What are the main challenges keeping AI agents from bei...

None of the individual obstacles are exotic — the issue is that several of them compound at once on any task complex enough to matter:

• Reliability gaps — even strong agents fail a meaningful share of real-world tasks, and the failure rate needed for unsupervised autonomy is far lower than what’s typical today. A benchmark score in the sixty-percent range on real coding tasks means roughly two in five attempts still need a human to catch or fix them.
• Long-horizon degradation — accuracy per step doesn’t stay constant as a task stretches to dozens of steps; small errors early compound into large failures later, and context compression across a long run loses fidelity in ways that make recovery harder than starting fresh would be.
• Verification is often as hard as the task — for complex technical work, checking that an agent’s output is correct requires nearly the same expertise as producing it, which undercuts the whole point of delegating the task to begin with.
• Adversarial exposure — an autonomous agent taking real actions is a bigger target for prompt injection and manipulation than one that only produces text a human reviews before anything happens.

The practical response isn’t to wait for these to resolve before deploying agents at all — reliability varies enormously by task type, so the right move is matching oversight to measured reliability per domain: near-autonomous for simple, high-reliability tasks like structured data extraction, heavy human review for the categories still failing a third or more of the time.