AUTOMATION WORKFLOW

Kimi Agent Mode

Agent Mode is for tasks that take more than one step. Instead of giving a single answer, Kimi can plan the work, execute it, and keep refining the result so you can go from idea → draft → finished output with less manual effort.

Explore Features Get Started

Research → Output (End-to-End)

Ask Kimi to research a topic, summarize key points, create a table, and produce a final document ready to publish or share. Great for reports, comparisons, and content planning.

Build & Refine (Region Editing)

Generate a page, then improve specific sections without rewriting everything. Agent Mode helps you edit "just this part," fix weak areas, and iterate until it matches your goal.

Kimi Agent Mode (How It Works + Examples)

Most AI tools today are glorified chatbots. You type a question, get a response, and the conversation ends there. If you want to turn that response into something useful say, a research report, a working website, or a data analysis dashboard you’re stuck doing the manual work yourself.

Kimi Agent Mode changes the equation entirely. Instead of just answering questions, Kimi acts as an autonomous worker that plans, executes, and delivers complete projects. It can research across dozens of sources, write 10,000-word reports with proper citations, generate interactive spreadsheets from raw data, and even build multi-page websites from a simple text description all without you micromanaging every step.

Whether you’re a knowledge worker drowning in research tasks, a developer who needs to prototype quickly, or a business analyst processing massive datasets, understanding how Kimi Agent Mode works can fundamentally change how you approach complex work. Let’s break down exactly what this capability does, where it shines, and how to use it effectively.

What Is Agent Mode?

Agent Mode represents a fundamental shift from conversational AI to autonomous task execution. While standard chat modes respond to isolated prompts, Agent Mode operates as a persistent, goal-oriented system that can manage multi-step workflows, make decisions about tool usage, and deliver structured deliverables rather than just text responses.

At its core, Kimi Agent Mode is powered by the Kimi K2.5 model—a 1 trillion parameter Mixture-of-Experts (MoE) architecture with 32 billion active parameters per token. But the technical specs matter less than what the system can actually do. When you activate Agent Mode (branded as "OK Computer" in the consumer interface), Kimi gains access to a virtual computing environment complete with a file system, web browser, code interpreter, and terminal access.

From Chatbot to Digital Worker

Traditional AI interactions follow a simple pattern: input → processing → output. Agent Mode introduces a planning layer that breaks down complex objectives into discrete steps. If you ask it to "create a market analysis of the electric vehicle industry," it doesn’t just generate text about EVs. Instead, it:

Plans the approach: Determines what data it needs (market size, key players, growth projections, regional breakdowns)
Gathers information: Uses web search and browsing tools to find current data from multiple sources
Processes and analyzes: Organizes findings, identifies trends, and cross-references data points
Creates deliverables: Generates structured outputs perhaps a Word document with executive summary, an Excel file with pivot tables, or a slide presentation
Iterates based on feedback: Adjusts the output if you request changes or additional analysis

What makes this different from simply chaining together chat prompts is the autonomy. Kimi K2.5 can execute up to 200-300 sequential tool calls in a single session without losing coherence or context. It decides which tools to use, when to search for more information, and how to structure the final output all while maintaining awareness of the original goal.

The Architecture Behind the Agent

Kimi Agent Mode relies on several technical innovations that enable reliable long-horizon task execution:

Native Tool Integration: Unlike models that treat tools as external add-ons, K2.5 was trained end-to-end with tool use as a core capability. It has "mastered" over 20 tools including Python execution, web browsing, image generation, and file manipulation. This isn’t just API calling it’s an intrinsic understanding of how to sequence actions to accomplish goals.

Multimodal Grounding: The model processes text, images, and video natively. This means you can upload a screenshot of a website design, and Agent Mode will generate the HTML/CSS code to recreate it. You can provide a video walkthrough of a workflow, and it will document the steps or build automation scripts.

Context Stability: With a 256,000-token context window and specialized training for long-form coherence, the agent maintains consistency across extended sessions. This addresses a common failure point in other systems where performance degrades after dozens of interaction turns.

Agent Swarm Capability: For tasks that can be parallelized, K2.5 can spawn up to 100 sub-agents working simultaneously. Instead of researching 50 companies sequentially, it can assign each company to a separate sub-agent, reducing execution time by up to 80% (4.5x speedup) compared to single-agent workflows.

The Four Modes of Operation

Kimi K2.5 offers four distinct operational modes, and choosing the right one determines your results:

Instant Mode: Fast responses for simple queries. Use this when you need quick facts or straightforward text generation. No tool access, minimal reasoning overhead.

Thinking Mode: Deep reasoning for complex problems. The model takes time to work through logic puzzles, math problems, or strategic questions. Better for planning and analysis when you don’t need external data.

Agent Mode: The full autonomous worker described above. Ideal for deliverables requiring research, data processing, or multi-step execution. This is where the tool use and planning capabilities activate.

Agent Swarm (Beta): Parallel execution for large-scale tasks. When you need to process hundreds of items, compare dozens of competitors, or conduct broad research across many domains, Swarm mode distributes the work across dynamically instantiated sub-agents.

Understanding these distinctions prevents frustration. You wouldn’t use a bulldozer to plant flowers, and you shouldn’t use Agent Swarm for simple questions. Conversely, trying to research 100 niche markets in single-agent mode will take hours rather than minutes.

What Agent Mode Is Good For

Agent Mode excels at cognitive assembly work tasks that require gathering disparate pieces of information, processing them through specific methodologies, and packaging them into structured formats. It’s particularly valuable for knowledge workers who spend significant time on research, documentation, data preparation, and content creation.

High-Volume Research and Synthesis

If your job involves staying current with industry trends, competitive intelligence, or academic literature, Agent Mode acts as a research assistant that never sleeps. It can:

Monitor multiple sources simultaneously: Search academic databases, news sites, company reports, and technical documentation in parallel
Synthesize conflicting information: When sources disagree, it identifies discrepancies and provides analysis of which claims have stronger evidence
Maintain source trails: Unlike generic AI summaries, proper Agent Mode execution tracks citations and references, allowing you to verify claims
Scale horizontally: Through Agent Swarm, you can research hundreds of topics or companies concurrently, producing comprehensive comparison matrices that would take human teams weeks to compile

For example, a venture capitalist might use Agent Swarm to analyze 100 startups across 12 evaluation criteria, generating a structured spreadsheet with risk assessments, market sizing, and competitive positioning work that traditionally requires an army of analysts.

Content Creation at Scale

Writers, marketers, and communications professionals often face the blank page problem. Agent Mode doesn’t just generate text; it handles the entire content production pipeline:

Research-backed long-form content: Generate 5,000-word white papers or 10,000-word technical reports with proper structure, citations, and formatting
Multi-format asset creation: From a single brief, create a blog post (Word doc), social media thread (text file), presentation slides (PPT), and infographic data (Excel) all styled consistently
Technical documentation: Upload API schemas or code repositories and generate user guides, developer documentation, or tutorials with working code examples
Visual-to-content workflows: Provide mockups or wireframes and receive complete website copy, UX microtext, and SEO metadata

The key differentiator is persistence. While standard AI might generate a blog post outline, Agent Mode researches statistics to include, finds relevant case studies, writes the full draft, creates accompanying charts from data it finds, and packages everything into a formatted document ready for stakeholder review.

Data Processing and Visualization

Analysts often spend 80% of their time cleaning data and 20% analyzing it. Agent Mode reverses this ratio by handling the tedious preparation work:

Large dataset handling: Process Excel files with hundreds of thousands of rows, performing cleaning, normalization, and analysis
Automated visualization: Transform raw data into interactive dashboards with appropriate chart types, filtering mechanisms, and summary statistics
Cross-source integration: Combine data from PDFs, web tables, CSV uploads, and API responses into unified datasets
Financial modeling: Build complex Excel models with pivot tables, scenario analysis, and LaTeX-formatted mathematical explanations in accompanying documentation

A financial analyst might upload Q3 earnings reports from 20 competitors and ask for a comparative analysis. Agent Mode extracts relevant metrics, standardizes currency conversions, identifies trends, and produces a PowerPoint presentation with charts ready for the board meeting.

Software Development and Prototyping

For developers, Agent Mode serves as a full-stack collaborator:

Visual coding: Upload screenshots of UI designs or wireframes and receive production-ready HTML, CSS, and JavaScript that matches the visual layout pixel-perfectly
Feature implementation: Describe a feature in natural language ("add user authentication with JWT tokens and a login modal") and receive working code integrated into your existing codebase
Debugging and refactoring: Provide error logs or code snippets and receive diagnosed issues with corrected implementations
Documentation generation: Automatically generate API docs, README files, and inline code comments based on actual code analysis

The "vibe coding" capability is particularly notable. Non-technical founders can describe a web application concept, and Agent Mode will architect the database schema, build the frontend interface, implement backend logic, and deploy the result to a live URL all from a text description.

Complex Planning and Project Management

Agent Mode functions as a strategic planning partner for complex initiatives:

Event planning: Research venues, compare vendors, create timeline spreadsheets, draft invitation copy, and generate budget trackers
Product launches: Coordinate research on market positioning, competitive analysis, messaging frameworks, and launch checklists across multiple document types
Academic research: Manage literature reviews, methodology documentation, and citation management across hundreds of sources
Trip planning: Research destinations, compare flight options, create itinerary spreadsheets with cost breakdowns, and generate daily schedules with maps and reservation details

The common thread across all these use cases is orchestration. Agent Mode handles the coordination between different tools, data sources, and output formats that would otherwise require switching between multiple applications and manual copy-pasting.

Example Tasks (Research → Output)

To understand how Agent Mode works in practice, let’s walk through three detailed examples showing the progression from initial prompt to final deliverable.

Example 1: Industry Intelligence Report

The Request: "Research the current state of quantum computing in 2026. Focus on commercial applications, key players (IBM, Google, IonQ, Rigetti), error correction breakthroughs, and market forecasts. Deliver a 15-page Word document with executive summary, technical appendices, and a competitor comparison matrix."

The Execution Process:

Phase 1: Research Orchestration
The agent begins by decomposing the request into research streams. It initiates parallel searches for:

Recent quantum computing commercial deployments (2025-2026)
Financial reports and market positioning for the four specified companies
Technical papers on error correction milestones
Market sizing reports from analyst firms

It uses web browsing tools to access paywalled content where possible, extracts data from PDF reports, and cross-references claims across multiple sources to verify accuracy.

Phase 2: Analysis and Synthesis
As data flows in, the agent identifies key themes:

IBM’s Condor processor milestones vs. Google’s Willow chip developments
IonQ’s trapped-ion approach advantages in certain algorithms
The shift from NISQ (Noisy Intermediate-Scale Quantum) to error-corrected logical qubits
Commercial traction in cryptography and pharmaceutical modeling

It flags conflicting data such as varying market size projections between McKinsey and BCG reports and notes methodological differences in how each firm defines "quantum advantage."

Phase 3: Document Construction
Using the research, the agent:

Creates a Word document with proper heading hierarchies
Writes an executive summary highlighting the transition from experimental to early commercial phase
Drafts technical sections explaining error correction progress for non-specialist readers
Generates a comparison matrix in Excel showing qubit counts, error rates, and commercial focus areas for the four companies
Inserts citations as footnotes with working hyperlinks to sources

Phase 4: Review and Refinement
The agent performs internal consistency checks, ensuring that financial figures cited in the text match the Excel matrix and that technical claims in the executive summary align with detailed appendix explanations. It adds a "Methodology" section explaining research limitations and confidence levels for predictions.

Deliverable: A professionally structured 15-page report with an accompanying Excel workbook, ready for executive review. Total execution time: ~12 minutes. Manual equivalent: 2-3 days of analyst time.

Example 2: Visual Website Prototyping

The Request: "I’ve sketched a landing page for my new coffee subscription service on paper [image uploaded]. I want a responsive website with the layout in my sketch, modern styling, a functional email signup form, and a pricing section. Deploy it so I can share the link with investors."

The Execution Process:

Phase 1: Visual Analysis
The agent analyzes the uploaded sketch using its multimodal capabilities. It identifies:

A hero section with headline and call-to-action button placement
A three-column feature grid below the fold
A pricing table with three tiers
A footer with contact information

It notes the rough spatial relationships and hierarchical importance of elements.

Phase 2: Design System Generation
Rather than jumping straight to code, the agent first establishes a design system:

Color palette extraction (suggesting coffee-themed browns and creams if not specified)
Typography hierarchy (heading sizes, body text, line heights)
Spacing scale (padding, margins, grid gaps)
Component library (buttons, cards, form inputs)

Phase 3: Frontend Development
The agent writes HTML5 with semantic structure, CSS3 with Flexbox/Grid layouts for responsiveness, and vanilla JavaScript for interactivity:

Responsive navigation with mobile hamburger menu
CSS animations for scroll reveals and hover states
Functional email capture form with validation (storing submissions or integrating with EmailJS)
Pricing toggle for monthly/annual switching

Phase 4: Deployment
Using terminal tools, the agent initializes a Git repository, commits the files, and deploys to a temporary hosting service (often using platforms like Netlify or Vercel via CLI). It configures DNS and SSL certificates automatically.

Deliverable: A live, shareable URL hosting a fully responsive website that matches the original sketch’s layout but with professional polish. The investor can click through on mobile or desktop, test the email form, and view smooth animations. Total execution time: ~8 minutes. Manual equivalent: 4-6 hours of designer and developer time.

Example 3: Financial Data Analysis with Agent Swarm

The Request: "Analyze the last 5 years of financial performance for all S&P 500 companies in the healthcare sector. Identify companies with consistent revenue growth above 10% annually but declining R&D investment as a percentage of revenue. Create a presentation highlighting the top 10 opportunities and risks."

The Execution Process:

Phase 1: Task Decomposition
Because this involves analyzing approximately 60+ companies independently, the agent activates Agent Swarm mode. It:

Defines the universe of healthcare sector tickers (JNJ, PFE, UNH, etc.)
Creates parallel sub-tasks for each company: fetch 5-year financials, calculate growth rates, analyze R&D trends
Sets aggregation criteria for identifying the "top 10" based on the specified metrics

Phase 2: Parallel Data Acquisition
The swarm spawns sub-agents that work simultaneously:

Sub-agent 1: Analyzes Johnson & Johnson (10-K filings, quarterly reports)
Sub-agent 2: Analyzes Pfizer (same data sources)
[…continuing for all 60+ companies…]

Each sub-agent uses web search to find recent 10-K filings, extracts revenue and R&D figures using code interpreter to parse tables, and calculates year-over-year growth percentages.

Phase 3: Cross-Validation and Filtering
As results stream in from the swarm, a coordinator agent (K2.5’s orchestration layer) validates data consistency. It flags outliers such as pandemic-related revenue spikes in vaccine makers and adjusts trend calculations to account for anomalous years.

It filters the dataset to companies meeting the criteria: >10% CAGR revenue growth but declining R&D/revenue ratio over the 5-year window.

Phase 4: Strategic Analysis
For the filtered subset, the swarm conducts deeper analysis:

Correlation analysis between R&D decline and pipeline strength (patent expirations, new drug approvals)
Competitive positioning versus peers
Regulatory risk assessment based on recent FDA warning letters or policy changes

Phase 5: Presentation Assembly
The coordinator generates a PowerPoint presentation:

Slide 1: Executive summary with sector-wide trends
Slides 2-11: Individual company deep-dives with charts showing revenue vs. R&D trends
Slide 12: Risk matrix plotting the 10 companies by opportunity size vs. innovation sustainability
Slide 13: Methodology and data sources

Charts are generated using Python (Matplotlib/Plotly) and embedded as high-resolution images.

Deliverable: A 13-slide presentation with supporting Excel data sheets containing raw financials and calculated metrics for all 60+ companies. Total execution time: ~15 minutes (parallelized). Manual equivalent: 1-2 weeks of financial analyst time.

Best Practices (Instructions, Checks)

While Agent Mode is powerful, it requires thoughtful interaction to get optimal results. These practices ensure you get high-quality outputs without wasting tokens on unnecessary iterations.

Be Explicit About Output Formats

Vague requests yield generic results. Instead of "write a report about AI," specify:

"Create a 2,000-word technical report in Word format with the following structure: Executive Summary, Technical Background (with LaTeX equations for transformer architecture), Current Limitations (bulleted list), Future Directions (numbered list), and References (APA style). Include at least 5 charts generated from real data you find online."

The more specific you are about structure, length, formatting, and content requirements, the less the agent has to guess and the fewer correction cycles you’ll need.

Provide Context Upfront

Agent Mode performs better when it understands the broader context. If you’re asking for a marketing plan, mention:

Your target audience demographics
Budget constraints
Timeline (Q2 launch vs. next year)
Competitors to avoid or differentiate from
Brand voice guidelines (formal vs. casual)

Uploading reference files helps enormously. Provide PDFs of previous reports, Excel templates with your preferred formatting, or screenshots of existing designs you want to match.

Use the Right Mode for the Job

Don’t use Agent Mode for simple Q&A. If you just need to know "what’s the capital of France," use Instant Mode. Agent Mode incurs higher latency and token costs because it’s planning tool use and potentially browsing the web unnecessarily.

Escalate to Swarm when parallelizable. If your task involves independent sub-tasks (researching 50 different things, processing 100 different files), explicitly request Agent Swarm mode or break the task into parallel components in your prompt. Otherwise, the agent will process sequentially, wasting time.

Reserve Thinking Mode for pure reasoning. When you need mathematical proofs, logic puzzles solved, or strategic frameworks analyzed without external data, Thinking Mode is faster and more cost-effective than Agent Mode.

Iterate Through Checkpoints

For complex deliverables, don’t wait for the final output. Request checkpoints:

"First, create a detailed outline of the report structure and share it with me for approval before proceeding to research and writing."

This prevents the agent from spending 20 minutes writing a full document in the wrong direction. Once you approve the outline, it can proceed with confidence.

Similarly, for code projects:

"Start by architecting the database schema and API endpoints. Show me the design before implementing the frontend."

Verify and Validate Outputs

Agent Mode is autonomous, not infallible. Implement these verification habits:

Check source recency: If the agent cites statistics, verify they’re from 2024-2026, not outdated data. Explicitly request "only use sources from the last 12 months" for rapidly changing topics.

Review calculations: For financial models or data analysis, spot-check a few calculations manually. The agent is good at math but can misinterpret table structures when extracting data from PDFs.

Test code thoroughly: While the agent can generate functional websites and scripts, test edge cases. It might miss input validation or error handling that production code requires.

Validate citations: Ensure quoted sources actually say what the agent claims. Occasionally, AI systems hallucinate URLs or misattribute quotes. Request "include verbatim quotes with page numbers" for critical research.

Manage Token Economics

Agent Mode uses significantly more tokens than chat mode due to tool calls, context maintenance, and output generation. For cost-effective usage:

Break large tasks into chunks: Instead of "write a 50-page report on global economics," chunk it by chapter. This prevents context window overflow and allows targeted revisions.
Reuse contexts strategically: If analyzing 20 companies, process them in batches of 5 rather than starting 20 separate conversations, so the agent retains methodological consistency.
Specify depth levels: Tell the agent whether you need exhaustive research (every source checked) or rapid synthesis (good enough for a draft). This controls the number of tool calls.

Leverage Multimodal Inputs

Don’t forget that K2.5 accepts images and video. When asking for website code, sketch the layout on paper and photograph it. When requesting analysis of a competitor’s product, screenshot their app. When documenting a bug, record a screen capture video.

Visual inputs often communicate requirements more efficiently than text descriptions, reducing ambiguity and revision cycles.

Use System Prompts for Consistency

If you’re building workflows or using the API, establish system prompts that define the agent’s persona and constraints:

"You are a senior financial analyst specializing in healthcare equities. Always cite SEC filings as primary sources. Format all monetary values in millions USD with two decimal places. Flag any material uncertainties in the data with [UNCERTAIN] tags."

This consistency ensures that whether you’re analyzing company #1 or company #100 in a swarm, the methodology and output format remain standardized.

FAQs

What’s the difference between Kimi Agent Mode and regular ChatGPT/Claude?

While GPT-4 and Claude offer agent-like capabilities through custom GPTs or projects, Kimi Agent Mode is natively built for autonomy rather than bolted on. Key differences include: native multimodal training (better visual-to-code), higher stability over long sessions (200-300 tool calls vs. degradation after 50-100 in some models), and the Agent Swarm capability for parallel execution. Additionally, Kimi K2.5 is open-source under a modified MIT license, allowing on-premise deployment for sensitive data.

Is Agent Mode available for free?

Kimi offers a freemium model. Casual users get limited Agent Mode requests (typically 3 free tasks) to test functionality. Paid tiers (Moderato at $19/month and Vivace at $199/month) provide higher or unlimited quotas. Agent Swarm mode is currently in beta and available primarily to high-tier paid users with free credits during the testing phase.

Can Agent Mode access my private data or internal systems?

Through the API and Kimi Code (the terminal/IDE integration), you can configure custom tools that connect to internal databases, company wikis, or private APIs. However, the web interface (kimi.com) operates in a sandboxed environment for security. For sensitive enterprise data, deploy the open-source model locally or use Moonshot’s enterprise API with VPC isolation.

How does Agent Swarm decide how many sub-agents to create?

The orchestrator layer analyzes task decomposition opportunities. If you ask it to research 50 companies, it recognizes the parallel structure and spins up agents accordingly (up to 100). For less obviously parallel tasks, it may default to sequential execution unless you explicitly prompt for parallelization: "Research these 20 topics simultaneously using sub-agents."

What file formats can Agent Mode create?

Currently supported outputs include: Word documents (.docx), Excel spreadsheets (.xlsx), PowerPoint presentations (.pptx), PDFs (with LaTeX support for academic formatting), HTML/CSS/JS (websites), Python/R scripts, Markdown files, CSV/JSON data files, and image assets (PNG/JPG via generation tools).

Can it browse the live web or is it limited to training data?

Agent Mode has live web browsing capabilities. It can search current information, navigate websites, fill forms, and extract data from dynamic pages. This makes it suitable for tasks requiring real-time data (stock prices, breaking news, current weather) rather than just training data snapshots.

What happens if the agent gets stuck or makes a mistake?

The system includes self-correction mechanisms. If a tool call fails (e.g., a website times out), it retries or seeks alternative sources. However, for logical errors or misinterpretations, you’ll need to provide corrective feedback. The checkpoint recommendation (approving outlines before full execution) minimizes the cost of major directional errors.

Is coding with Agent Mode secure?

When using the web interface, code executes in a sandboxed environment without access to your local file system. When using Kimi Code (the CLI/IDE extension), it operates with the permissions you grant it—similar to Copilot or Cursor. Review generated code before running it in production environments, especially regarding API keys, database connections, or network requests.

How does it handle copyrighted material?

The agent respects robots.txt and terms of service when browsing. For generated content, it aims to create original synthesis rather than copying source text verbatim. However, users should verify that outputs don’t inadvertently plagiarize source material, especially when requesting long-form content. Citation practices help mitigate this risk.

Can I schedule Agent Mode to run recurring tasks?

Currently, scheduled execution requires using the API with external orchestration (cron jobs, Zapier, Make.com). Moonshot has indicated that native scheduling features are on the roadmap for 2026. For now, you can automate workflows by building custom integrations that trigger Agent Mode via API calls at specified intervals.

Kimi Agent Mode represents a genuine evolution in AI utility from assistant to autonomous worker. By understanding when to deploy it, how to structure requests for optimal results, and which workflows benefit most from its capabilities, you can reclaim hours spent on repetitive research, data wrangling, and content formatting. The technology is particularly mature for knowledge workers, developers, and analysts who need to bridge the gap between raw information and polished deliverables.

Start with small experiments automate a weekly research summary or generate a presentation from your notes and scale up to complex multi-agent workflows as you learn the system’s strengths. The future of work isn’t AI replacing humans; it’s humans directing AI agents to handle execution while focusing on strategy, creativity, and decision-making.