SUMMER
In recent years, AI Agents have rapidly gained popularity in the tech community. They are characterized as intelligent assistants capable of autonomous planning, action, and learning, rather than just being a “chat partner” that responds to your queries. Let’s unveil the “black box” of AI Agents!
🌈 What Comprises an AI Agent?
According to academic and industry overviews, AI Agents typically consist of the following three components:
Perception Module – Receives inputs (text, images, API calls, etc.) and understands the context and task environment.
Cognitive Module – Makes decisions and plans based on goals, using large language models (LLMs) for reasoning, and executes actions through tool calls.
Execution and Feedback Module – Executes tasks (such as calling APIs, writing, publishing, etc.) and continuously optimizes and learns based on feedback.
This closed-loop structure of “perception → cognition → execution → feedback” is the foundation of AI Agent intelligence.
Currently, there are many open-source and commercial frameworks that support rapid Agent development. Here are a few representative tools:
AutoGen (Microsoft): A multi-Agent collaboration framework that supports asynchronous messaging, task tracking, and includes analysis and low-code development interfaces, suitable for complex task collaboration.
LangChain: One of the hottest frameworks this year, adept at chaining LLMs, memory, and tools into pipelines, enabling dialogue, retrieval enhancement, task execution, and more.
LangGraph: Manages task processes and states through graph structures, suitable for complex scenarios requiring fine dependency control.
AutoGPT: The most typical “crash-style automatic agent” that automatically decomposes tasks, calls the internet and tools to advance based on given goals.
Among these, the AutoGen framework stands out. Unlike other Agent frameworks (such as LangChain, which emphasizes chain calls and tool integration, ReAct, which focuses on the thinking and action cycles of single Agents, and CrewAI, which emphasizes role division and collaboration), AutoGen’s uniqueness lies in its use of “dialogue” as the core mechanism for collaboration between Agents, supporting flexible and highly configurable communication processes among multiple Agents, allowing human users to participate, control, or intervene throughout the task process. It is particularly suitable for building complex, long-term, multi-stage task “AI team collaboration systems.” It resembles an “operating system” for building teams, defining processes, and setting roles for AI Agents.
🌈 What Can AI Agents Do?
AI Agents are not just simple chatbots; they are intelligent systems capable of autonomous planning, execution, and learning. When you give them a goal, they can not only understand the task but also autonomously decompose it, call tools, take initiative, and iterate based on feedback. This is what truly gives AI its “execution power.” Kiplinger points out: “Unlike chat tools that require step-by-step prompts, AI Agents can think and act independently.”
Imagine this process: you ask the Agent to help plan a business trip. It will first understand the requirements (location, time, budget), then automatically check flights, compare hotel prices, book, and generate an itinerary, all without you having to give step-by-step instructions. The Financial Times emphasizes that modern AI Agents are evolving from “assistance-type copilots” to “full-process autopilots,” capable of analyzing data, understanding context, and making decisions autonomously, although they are still in a medium to low level of autonomy.
Companies are also accelerating the application of these systems. For example, Salesforce, ServiceNow, and SAP use Agents to automatically handle customer service tickets, write emails, and process reimbursements. ServiceNow has significantly reduced the processing time for complex cases by 52%.
At the 2025 Build conference, Microsoft pointed out that since this year, the usage of Agents has doubled, and Copilot plugins have evolved from simple assistants to true task-executing partner platforms. Kiplinger also mentioned that Agents can control the mouse and keyboard to complete operations like ticket purchasing and emailing without human intervention.
In terms of industry applications, there are cases across customer service, manufacturing, finance, law, retail, and education. Wonderful, an Israeli startup, has raised $34 million to focus on building voice + chat + email customer support Agents for non-English markets; the legal firm A&O Shearman has collaborated with Harvey to develop an Antitrust review Agent for handling complex legal processes; and in the fashion industry, LVMH and Diane von Furstenberg are exploring applications of Agents for virtual styling recommendations and supply chain planning, providing “visible intelligence” and “invisible efficiency improvements.”
In the security domain, Kiplinger also noted that companies like Amazon, Microsoft, and CrowdStrike have launched security Agents capable of automatically patching and simulating attacks, showcasing their potential in IT protection.
The underlying technological drivers include the integration of large language models (LLMs), predictive AI, and visual and conversational modules, combined with reinforcement learning, neural networks, and other evolutionary algorithms, supporting environmental perception, decision planning, tool invocation, and closed-loop feedback. As the FT stated, these Agents are not just based on fixed scripts but are continuously analyzing, learning from data, and self-optimizing. In terms of collaboration, there is a trend of multiple Agents working together, such as Microsoft’s concept of an agentic web: different role Agents can collaborate across platforms to achieve complex goals, allowing users to select suitable Agents to enhance productivity.
🌈 What Can AI Agents Do?
For ordinary people, you can also build your own AI Agent. For example,
If I want to travel to Tokyo, Japan, I can ask the Agent to help me plan a complete itinerary:
💡 Overall Goal:
“I want to visit the classic attractions in Tokyo within 5 days, with a budget of 8000 RMB, including airfare, hotel, tickets, and meals.”
🧠The AI Agent Collaboration Process
✅ 1. User Inputs Requirements → UserProxyAgent Receives
You only need to describe the task in natural language, such as:
“Please help me design a 5-day free travel itinerary in Tokyo, with a budget not exceeding 8000 RMB.”
This requirement is sent to the Agent system.
✅ 2. Task Analysis and Decomposition → PlannerAgent
This Agent will understand your goal and decompose it into multiple sub-tasks, such as:
Getting flight information
Finding hotels (location, price, rating)
Planning daily sightseeing routes
Estimating total costs (tickets, transportation, meals)
Outputting a visual itinerary
It will assign each sub-task to other Agents.
✅ 3. Flight & Hotel Inquiry → TravelAgent (invoking external APIs or databases)
Searching for flight prices from the departure city (e.g., Shanghai) to Tokyo, including round-trip times and airlines.
Searching for budget hotels near transportation hubs (e.g., Shinjuku, Shibuya) and filtering for ratings > 4.
Outputting recommended combinations: e.g., “3-star hotel + Spring Airlines ticket, total price about 3200 RMB.”
✅ 4. Sightseeing Route Planning → MapAgent + CultureAgent Collaboration
MapAgent retrieves the geographical locations and transportation routes of major attractions in Tokyo (e.g., Senso-ji, Akihabara, Tokyo Tower, Disneyland).
CultureAgent recommends themed itineraries based on user interests (history, food, modern trends, etc.).
Automatically generating daily itinerary maps and routes, such as:
Day 1: Senso-ji → Tokyo Skytree → Sumida River Cruise
Day 2: Meiji Shrine → Harajuku → Shibuya Check-in
✅ 5. Cost Estimation → BudgetAgent
This Agent will integrate the prices of each part and output a budget analysis,ensuring it does not exceed the budget. If there is an overspend, it will remind PlannerAgent to make adjustments.
✅ 6. Summary Table → AssistantAgent Reports Results
Integrating all content, outputting a beautifully formatted travel plan PDF or web preview, including:
Daily itinerary arrangements (attractions + time slots)
Map navigation links
Budget explanations and suggestions
Booking and visa reminders
You can also continue to tell it: “Add a leisure hot spring day,” and the Agent will automatically adjust the route and recalculate the budget.
🌟 Final Result (User Perspective)
You will receive a travel plan automatically generated by the AI assistant, including:
✅ Reasonable routes✅ Comfortable accommodations✅ Budget control✅ Real-time adjustment capabilities✅ No need to check guides/compare prices/rankings yourself
This multi-Agent collaboration model is a significant manifestation of AI Agents moving towards “personal assistant” capabilities, no longer just a single Q&A, but a complete execution of task processes.