How to Build an AI Agent: Dify or In-House Development? – A Complete Guide to Choosing the Right Approach Without Failure

Want to implement AI agents, but not sure where to start?
Are you stuck at just using conversational AI tools like Copilot and Gemini?

This article is a practical guide to building business AI agents that helps DX and marketing leaders take action “starting today.” Through seven steps and clear selection criteria, it clarifies how to decide on adoption and take the first step.

Three barriers to business use of generative AI

Demand for generative AI and key challenges

Many companies are facing labor shortages and operational inefficiencies.
For example, in marketing operations, teams often say they cannot spend enough time on core work such as strategy planning. In sales, we frequently hear that complex internal processes reduce time available for customer visits.
To overcome these issues, many organizations are looking to improve efficiency by using generative AI.

General-purpose generative AI tools like ChatGPT and Gemini can be adopted quickly. However, they also present operational challenges.

Challenge 1: Cost

General-purpose generative AI can be used for free. However, serious business use typically requires paid plans due to limits on usage volume and available AI models. Most services are priced at around $20–$30 (as of February 2026).

For example, if a company with 1,000 employees buys licenses for everyone, the monthly cost can reach $20,000–$30,000 (about JPY 3–4.5 million), making cost a major concern. Given that not everyone will use the tools intensively, the cost-benefit burden can be substantial.

Challenge 2: Confidentiality

Information entered into generative AI is transmitted to the companies providing those tools. That data may be used for model training and could potentially appear in outputs in ways users did not expect. ChatGPT and Gemini allow settings to opt out of training use, but highly security-sensitive companies may still find adoption difficult.

Challenge 3: Additional effort required to use generative AI

When using general-purpose generative AI for business, extra work is often required—such as preparing input data and iterating multiple times with the AI—along with operational know-how and upfront setup.

For example, if an employee uses generative AI to find and review internal regulations, the workflow may look like this:

1. Find the relevant departmental regulations based on the inquiry.
2. Download the regulation PDF to a PC.
3. Open a generative AI tool such as ChatGPT or Gemini.
4. Attach the regulation document in the AI chat window and enter the question.
5. Receive the answer.

This is more efficient than manually reading through materials and finding relevant rules, but it still introduces overhead to use generative AI—so dramatic efficiency gains can be difficult.

What is an AI agent?

Definition of an AI agent

An AI agent is a system that autonomously performs tasks to achieve a given goal by understanding context and making decisions.
AI agents are increasingly being developed and applied across many domains, including software development automation and research process automation.

Similarities and differences between general-purpose AI chat and AI agents

The common ground between general-purpose AI chat and AI agents is that both use the generative AI “brain,” called a model, to surface knowledge and automate decision-making.

The key difference is how that “brain” is used.

General-purpose AI chat works in a Q&A format: users provide instructions and information to elicit answers. People must gather and sometimes preprocess information before submitting it to the AI. It requires iterative questioning and refinement to get quality outputs. While this flexibility supports deeper understanding, it also increases workload.

AI agents can execute complex workflows such as “if X happens, find Y and consider Z,” enabling fast, automated task completion. You can also have the model reference proprietary internal data or execute specific functions, such as checking today’s weather.

Key characteristics of AI agents

AI agents are defined by autonomy and intelligence. Autonomy is the ability to make decisions and perform tasks independently. Intelligence is the ability to understand, reason, and decide. The relative importance of autonomy vs. intelligence varies depending on the type of problem being solved.

When AI agents are viewed as automation systems, autonomy is prioritized to reduce human intervention. When intelligence is prioritized, the focus shifts to how human-like the behavior is.

How to build an AI agent: 7 steps

AI agent development can be advanced through the following seven steps.

Selecting development approaches and tools

Technology components that make up an AI agent

This section explains the AI agent architecture from a technical perspective.

An AI agent consists of five layers: user touchpoint, orchestration, knowledge, LLM model, and LLMOps. The role of each layer and typical implementation methods/tools are as follows.

• User touchpoint
  • The interface where users interact with the AI agent—submitting input and receiving results. Example: embedding an AI agent in chat channels such as LINE.
• Orchestration
  • The control tower that manages the agent’s overall behavior. For example, it receives user input, queries the LLM, and retrieves data from the knowledge layer. Dify is a notable orchestration tool.
• Knowledge
  • LLMs now have vast knowledge, often beyond human capacity, but they do not inherently contain proprietary internal data. The knowledge layer manages and provides that data. Common approaches include RAG (retrieving needed external information on demand) and long-context prompting (injecting information directly into prompts).
• LLM model
  • The brain of the AI agent, responsible for core functions such as understanding, reasoning, and decision-making. Without it, an AI agent cannot function. Widely used models include OpenAI (GPT), Gemini, and Claude.
• LLMOps
  • Ensures post-deployment quality management, including testing, monitoring, and cost/latency analysis. It tracks and debugs questions such as: “Is answer quality sufficient?”, “Is the model hallucinating?”, and “How much token cost was incurred?”

These components are also often referred to as an AI agent’s “tech stack.”

Comparison of three development approaches

There are three ways to combine the five technical layers into a complete AI agent.

1) LLM app development platforms (e.g., Dify)

For simple use cases, you can build AI agents in just minutes. Since no programming skills are required, business owners closest to operations can build them directly. Post-development operations are also easy to manage.

There are limitations—such as restricted UI flexibility and weaker support for extremely complex workflows—but the major strength is rapid prototyping and validation without coding skills.

• Best for
  • Teams that want to validate within 1–2 weeks whether AI agents can be used in their business, before full production deployment.

2) Full-scratch development (e.g., Python)

Fully customizable with maximum development freedom. You can also experiment with cutting-edge algorithms.

Requires programming skills. Because libraries update very frequently, maintenance burden in production can be significant.

• Best for
  • Teams that need deeply customized logic, UI, and optimization.

3) Cloud agents (e.g., Azure AI Agent)

Major cloud vendors such as Microsoft and Amazon provide capabilities like tool integration, monitoring integration, and ID/network integration, helping you build secure and stable systems. Cloud infrastructure knowledge is required.

• Best for
  • Enterprises in highly regulated sectors (finance, public sector, healthcare, etc.) that need very high security and want major cloud providers to assume operational responsibility in the cloud environment.

How to use the three approaches strategically

These three approaches are not an “ultimate choice” where one is always best. It is better to use them differently depending on your AI agent’s planning and development stage.

For example, following the steps below at each project stage can increase the probability of successful AI agent adoption:

• Use an LLM app development platform such as Dify to validate ideas at ultra-high speed and share “what is possible” internally.
• If specialized processing or advanced customization is required, combine full-scratch development.
• When moving to company-wide and production operation, migrate/integrate to cloud agents (e.g., Azure AI Agent) for stronger security and stability.

Comparison with “similar but different” tools

The term “AI agent” is sometimes used broadly for anything that uses AI in some form. In that context, products with very different target users and roles may also be labeled as AI agents. Here we briefly compare those “similar but different” tools with LLM app development platforms.

Integration automation tools such as n8n / Make / Zapier

These tools automate integrations across existing SaaS applications. Some include AI capabilities, but their primary role is cross-app connectivity and automation.

Customer support tools such as Intercom and Zendesk

Cloud-based customer support tools that help companies streamline and centralize customer interactions. They are optimized specifically for customer service use cases.

LLM app development platforms such as Dify

These are specialized for generative AI utilization and positioned as foundational platforms for building a wide range of applications.

Comparison of LLM app development platform tools

There are multiple LLM app development platforms. Here are the major ones.

Coze

You can quickly build AI chatbots with no code. It supports memory in user conversations. Operated by ByteDance (TikTok’s parent company), it offers smooth SNS integration. It also provides a rich plugin ecosystem (external service integrations).

Dify <Cloud Edition>

With no code, you can quickly build applications powered by generative AI (LLMs). It is especially strong in easy RAG setup (data retrieval).
Dify can be used for free. However, the free plan limits the number of builder/operator users and the number of apps you can create, so serious use typically requires a paid plan.
Please note that the chat UI (e.g., layout) is largely fixed, and the terms of use may restrict a single administrator from providing services across multiple tenants.

Dify <Self-Hosted Edition>

The self-hosted edition allows you to keep data within your own hosting environment. This helps avoid risks such as unexpected external access to built applications and leakage of internal knowledge data. Operational management (including updates) must be handled by the user.
There is no software license fee. However, for large-scale commercial use requiring advanced management features, a paid Enterprise edition may be needed.
Aside from that, especially in app functionality, it is broadly similar to the cloud edition. (Operational responsibility, security governance, support, and cost structure differ between cloud and self-hosted.)

Flowise / LangFlow

Flowise / LangFlow provide a GUI (graphical user interface) for operating the “LangChain” programming library. Their key advantage is high development flexibility. They are tools for building processing logic; the end-user chat interface must be prepared separately.

Key characteristics of Dify

If your goal is to build no-code business applications with embedded AI in a short period, Dify is the most widely adopted de facto standard open-source LLM app development platform globally. It is a very attractive option when you want to build workflows through iterative trial and error or experiment with RAG. Since both cloud and self-hosted editions are available, you can prototype in the cloud first and migrate to self-hosted for full-scale deployment.

Especially for early-stage validation (PoC) of whether an AI app can meet business requirements, Dify is an excellent choice because people closest to the operational pain points can quickly turn their ideas into working implementations.

Summary

AI agents can be a powerful way to solve labor shortages and operational inefficiencies.

The key, however, is not to make “introducing AI agents” the goal itself. We recommend making decisions—including whether to adopt—based on the seven-step framework above.

The key to success is not aiming for perfection from day one, but taking the first step by visualizing familiar, real-world issues. With no-code tools like Dify, that first step can be taken faster and at lower cost than you might expect.