It's a deeper level of automation that changes the nature of website engagement.

So, is agentic coding a waste? No. Is it going to be a bumpy road filled with trial & error? Absolutely. We're in the very early days. The tools will get better, & our skills in using them will improve. For now, the best approach is one of curious, critical, & hands-on experimentation.

Hope this was helpful & gives you a more balanced view of what's going on. I'd love to hear what you think, especially if you've been experimenting with these tools yourself. Let me know in the comments.