What Are AI Agents?

What is an AI agent?

An AI agent is a software system that uses AI to understand a goal, plan steps, and take actions to complete a task. Unlike a regular chatbot that mainly responds to prompts, an AI agent can work through a process. For example, it may search for information, compare sources, organize findings, create an output, and revise it when something is missing.

Why AI agents matter now

AI agents become more important because many real tasks require more than one prompt. Research, writing, data analysis and coding often involve gathering information, using tools, checking results, and improving the output. AI agents help automate these repeated steps, so humans can focus more on setting goals, reviewing results, and making final decisions.

AI agent vs AI assistant vs chatbot

AI agent, AI assistant, and chatbot are often used interchangeably, but they solve different levels of work. A chatbot mainly responds. An AI assistant helps the user work faster, usually with the user guiding each step. An AI agent is more outcome-oriented: it can plan a sequence of actions, use tools, check progress, and keep going until the task reaches a usable result.

The boundary is not always strict. A modern AI assistant may include agentic features, and a chatbot may use AI models behind the scenes. The practical difference is how much responsibility the system can take after the first response: a chatbot answers, an assistant supports, and an agent works through a goal.

For example, Kimi can function as a web-based chatbot. You can also interact with it through conversation and use it as an AI assistant to complete simple search tasks. When you enable Agent mode, Kimi can work as an AI agent, helping you complete more complex tasks, such as generating an original piece of instrumental music.

Key features of AI agents

AI agents can vary widely, but most useful systems share five core features:

Autonomy: acting with less step-by-step instruction

Autonomy means the agent can keep working without the user manually prompting every step. A user gives a goal, and the agent decides the next action. Some agents only suggest actions and wait for approval, while others can run tasks in the background, call tools, or coordinate sub-agents.

Planning: breaking a goal into smaller tasks

Planning turns a vague goal into a sequence of actions. If the user asks for a research brief, an agent may plan to define the topic, search for sources, compare findings, extract key points, write the draft, and produce a final summary. Good planning also includes checkpoints, so the agent can notice when it needs more information, when source quality is weak, or when the output should be reviewed by a person.

Tool use: search, files, apps, code, and APIs

Tool use is one of the main reasons AI agents are more capable than basic chatbots. An AI agent may use web search for recent information, file readers for PDFs and spreadsheets, code execution for analysis, APIs for external data, browsers or app connectors for workflow actions, and generators for documents, slides, spreadsheets, or websites.

Memory and learning: remembering task details and improving within a workflow

LLMs are stateless by default: they do not permanently remember previous interactions on their own. An agent can provide the LLM with more persistent context, which gives the agent a form of long-term memory. This memory helps the agent maintain context such as user instructions, uploaded files, task constraints, intermediate findings, and previous decisions within the workflow.

Collaboration: working with humans or other agents

AI agents collaborate with humans by asking clarifying questions, showing drafts, and waiting for approval before high-impact actions. They can also collaborate with other agents or sub-agents when a task can be divided into parallel workstreams. For example, one sub-agent may search sources, another may extract data, another may draft, and another may review for gaps.

How do AI agents work?

AI agents usually follow a loop: understand the goal, create a plan, gather information, use tools, take action, review the result, and continue if needed.

Step 1: Understand the user’s goal

Agents understand users’ goals by interpreting the request, identifying the intended outcome, and turning vague language into a workable task representation. They look for signals such as the task type, topic, audience, output format, constraints, available files, and source requirements. If the request is ambiguous, a well-designed agent may ask a clarifying question or make a reasonable assumption before creating a plan.

Step 2: Create a task plan

Next, the agent creates a plan by decomposing the goal into smaller actions, deciding the order of those actions, and matching each step with the tools or information it needs. It may identify dependencies, such as researching before writing or cleaning data before generating charts. Planning is important because it lets the user correct the workflow before the agent spends time on the wrong task.

Step 3: Gather information and use tools

Once the plan is set, the agent gathers the information it needs. This may involve web search, uploaded files, APIs, code inspection, spreadsheet analysis, or task-specific tools. Tool use is what allows an AI agent to interact with external information and systems instead of relying only on its built-in model knowledge.

Step 4: Take action and generate output

After gathering the necessary information, the agent takes action and produces an output. Depending on the task, this output may be a report, a chart, a piece of code, a slide deck, or a completed workflow. The best outputs are well structured, making the result easier to review, edit, and use.

Step 5: Check results, revise, and continue if needed

The final step is review. The agent checks whether the output matches the original goal and revises the result when it finds gaps, errors, or missing details. A human should still review important work, especially when the task involves decisions or public publishing. This review loop is what makes AI agents more valuable than one-shot text generation.

What can AI agents do?

AI agents have become important tools across many industries and job roles. The following section uses Kimi as an example to show how AI agents can be applied in different scenarios.

Research and information synthesis

Research is one of the clearest use cases for AI agents because it usually requires searching, reading, comparing, extracting, summarizing, and organizing information. An agentic workflow can work through sources and produce a more structured result. In Kimi, users can use Kimi Deep Research for longer research workflows, it is suitable for deeper briefs, topic analysis, market scans, and source-backed reports.

Example prompts:

Writing, summarizing, and content planning

AI agents can support writing by turning messy inputs into structured outputs: they can summarize long material, create outlines, draft sections, adapt tone, and revise based on feedback. Kimi AI Agent can be used for writing and long-context conversation, which is helpful when working with source notes, long documents, or multi-step content tasks.

Data analysis and spreadsheet work

Many business tasks happen in spreadsheets. AI agents can help clean data, identify patterns, generate formulas, explain tables, and create charts or pivot-style summaries. With Kimi Sheets, you can create spreadsheets, generate formulas, build pivot tables and charts, convert file formats, and clean messy data.

Possible spreadsheet workflows include:

• Cleaning inconsistent names, categories, or date formats

• Explaining formulas and generating new ones

• Summarizing survey results or sales data

• Creating charts for a report

• Converting unstructured data into a table

Human review is still important. Agents can misread messy data or choose the wrong analytical method, but for repetitive spreadsheet work, they can reduce manual effort and help users move faster.

Coding, testing, and workflow automation

AI agents are increasingly useful in software development because coding is naturally multi-step. A developer may need to understand requirements, inspect existing logic, generate code, test the result, read errors, and revise the implementation. In Kimi, users can ask for code generation, code explanation, debugging ideas, small scripts, and workflow automation plans.

Documents, slides, websites, and business operations

AI agents are useful when the final output is not just text. Many work products are structured deliverables: documents, presentations, spreadsheets, websites, reports, and operating plans. Kimi is able to plan and complete tasks such as website generation, PPT creation, deep research, and document or sheet handling.

Multi-agent workflows for larger tasks

Some tasks are too broad for a single linear workflow. Competitive research, long-form writing, large-scale search, batch analysis, and multi-source synthesis may benefit from multiple agents working in parallel. Kimi Agent Swarm is a product surface for larger-scale agentic work. It supports up to 4,000 parallel tool calls and can orchestrate 300+ sub-agents for use cases such as large-scale search, long-form writing, and batch tasks. The advantage is speed and coverage.

What are the main types of AI agents?

AI agents also come in different types, and each type is suited to different use cases:

Simple reflex agents

Simple reflex agents respond to current input using fixed rules. They do not build a detailed model of the world or plan far ahead. A basic support bot that replies based on keywords is close to this pattern: if the user says "password reset," it gives password reset instructions. This can be fast and reliable for narrow tasks, but it is limited when the situation changes.

Model-based agents

Model-based agents maintain some internal representation of the environment and use that model to decide what action makes sense. For example, a scheduling assistant may track calendar availability, time zones, participants, and meeting constraints before proposing a slot. Beyond simply reacting to messages, it also uses context to make better decisions.

Goal-based agents

Goal-based agents choose actions based on a desired outcome. They can compare possible paths and select the one most likely to reach the goal. This is close to how many modern AI agents are described in everyday use: the user gives a goal, such as "prepare a research brief," and the agent works backward from that outcome.

Utility-based agents

Utility-based agents evaluate not only whether a goal can be achieved, but which outcome is best according to a scoring function or preference. For example, a travel planning agent might optimize for cost, travel time, number of stops, hotel location, and user preferences. It is not enough to find any trip; it tries to find the best trip under the user's constraints.

Learning agents

Learning agents improve their behavior based on feedback or experience. They may adjust future actions when previous results were incomplete, inaccurate, or inefficient. In user-facing products, learning can be constrained for privacy and safety, but the general idea is important: feedback helps agents better match user intent.

Single-agent and multi-agent systems

A single-agent system uses one agent to handle the workflow, while a multi-agent system divides work across multiple agents or sub-agents. Multi-agent workflows can be useful for large tasks because different agents can search, analyze, write, review, or test in parallel. The tradeoff is coordination: without clear task boundaries, shared context, and review mechanisms, parallel work can create duplicated effort or inconsistent outputs.

Conclusion

AI agents are becoming a practical way to handle difficult work. By understanding goals, planning steps, using tools, taking action, and reviewing results, they can help users complete complex tasks. Kimi AI Agent is one of the best practices from deep research and sheets to coding support, document handling, website generation, and Agent Swarm. As AI agents continue to improve, learning how to use them effectively will become an increasingly practical way to save time and improve productivity in both work and everyday life.