"According to the internet, startups are running entire companies on AI. Founders have AI sales teams closing deals while they sleep. AI agents are supposedly replacing full departments overnight. Meanwhile, your agents stall out. They make questionable tool calls, get stuck in loops and fail to complete tasks reliably. That doesn't mean you're behind. It means you're operating in the real world."
"Research from MIT helps explain why this gap exists. Tools like ChatGPT are now ubiquitous. MIT found that roughly 90% of employees in surveyed companies use large language models regularly at work. Coding agents such as Claude Code, Cursor and Codex have become standard in many developer workflows. But the area with the most excitement is also the area with the least success: AI agents designed to automate tasks - and eventually entire business functions."
AI systems perform well on simple, demo-style tasks but frequently fail in real-world deployments where users, enterprise systems, and constraints are unpredictable. MIT found roughly 90% of employees use large language models at work, yet 95% of pilot projects for task-specific or embedded generative AI failed to deliver sustained productivity or P&L impact in production. AI agents often make questionable tool calls, get stuck in loops, and fail to complete tasks, causing real costs in time, money, and credibility. Systems that can adapt, remember, and improve over time are necessary to convert automation into lasting business results.
Read at Entrepreneur
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