Turn Your Website AI Chatbot Into an AI Agent

For the last couple of years, nearly every software product has added an AI chatbot. Some answer questions surprisingly well. Others summarize documents, search knowledge bases, or generate content. But despite the marketing, most of these systems have one thing in common:

They don't actually do anything. They answer.

An AI agent, on the other hand, performs work.

That distinction is becoming increasingly important for enterprise websites. Visitors don't simply want answers. They want outcomes.

• Schedule a demo
• Download gated content
• Contact support
• Create a ticket
• Request pricing
• Register for an event
• Find the correct product
• Submit a warranty claim

Answering these requests is only half the problem. Completing them is what delivers business value.

The missing ingredient is Actions.

From Conversation to Action

Traditional website chatbots typically follow a simple pattern.

User
 ↓
Question
 ↓
LLM
 ↓
Answer

Useful? Absolutely.

Agentic? Not really.

An AI agent introduces another capability.

User
 ↓
LLM
 ↓
Reasoning
 ↓
Action
 ↓
Business System
 ↓
Result

Instead of simply replying: "You can request a demo on our Contact page."

the agent can say: "I'd be happy to arrange that."

…and immediately present a booking form, create a CRM lead, notify sales, and schedule a follow-up.

The AI has moved beyond conversation into execution.

What Exactly Is an AI Action?

An Action is a capability the AI can invoke while carrying on a conversation.

Think of Actions as tools available to the language model.

Examples include:

• Lead capture
• Human escalation
• Creating support tickets
• Booking appointments
• Product configuration
• Retrieving personalized account information
• Searching enterprise systems
• Generating quotes
• Initiating workflows
• Calling REST APIs
• Executing business logic

Instead of hard-coding every conversation path, the LLM decides when each Action should be used.

This is where modern AI frameworks like Spring AI have fundamentally changed application architecture.

Spring AI Makes Actions First-Class Citizens

One of the strengths of Spring AI is its support for tool calling (also referred to as function calling).

Developers expose ordinary Spring beans as callable tools.

During a conversation, the model determines:

• whether an Action should be invoked
• which Action is appropriate
• what parameters should be passed
• how the result should influence the ongoing conversation

This creates a clean separation between intelligence and business logic. The LLM handles reasoning. Your application handles execution.

For enterprise developers already building with Spring Boot, this feels remarkably natural.

Why Content Still Matters

A website AI agent cannot make good decisions without context.

This is where a modern CMS becomes much more than a publishing platform.

CrafterCMS supports multiple approaches to Retrieval-Augmented Generation (RAG). Authoring assistants can retrieve content directly from the Git-based content repository, while website AI agents typically index the published site by crawling rendered pages or reading the delivery tier's XML content through Spring AI. OpenSearch then provides semantic retrieval over that indexed content, giving the LLM the context it needs to determine whether an Action should be executed.

For example:

Site visitor: "We're evaluating your platform for a partner portal."

The agent retrieves architecture documentation, pricing guidance, security information, and relevant case studies.

Only after understanding the visitor's intent does it decide to:

• answer the question
• recommend documentation
• collect contact information
• route the visitor directly to enterprise sales

The Action is informed by enterprise content rather than operating in isolation.

OpenSearch Provides More Than Search

OpenSearch is often discussed in the context of vector databases, but its role in an agent architecture is much broader.

It provides:

• semantic retrieval for RAG
• hybrid keyword and vector search
• conversation memory
• relevance ranking
• analytics
• scalable indexing of enterprise content

An Action should rarely operate on the raw prompt alone.

Instead, the workflow becomes:

Question
    ↓
Retrieve relevant content
    ↓
Retrieve conversation memory
    ↓
LLM reasoning
    ↓
Select Action
    ↓
Execute
    ↓
Generate response

OpenSearch becomes the knowledge layer that enables better reasoning before execution.

Building Agentic Experiences with CrafterCMS

Because CrafterCMS is built on Java/Spring and integrates naturally with Spring AI and OpenSearch, developers can compose sophisticated AI agents directly into digital experiences.

Examples include:

• Marketing sites that qualify leads before creating CRM opportunities
• Documentation portals that open support tickets only when documentation doesn't resolve the issue
• Brand portals that guide partners to the correct assets while enforcing permissions
• Developer portals that generate code samples and launch workflows
• E-commerce experiences that recommend products, configure bundles, and initiate checkout

Rather than embedding a generic chatbot, organizations can build AI capabilities that are deeply integrated with their own content model, business services, and enterprise workflows.

This is a significant architectural shift from treating AI as a standalone widget to treating it as another application layer within the digital experience.

Designing Good Actions

One common mistake is attempting to create dozens of narrowly scoped Actions.

In practice, fewer, well-designed Actions tend to produce better results.

Good Actions should:

• accomplish meaningful business outcomes
• have clear input parameters
• return structured results
• fail gracefully
• expose only the permissions they require
• be understandable by the language model through concise descriptions

Just as APIs benefit from thoughtful design, so do AI Actions.

The quality of the Action interface often determines the quality of the agent.

The Future of Enterprise Websites

The next generation of enterprise websites won't simply answer questions. They will complete work.

Visitors won't navigate complex menus searching for forms. They'll describe what they want, and the website will orchestrate the necessary content, APIs, and workflows to deliver the result.

Actions are the bridge between language understanding and business execution.

Combined with Spring AI's tool-calling capabilities, OpenSearch's retrieval and memory features, and CrafterCMS's structured content platform, they enable developers to build true AI agents rather than conversational demos.

For teams that prefer a ready-to-use solution, CrafterQ packages this architecture out of the box with configurable AI Actions such as lead capture and human escalation, allowing organizations to deploy agentic website experiences without building the underlying infrastructure themselves. For developers who want complete control, however, the same architectural patterns can be implemented directly using CrafterCMS, Spring AI, and OpenSearch.

As AI continues to evolve, the distinction between a chatbot and an agent will become increasingly clear.

One answers questions. The other gets work done.

Learn More

Discover the future of websites by reading our recent blog post: The Future of Web Experiences