**We're reading papers again!!!**
Today we're reading some of the most noteworthy technical reports from recent weeks: the technical reports for **Kimi K2, ChatGPT Agent, Qwen3-Coder, and a blog post from Manus.** They are related because all these contents are related to Agent.
Today's guest is Zheng Boyuan, a Ph.D. student at The Ohio State University, whose research direction is Language Agent. He will lead us to read the above technical reports and blog posts together.
This is the **"Beauty of Technology" series** of "Business Interview Record". We look forward to reading papers with you, appreciating technological equality, and experiencing the beauty of technology - being your cyber group meeting :)
00:02:00 Defining and classifying Agent
00:14:50 Comparison of technical routes of Kimi K2, ChatGPT Agent, Qwen3-Coder, and Manus
00:19:05 Why is there overall disappointment with ChatGPT Agent?
00:28:29 Key aspects of Agent Training: synthetic data, reinforcement learning, safety
00:30:57 **First technical report: Kimi K2: Open Agentic Intelligence**
[github.com](https://github.com/MoonshotAI/Kimi-K2/blob/main/tech_report.pdf)
00:43:50 **Second technical report and interview: Introducing ChatGPT agent: bridging research and action**
[openai.com](https://openai.com/zh-Hans-CN/index/introducing-chatgpt-agent/)
**Sequoia Interview OpenAI: OpenAI Just Released ChatGPT Agent, Its Most Powerful Agent Yet**
[www.sequoiacap.com](https://www.sequoiacap.com/podcast/training-data-chatgpt-agent/)
01:53:38 **Third technical report: Qwen3-Coder: Agentic Coding in the World**
[qwenlm.github.io](https://qwenlm.github.io/blog/qwen3-coder/)
01:59:04 **Fourth technical blog post: Context Engineering for AI Agents: Lessons from Building Manus (Author: Yichao 'Peak' Ji)**
[manus.im](https://manus.im/zh-cn/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus)
02:06:06 Outlook: Maybe there will be a new paradigm
02:15:20 I feel that Agent is "my extended brain", and I have a "legion" (Family of Agents) behind me
02:16:41 Different Bot language styles: DeepSeek is foul-mouthed, Yuanbao is a bootlicker
> **Agent Definition**
An Agent is an intelligent system capable of interacting with the environment.
It has two basic capabilities:
**Perception**
Ability to observe the state of the environment, including obtaining external information, reading feedback signals, and parsing context.
**Action**
Ability to perform actions in the environment, such as calling tools, generating output, controlling interfaces, and modifying variables.
In short, Agent = Perception + Action
Continuously perform the "observe → decide → act" process in a loop to achieve task goals.
> **Definition and Classification of Agents**
**1. Coding Agent**
Representative products: Cursor, Windsurf
Features: Strong code generation and editing capabilities, excellent user experience
Application scenarios: Code completion, code refactoring, multi-person collaborative programming
**2. Search Agent**
Features: Combined with search engines, automatically completes information retrieval and aggregation
Application scenarios: Market research, report generation, competitor analysis, etc.
Potential: Has strong application value in enterprise-level scenarios
**3. Tool-Use Agent**
Features: Able to call a variety of external tools to complete complex tasks
Focus of application: It is the main direction of current Agent research and implementation
Example: ReAct (Reasoning + Action) type Agent, which executes tasks through tool calling
**4. Computer Use Agent**
Representative products: OpenAI Operator, Claude's Computer Use
Features: Simulates humans using computers to complete complex operations across applications
Application scenarios: Execution process automation, remote assistant, office agent
> **Comparison of Agent Technical Routes**
**1. In-Context Learning**
Features: Relies on powerful pre-trained models, and task planning and execution are achieved through prompt construction
Advantages: No fine-tuning, high flexibility
Limitations: Weak generalization ability, limited rollout length, easy to get out of control
**2. End-to-End Training**
Features: Encodes all Agent behaviors into model weights
Advantages: Stable reasoning, strong controllability
Limitations: High training cost, complex environment construction
> **Key Aspects of Agent Training**
**1. Data Synthesis**
Method: Generate a large number of high-quality trajectories
Purpose: Training Agent on how to make decisions, call tools, and manage memory in tasks
**2. Reinforcement Learning**
Conditions: Requires clearly defined tasks and verifiable rewards
Challenges: Task difficulty and environmental feedback design directly affect the quality of Agent behavior
**3. Safety Issues**
Risks: Agent has autonomous decision-making ability and is prone to misuse tools and deviate from the track
Countermeasures: Add sandbox restrictions, behavior constraint mechanisms, Human-in-the-loop
> **Outlook: Maybe there will be a new paradigm**
The core of generating data will shift from input-output data annotation to building an environment and corresponding task-reward. For example, Scale AI proposed rubrics as reward.
Can Agent achieve self-improvement? On the one hand, Agent will continuously obtain new data in the process of interacting with the environment; can it find or construct verifiable rewards by itself? Can the experience accumulated in the interaction be used more effectively?
Original title:
110. 逐段讲解Kimi K2报告并对照ChatGPT Agent、Qwen3-Coder等：“系统工程的力量”
Original description:
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