Increasing doubts about the direction of AI agent development

I specialize in creating AI agents and have growing concerns about this industry’s future.

I’ve worked on developing AI systems for clients for quite some time, and while I still hold faith in the technology, I am becoming increasingly worried about the industry’s progression. The gap between the expectations communicated and the actual outcomes continues to widen.

Many so-called ‘AI agents’ are just advanced scripts

To be honest, most products labeled as ‘AI agents’ are essentially workflows enhanced with some AI features. While workflows are undoubtedly beneficial, branding them as ‘agents’ leads to misconceptions about their capabilities to think independently. I end up spending a significant amount of my time clarifying this for new clients.

Presentations seem polished, but reality can be chaotic

The impressive demos showcased at tech events work seamlessly in perfect conditions. However, when faced with real-world scenarios, these systems often falter. A single unusual input from a user or an erroneous AI response can derail everything. We are aiming to build trustworthy systems, but we are still struggling with fundamental reliability challenges that remain unaddressed.

The messaging shifts based on the audience

At times, we hear that AI will soon replace all human workers, while at other moments, when scrutiny arises, we’re told they’re merely ‘tools’ for data assistance. This inconsistent messaging leaves customers baffled and complicates straightforward discussions about what these systems can realistically achieve.

The actions of industry leaders don’t reflect the hype

What frustrates me the most is the constant movement of top AI researchers between companies for significant pay raises. If they genuinely believed they were on the brink of revolutionizing the world, would they be so quick to jump ship? Their behavior suggests a lack of conviction in their own ambitious claims.

We’re chasing solutions to non-existent problems

A considerable amount of funding is directed towards creating groundbreaking AI agents that tackle issues most organizations do not face. The most successful projects I have seen focus on mundane tasks, providing tangible time-saving benefits. However, ‘automating report summaries’ doesn’t grab media attention.

We must adopt a sensible approach

It’s not that AI automation lacks potential. However, we need to curb our tendency to overhype and focus on developing dependable solutions that genuinely assist users. If we don’t, we risk disappointing many and stifling enthusiasm for this technology before it can truly flourish.

Honestly, the whole industry feels like it’s built on quicksand right now. I’ve been watching companies burn through budgets on these “intelligent agents” that can’t handle basic unexpected inputs without breaking. The worst part is when clients start comparing everything to ChatGPT and expect magic while we’re still figuring out how to make systems that don’t hallucinate important details.

I’ve been implementing AI systems in enterprise for years, so I totally get your frustrations. The gap between what marketing promises and what actually happens during deployment is huge. This feels exactly like previous tech bubbles - everyone’s just slapping AI buzzwords on existing solutions instead of making incremental improvements that actually work. The reliability issues you mentioned are scary when you’re dealing with mission-critical processes. I’ve watched organizations pull back from AI implementations because they’ve experienced how unpredictable it can be. It’s not just a technical problem - you’ve got to manage expectations across the entire organization. Your point about researcher mobility is spot-on. I’ve noticed the same thing - all this rapid talent movement makes it seem like the industry is chasing short-term gains instead of building something sustainable. You end up with this cycle where every new company promises revolutionary breakthroughs while working on the same fundamental problems everyone else is tackling. The part about solving non-existent problems really hits home. The most successful deployments I’ve seen addressed specific operational pain points rather than trying to create entirely new workflows. Maybe the industry needs to celebrate the boring success stories instead of constantly chasing the next headline-grabbing breakthrough.

The reliability problem you mentioned keeps me up at night. I’ve shipped systems that worked perfectly for months, then one weird edge case breaks everything and I’m fielding angry calls at 2 AM.

Demo curse is absolutely real. Lost count of how many times I’ve watched sales teams show flawless presentations, then I get stuck building something that handles messy real-world data. Users don’t input clean, structured information like demos assume.

What bugs me most is how we keep reinventing the same wheel. Every company thinks they need their own foundation models instead of using existing APIs to solve actual problems. I’ve seen more value from simple scripts that call GPT to categorize emails than from these massive “revolutionary” agent platforms.

The researcher job hopping thing is wild. If someone truly believed they were building AGI, would they really leave for a 20% salary bump? Feels like everyone knows this is a gold rush moment and they’re cashing in while they can.

We need to get back to basics. The most successful AI projects I’ve worked on solved one specific problem really well instead of trying to be everything to everyone. Maybe boring is exactly what this industry needs right now.

I’ve seen the exact same thing moving from traditional dev to AI projects. The terminology inflation is killing us - clients walk in expecting real autonomous systems but get fancy rule-based setups with some LLM sprinkled on top. Once that trust breaks, it’s brutal to fix. What gets me is how VCs throw money at complexity instead of what actually works. Startups raise millions for these crazy multi-agent frameworks while simple chatbots that fix real customer service problems get ignored. The funding world is completely out of touch with reality. The mixed messaging makes boardroom conversations a nightmare. Execs can’t build coherent AI strategies when industry leaders flip between ‘AI will kill us all’ and ‘it’s just a tool’ depending on what regulators want to hear. You can’t have honest talks about timelines when everyone’s getting whiplash. I think we’re heading for a correction where companies will actually demand ROI instead of buying into vague future promises. The survivors will be the ones improving existing processes bit by bit, not chasing magical breakthroughs.