AI agents in 2026 can't plan. This paper might fix that.
· 2 min read
AI agents keep failing on multi-step tasks. Not because models are dumb. Because they plan inefficiently. A 2026 paper just proposed a fix — here's what it does.
In the year 2026, it was discovered that AI agents are incapable of planning anything. This paper may be able to remedy the situation as it proposes a solution to every problem encountered by AI agents with respect to multi-step planning.
It has been observed that various problems faced by the AI agents in the execution of multi-step tasks have nothing to do with the intelligence of the model. The major issue in planning lies in the inefficiency of the planning strategy.
A new solution has been proposed in the paper released in the year 2026 regarding the planning issue, and it explains what is meant by the introduction of new concepts regarding AI agents.
A year back, Andrej Karpathy had a talk at Y Combinator's AI Startup School, where he mentioned something important that I have realized — we are not only creating better tools; at the same time, we create a new platform. One of the most important things about the AI agents is that LLMs act as memory and as processing at the same time.
However, the question left unanswered in that talk is: if LLMs are the new OS, then why do AI agents experience problems while having multi-step planning? The answer to this question will not be intelligence for some reason. The problem of planning is determined by the bad architecture of planning.
The paper titled “Latent World Models for Long-Horizon Planning” proposes a way of creating models of the environment by training a world model in parallel with the language model. The advantage of this method is that it provides the possibility of planning while using compressed latent vectors.
Karpathy ended his talk saying, "This is the decade of agents, not the year." Reading this paper, I think he's right — but only if we solve the planning problem first.
Memory and tools we've somewhat figured out.
Planning is the hard one.
What's your take — is planning the real bottleneck for agentic AI in 2026?
Source: emasterlabs.com/llm-research-papers
Talk: Andrej Karpathy, AI Startup School 2025
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