What Is an LLM agent?
An LLM agent is an AI system that uses a large language model as its core reasoning engine, then combines it with planning, memory, tools, and execution logic to complete tasks. Besides generating a text response, an LLM agent can also understand a goal, decide what steps are needed, use external tools, observe the results, and continue working until it reaches a useful outcome.
LLM agents vs. chatbots: What’s the difference?
A basic chatbot is designed for conversation. It receives a user message and generates a reply. This works well for questions that can be answered in a single response.
An LLM agent is designed for task completion. It can plan a sequence of actions. For example, a chatbot can explain how to create a presentation. An LLM agent can help gather information, structure the story, generate slides, refine the wording, and produce a more complete deliverable.
This is where products like Kimi Agent fit naturally. Kimi Agent can support workflows such as researching information, processing documents, generating PPT content, analyzing spreadsheets, and handling multi-step tasks that require more than a single answer.
LLM agent architecture
A typical LLM agent architecture includes several connected parts. The LLM acts as the reasoning core, but it is not the whole system. To complete real tasks, the agent also needs planning, memory, tools, observations, and safety controls.
A simplified LLM agent architecture looks like this:
Each component plays a different role. The agent core decides what to do, planning breaks the work into steps, memory keeps track of context, and tools let the agent interact with external systems.
The agent core: The LLM as the brain
The agent core is usually a large language model. It interprets the user’s request, understands the goal, decides what information is needed, and chooses the next action.
You can think of the LLM as the agent’s brain. It coordinates the workflow, but it still needs the rest of the system to do useful work. Without tools, it can only generate text. Without memory, it may lose track of the task. Without planning, it may struggle with long or complex workflows. Without safety boundaries, it may take actions that should require verification.
In a well-designed agent, the LLM does not act alone. It operates inside a system that defines its role, available tools, task boundaries, and rules for when to stop or ask for confirmation.
Planning: How agents break down complex tasks
Planning allows an LLM agent to turn a large request into smaller, manageable steps. This is especially important when the answer cannot be found with a single response.
Some agents create a plan at the beginning and follow it step by step. Others use a more flexible loop: they take one action, observe the result, and then revise the plan. Techniques such as ReAct and Reflexion are often discussed in this context because they combine reasoning, action, feedback, and adjustment.
Planning is what helps agents move from “answering a prompt” to “managing a workflow.”
Memory: How agents keep context
Memory helps an agent keep track of what has happened and what still needs to happen.
Short-term memory is used during the current task. It may include the user’s request, recent tool results, intermediate notes, and the current plan. This helps the agent stay coherent while completing a multi-step workflow.
Long-term memory stores information across interactions. It can include user preferences, previous decisions, project history, or useful facts from earlier tasks. Long-term memory is especially useful when an agent needs to personalize future work or continue a project over time.
Memory does not mean the agent “understands” everything like a human. It means the system has a way to store, retrieve, and use relevant context when making decisions.
Tools: How LLM agents take action
Tools are what allow LLM agents to act outside the model itself. They can include search engines, databases, code interpreters, file systems, browsers, APIs, calculators, enterprise software, or other specialized systems.
For example, an agent might use:
A search tool to gather current information
A database connector to retrieve business data
A code interpreter to run calculations
A file tool to read and edit documents
A spreadsheet tool to clean and analyze data
An API to update a workflow or trigger an action
Tools are one of the biggest differences between a chatbot and an agent. A chatbot can tell you what to do. An agent with tools can help do it.
LLM agent frameworks
LLM agent frameworks help developers build, connect, and manage agentic systems. Instead of manually wiring together prompts, tools, memory, planning logic, workflow state, logs, and evaluation steps, developers can use frameworks to create reusable agent workflows.
Many agent frameworks are built around orchestration, retrieval, multi-agent collaboration, and production deployment. LangGraph focuses on long-running, stateful agent orchestration. LlamaIndex is often used for data-connected agents, especially when applications need retrieval, document parsing, indexing, and query workflows. Haystack supports production-ready LLM applications, including retrieval, search, document processing, and agent workflows. CrewAI is designed for multi-agent workflows where different agents can take on different roles and coordinate through shared processes.
These frameworks are not agents by themselves. They provide the infrastructure around agents: how agents receive context, call tools, remember information, coordinate steps, recover from failures, and produce outputs that can be inspected or improved. In practice, the right framework depends on the target workflow. A research or document agent may need retrieval and citation support. A coding agent may need file access, terminal execution, and test feedback. A business automation agent may need workflow state, approval checkpoints, observability, and integration with internal systems.
What can LLM agents do?
LLM agents have become important tools across many industries and job roles. The following section uses Kimi as an example to show how LLM agents can be applied in different scenarios.
Research and information synthesis
Research is one of the clearest use cases for LLM 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
LLM 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 LLM 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. LLM 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
LLM 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
LLM 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.
Benefits of LLM agents
LLM agents are useful because they can handle tasks that require more than one response.
First, they can manage multi-step workflows. A research report, code fix, spreadsheet analysis, or presentation is rarely completed in one step. Agents can break these tasks down and work through them systematically.
Second, they connect to tools and external systems. This lets them search the web, read documents, run code, analyze data, or interact with business software.
Third, they can preserve context. Memory allows agents to track what they have already done and what still needs to be completed.
Fourth, they reduce manual switching between tools. Users no longer have to switch between tools manually, because the agent handles the process for them.
Fifth, they can produce more personalized results. When an agent has relevant context, it can adapt its output to a user’s task, document, data, or preferred format.
Conclusion
LLM agents combine language models with agent capabilities to complete tasks that go beyond a single response. They can manage multi-step workflows across research, data analysis, coding, and content creation, while adapting their actions based on new information. As their capabilities grow, clear goals and human review remain essential for producing reliable results.