Will autonomous AI agents ever be successful as they tend to hallucinate?

The hype around "Agentic AI" is everywhere right now. We’re moving past simple chatbots to agents that can supposedly plan, use tools, and execute multi-step workflows autonomously. But there’s a massive elephant in the room: Hallucinations.

In a standard LLM chat, a hallucination is just an annoying bit of misinformation. In an autonomous agent, a hallucination is a functional catastrophe. If an agent "hallucinates" an API parameter, a file path, or a success confirmation for a task that actually failed, it can trigger a chain reaction of errors that’s impossible to debug in real-time.

It feels like we’re hitting the "Self-Driving Car" wall. We have the "90% solution" where it looks amazing in demos, but the "last 5%" of reliability seems like an infinite climb.

Are multi-agent systems (where one agent checks another’s work) actually the answer, or does that just create "hallucination loops" where they all eventually agree on a lie?

If an autonomous agent requires a human to check every step to ensure it isn’t hallucinating, is it actually an autonomous agent anymore, or just a very complex autocomplete?

Is it possible that LLMs are just the wrong "brain" for autonomous action? Or are we just one breakthrough (like Q* or better neurosymbolic integration) away from agents we can actually trust with a credit card or a production server?

TL;DR: Hallucinations make autonomous agents dangerous and unreliable. Can we ever reach "Level 5 autonomy" for AI agents, or will they always be limited to low-stakes tasks?