Enthusiasts caim we’re entering a world of AI assistants that plan vacations and write code, but social posts reveal a different story. The hype is huge, yet real‑world agentic AI remains glitchy, over‑promising and under‑delivering.
A hype cycle that keeps repeating itself
Anyone who scrolls through tech Twitter or r/Artificial warns that a wave of AI agents will soon automate our daily tasks. Visionary threads liken them to Jarvis or Samantha from Her. In reality, agentic AI today amounts to a handful of prototypes and chatbots that still require constant supervision. According to recent reporting, an AI agent is a piece of software that can perform a multi‑step process without user intervention, chaining together smaller tasks to achieve a goal.
But the timeline of agentic AI is far from the futuristic visions peddled by influencers. Hype around autonomous agents began in 2023 and peaked in 2024 when startups released personal finance bots, shopping assistants and scheduling aides. Most fizzled, hamstrung by poor reasoning and hallucinations. In 2025, some improvements emerged—Klarna’s customer‑service assistant reportedly handled the work of 700 full‑time agents, and at Microsoft and Google up to 30 % of new code is generated by AI tools. Yet these successes are narrow and highly managed. Outside of specific, high‑volume support tasks, agentic AI remains unreliable.
Charting the hype vs. reality
Below is a simple chart illustrating the hype cycle of agentic AI—tracking social media hype versus real‑world adoption. Notice how the hype skyrocketed in late 2023, dipped in mid‑2024 as early products flopped, and ticked up slightly in 2025 with corporate deployments. Real‑world adoption remains far lower.
Where AI agents shine—and fail
The reason there is such a gap between hype and reality lies in the fundamental limitations of today’s large language models. When tasks require persistent memory, external tool use or complex reasoning, AI agents break down. Early prototypes promised to book flights, file taxes and manage finances; in practice they hallucinated travel dates or misfiled forms. According to industry insiders, agentic AI is working best in two narrow domains: customer support, where a model can follow a pre‑defined script and handle repetitive inquiries, and coding assistance, where it can auto‑complete functions under human supervision. Even then, developers report bugs, hallucinations and the need to manually verify outputs.
Companies are still investing—and hiring
Despite the issues, the promise of agentic AI has captured corporate budgets. In 2025, Klarna’s AI customer assistant eliminated the need for 700 human agents, saving millions in labour costs. Microsoft’s CEO Satya Nadella boasted that 20–30 % of new code at the company is generated by AI. Startups like XAI and Anthropic continue to announce “agentic” models that can reason and call external functions. Meanwhile, enterprise consulting firms are creating new roles like “context engineers” to tune and monitor these agents.
Still, many in the tech community caution against blind adoption. A widely‑shared blog post on r/Artificial noted that “today’s agents are basically interns who need constant babysitting.” Even OpenAI, which consolidated its Operator and Deep Research teams into a new ChatGPT Agent group in July 2025, quietly admitted the technology is still glitchy. We’ve covered the details in our deep dive on ChatGPT’s new AI Agent, including what makes it different from earlier prototypes.
For now, the dream of a fully autonomous digital butler remains just that—a dream.
