AI Agents & Workflow Automation
Beyond answering — AI that does the work.
Finding materials, analyzing content, writing documents, entering results into systems. In many companies, work is still done by people repeatedly moving between multiple programs.
AI agents connect AI and tools to these work processes, building an automation environment that collects needed information, performs tasks, and verifies results.
Beyond a simple chatbot, we connect AI to your company’s workflows and existing systems. We automate repetitive work, handle complex tasks step by step, and design it so people participate in important judgments and approvals.
01. Repetitive work — now handle it with AI.
Company work involves many repetitive tasks. Collecting materials, comparing documents, organizing data, and writing reports in defined formats are typical examples.
Even if each task is not hard, repeating multiple steps requires considerable time and effort.
AI agents and workflow automation are a way to reduce these repetitive tasks and make work flows more efficient.
By defining work goals and handling rules and connecting tools and systems AI can use, you can compose everything from material search to analysis, result writing, and follow-up tasks into a single flow.
Rather than simply replacing people with AI, we support reducing the time people spend on repetitive tasks so they can focus on more important judgments and decisions.
02. What is an AI agent?
A typical AI chatbot focuses on answering the user’s questions or summarizing and generating input content.
An AI agent connects work tools and execution capabilities to this. It can be designed to check needed information, select tools to use, and perform the next step based on task results to achieve a given goal.
For example, consider work like this:
Depending on configuration, an AI agent can query incident records, search related materials, analyze key content, and then write a report draft in a defined format. Saving the report to a designated location or delivering it to the owner can also be automated if the needed tools and permissions are provided.
Typical AI Chatbot
Answers questions, summarizes information, and assists the user’s tasks.
AI Agent
Based on work goals, it can use multiple tools, perform step-by-step tasks, and connect results to the next task.
Workflow Automation System
Executes repetitive tasks according to defined rules and procedures. Combined with AI, it can also support tasks that were hard to handle with rule-based automation alone, such as unstructured document analysis and natural language understanding.
Using AI agents and workflow automation together creates a work environment combining the stability of defined procedures with the flexibility of AI.
03. How AI agents work.
Confirm Work Goal
Based on the user’s request or pre-defined conditions, confirm the tasks to perform and completion criteria.
Collect Needed Information
Secure information needed for the task through internal documents, databases, business systems, RAG search, and more.
Task Planning & Tool Selection
Decide the steps and tools needed for the work. Depending on task complexity, it can follow pre-defined procedures or let the AI select the next task.
Task Execution
Perform actual tasks using authorized APIs, data query functions, document processing tools, and more.
Result Verification & Follow-up
Verify task results and, if needed, perform additional tasks or report errors. Important tasks can be configured to require human confirmation and approval.
Result Logging & Reporting
Record the performed tasks and results, and deliver them in needed forms such as summary reports or notifications.
Through this flow, tasks previously divided among multiple programs and people can be composed into a single connected work process.
04. AI Workflow Automation & Setup Services
AI Adoption Consulting & Work Analysis
You don’t need to automate all work with AI. We analyze repetition frequency, processing time, error likelihood, and data access conditions to select the work with the greatest automation effect first.
- Existing workflow analysis
- Identification of repetitive tasks & bottlenecks
- Selection of AI-applicable work
- Review of automation scope & expected effects
- Separation of steps requiring human judgment
- Phased setup plan & prioritization
We design the automation direction centered on real work improvement effects, not just adopting AI itself.
AI Agent Design & Development
We design the tools and execution flow AI will use, aligned with work goals and handling procedures.
- Per-work AI agent configuration
- Task planning & execution flow design
- Building a task system connecting multiple tools
- AI model & prompt configuration
- Step-by-step result verification & error handling
- Repeated execution & exception handling
- Implementing task logging & result reporting
You can configure a single agent to perform multiple tasks, or combine multiple agents with separated roles or defined workflows according to work characteristics.
Existing System & Data Integration
For AI to be used in real work, it must be able to connect to systems your company already uses.
- Existing system API integration (REST API, etc.)
- Database querying & needed data processing
- Internal document & RAG search system integration
- Business program & internal service connection
- Result storage & notification delivery automation
- MCP-based AI tool connection
- Per-system authentication & access permission design
We review adding AI features according to provided interfaces and security policies, without fully replacing existing systems.
Document & Data Processing Automation
We connect repetitive work centered on documents and data with AI and automation tools.
- Document classification & key item extraction
- Comparison & summarization of multiple documents
- Drafting reports & work documents
- Structured data transformation & result organization
- Work material search & integration
- Document generation in defined formats
- Result review & owner delivery
Parts with clear work rules are handled with general program logic, while parts needing content understanding and analysis apply AI, considering both efficiency and controllability.
AI-Based Analysis & Decision Support
We use AI for tasks that analyze multiple materials and organize results.
- Summarization of work data & records
- Analysis of commonalities & differences across materials
- Support for identifying anomalies & cautions
- Organizing related evidence & reference materials
- Report writing based on analysis results
- Providing judgment materials for owner review
Analysis results presented by AI can vary by data quality and model characteristics, so important decisions can apply evidence verification and owner review procedures.
Local AI-Based Workflow Automation
We use local or on-premises AI for workflow automation, aligned with your security policy and operating environment.
- Self-operated LLM & agent integration
- Internal RAG & database connection
- AI workflow execution on internal networks
- Separating processing paths by external API usage
- Controlling execution tools & data access permissions
- Operation logging & error monitoring
Using self-operated models lets you design a structure that reduces dependence on external AI services. However, the actual level of data protection is also affected by connected tools, networks, storage, and permission policies, not just model execution location.
05. Applicable to various work tasks.
Report Writing Automation
Query data from multiple systems, search related documents, organize key metrics and issues, then generate weekly or monthly report drafts.
Internal Information Collection & Organization
Find work materials and related documents, extract needed items, and organize them in defined formats.
Customer Inquiry & Work Support
Classify customer inquiries, search related manuals and past handling cases, and draft answers or deliver them to the responsible department.
Development & Technical Work Support
Search code and technical documents, assist error analysis, and organize materials needed for development tasks. Actual code changes or deployment can apply separate review and approval procedures.
Data Query & Processing Automation
Query needed information from business systems and databases, organize data matching conditions, and store results or deliver them to owners.
Security Operation Automation
Analyze system logs, search related security rules and past cases, and present threat summaries and response plans. Depending on the environment, it can be configured to execute follow-up actions within approved scopes.
Internal Workflow Connection
Connect steps previously done by moving between multiple programs — material collection, review, document writing, result delivery — into a single automation flow.
06. We connect existing programs with AI.
The scope of AI agent use is not determined by the model’s features alone. It varies by what data and tools it can access and what tasks it can execute.
API-Based Integration
Use APIs provided by existing programs to query data or request tasks, and receive and process results.
MCP-Based Tool Integration
Using MCP (Model Context Protocol), you can connect tools and data sources the AI model can use in a standardized way. Actually usable tools and permissions must be configured separately.
Database Integration
Connect database access functions to query needed information and organize results. Data modification tasks are separated from read tasks, applying needed permissions and approval procedures.
RAG-Based Knowledge Integration
Search internal documents and work knowledge so AI agents can reference information needed for tasks.
Workflow-Based Automation
Combine AI’s document understanding, classification, and analysis capabilities with workflows that perform tasks in defined sequences and conditions.
Combining these technologies to fit your work lets you gradually expand AI-based features while using existing systems.
07. We control AI’s execution permissions and tasks.
If AI can operate real systems or modify data, you must design execution permissions and safety mechanisms alongside automation features.
AI agent permissions are limited to the needed work scope, and high-risk tasks can apply separate verification and approval procedures.
Least Privilege
Grant each agent and tool only the minimum access permissions needed. Separate query and data modification permissions, and apply separate controls for sensitive tasks.
Pre-Execution Verification
Confirm task targets and parameters, and block requests beyond allowed scopes or unexpected inputs.
Human Approval
High-impact tasks such as data deletion, external transfer, and system configuration changes can be designed to execute only after owner confirmation and approval.
Task Logging & Tracking
Record request content, execution tools, task results, and errors so you can identify and respond to causes when problems occur.
Error & Exception Handling
When tool execution fails, data is missing, or unexpected results occur, it is configured to connect to retry, stop, or owner confirmation rather than always proceeding to the next task.
Step-by-Step Verification & Testing
Before real operation, verify normal and exception situations in a test environment and confirm the scope and constraints of executable tasks.
We do not assume AI’s judgment is always correct. Designing automation scope and control level together to create a trustworthy work environment is important.
08. Setup Methods for Your Environment
Single Task Automation
Start with a specific task such as report writing, document classification, or material collection. The scope is clear, making it easy to confirm and improve effects.
Existing Business System Integration
Connect internal programs and APIs already in use so employees can use AI features within their existing work environment.
RAG-Linked AI Agent
Connect the internal knowledge search system with an AI agent to find needed information and use its results in analysis or follow-up work.
Multi-Stage Workflow Automation
Connect work made of multiple stages — material collection, analysis, verification, document writing, and result delivery — into a single flow.
Local AI-Based Automation
Connect self-operated AI models with internal systems to build an execution environment that fits your data policy and security requirements.
Multi-Agent & Specialized Tool Combination
For complex work, you can combine role-based agents, specialized analysis tools, and defined workflows. However, as components increase, execution cost, latency, and error handling complexity also grow, so we design from the needed scope.
09. Setup Process
Work Analysis
Analyze current work flows, repetitive tasks, systems in use, and data access conditions.
Automation Goal & Scope Definition
Separate tasks AI performs from judgments people handle, and define completion criteria and exception situations.
System & Tool Design
Select a suitable AI model and design needed APIs, databases, RAG, MCP tools, and workflows.
Development & Integration
Implement the AI agent and automation logic and connect it to existing business systems.
Verification & Safety Mechanisms
Test not only normal situations but also errors, permission limits, unexpected inputs, and risky tasks, and apply needed approval procedures.
Operation & Improvement
Monitor task results and errors, and improve automation scope and processing quality based on real usage results.
10. Frequently Asked Questions
A typical chatbot focuses on answering questions or generating content. An AI agent can be configured to perform multi-step tasks by connecting tools and systems. However, the actual execution scope is determined by connected tools, permissions, and work flows.
Reduce repetitive work,
focus on what matters.
The value of AI doesn’t end with simply answering questions. Connecting your company’s data and systems and using them aligned with real work flows creates greater possibilities.
From work analysis to AI agent development, existing system integration, execution permission control, and operation — we propose an AI workflow automation plan that fits your environment and goals.