Improving AI Agent Planning and Reasoning: Challenges and Practical Solutions

The current planning capability of AI agents is limited and their complex reasoning ability is insufficient; methods such as COT/TOT do not observe feedback and are only suitable for simple tasks or initialization, while approaches like ReAct and Reflection, although feedback‑aware, lack global thinking and often become trapped in inefficient local oscillations.

How can these issues be addressed?

In practice, combining COT planahead with Reflection provides a balance of efficiency and accuracy and is widely adopted.

Algorithmically, employing structured thinking memory and a “slow thinking” approach similar to OpenAI o1 can improve reasoning depth.

From a product perspective, white‑box interaction and domain‑specific SOPs serve as effective complementary measures.

These contents belong to the DataFun Data Intelligence Knowledge Map 3.0 – Agent module.

The Data Modeling domain includes the following topics:

Application of Graph Neural Networks in E‑commerce Recommendation Systems

Insights into Large‑Scale Model Evaluation in Major Tech Companies

Breakthroughs in Large Model Fine‑tuning

Advanced Retrieval in the Upgraded RAG

Agent Technical Challenges and Trends

Multimodal Learning

Implementation Paths and Strategies for LLMOPS Solutions

Engineering AI Infra: Speculative Sampling and Communication Optimization

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Instructor:

Qi Xiang – Ant Group NLP Algorithm Lead, Senior Algorithm Expert at Ant Group, undergraduate from USTC, Ph.D. in Computer Science from the Chinese Academy of Sciences, specializing in NLP and machine learning, responsible for ToB Agent algorithm development, focusing on knowledge engineering, complex task reasoning, scenario evaluation, and system evolution in serious B‑side applications.