"While new models with more parameters and better reasoning are valuable, models are still limited by their lack of working memory. Context windows and improved memory will drive the most innovation in agentic AI next year, by giving agents the persistent memory they need to learn from past actions and operate autonomously on complex, long-term goals. With these improvements, agents will move beyond the limitations of single interactions and provide continuous support."
"In 2026, the biggest obstacle to scaling AI agents-the build up of errors in multi-step workflows-will be solved by self-verification. Instead of relying on human oversight for every step, AI will be equipped with internal feedback loops, allowing them to autonomously verify the accuracy of their own work and correct mistakes. This shift to self-aware, "auto-judging" agents will allow for complex, multi-hop workflows that are both reliable and scalable, moving them from a promising concept to a viable enterprise solution."
Agentic AI will become the primary focus as raw model-size improvements slow. Enhancing context windows and human-like memory will provide agents with persistent working memory to learn from past actions and pursue complex, long-term goals autonomously. Self-verification and internal feedback loops will reduce reliance on human oversight by allowing agents to detect and correct multi-step errors, enabling reliable multi-hop workflows. Coding will serve as the decisive test of AI reasoning, with natural-language goal specification replacing manual syntax knowledge. The bottleneck will shift from writing code to clearly articulating objectives that agents can execute.
Read at InfoWorld
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