How is an AI Agent different from a chatbot?

Chatbots mainly answer questions. An AI Agent breaks goals into steps and calls business systems such as CRM and ERP to complete actions, while leaving auditable records.

Can it be deployed privately?

Yes. Models, knowledge bases, and applications can be deployed on the enterprise's intranet or private cloud, with data staying within the domain.

How does it work with existing CRM and ERP?

The Agent reads from and writes back to existing systems through tool calls, without replacing your CRM or ERP. Sales assistants, approvals, and reports all plug into your existing workflows.

How do we move from requirements to launch?

First, clarify the scenario and conduct prototype validation. After confirming the results, proceed with development integration, launch, and operations/maintenance iterations. Once the scope is confirmed, milestones will be provided.

Enterprise AI Agent Development and Implementation

Use Cases ·

These scenarios are suitable for Agent implementation

From external reception to internal documents, the Agent connects to existing systems to perform verifiable actions.

Intelligent Customer Service

Multi-channel reception and transfer to human agents

Automated FAQ Responses
Escalate complex issues to human agents with context
Consolidate multi-channel conversations under the same customer
Preserve full conversation history before transfer

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Internal knowledge base Q&A (RAG)

Retrieve policies and documents by permissions

Answers include source citations
Apply permission filtering at the retrieval layer first
Return current policies by document version
Clearly state when no match is found instead of making things up

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Sales/CRM assistant

Follow up with customers and write back to CRM

Summarize customer and follow-up records
Write next actions back to existing CRM
Prepare customer background and objections before meetings
Sync follow-up results to the owner

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Approval and document automation

Verify rules and advance documents

Read request forms and check against rules
Return for supplementation when materials are missing
Advance eligible documents to the next step
Keep a traceable record for every decision

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Data Reporting Assistant

Query and explain data in natural language

Query business data based on permissions
Explain metric changes in business language
Keep definitions consistent with existing reports
Flag abnormal fluctuations and provide possible causes

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Capabilities

Connect models to business systems

Model selection, knowledge base, tool calling, deployment, and auditing are delivered together, instead of leaving just a chat box.

LLM selection and integration

Select models by scenario, with unified integration, quotas, and switching.

RAG knowledge base

Chunking, retrieval, citation, and evaluation make answers verifiable.

Tool calling and system integration (CRM/ERP/OA)

Encapsulate query, create, and update operations into authorizable tools.

Private deployment

Support for intranet and private cloud, keeping models and data within the enterprise environment.

Permissions and auditing

Reuse existing identity systems, logging every tool call and its results.

Delivery process

From requirements analysis to operations iteration, every step has verifiable outputs.

01
Requirements analysis
Confirm scenarios, data sources, permission boundaries, and success criteria.
02
Prototype validation (PoC)
Use real samples to validate retrieval, tool invocation, and answer quality.
03
Development integration
Integrate CRM, ERP, and OA, and complete permissions, auditing, and exception handling.
04
Launch
Complete deployment, integration testing, training, and cutover.
05
Operations and iteration
Continuously optimize knowledge and tools based on invocation logs and business feedback.

Differences between Agent and RPA

Both can reduce repetitive operations. Agent makes decisions based on goals, while RPA executes fixed scripts.

Differences between Agent and RPA
Differences between Agent and RPA	AI Agent	RPA
How it works	Understands the goal and independently breaks down steps and calls tools	Executes step by step according to pre-recorded scripts
When changes occur	Can handle differently worded questions and semi-structured information	Easily breaks when the interface or rules change
Relationship with systems	Connects to CRM, ERP, and OA through APIs and tool calls	Mostly simulates clicks and data entry on the UI
Better suited for	Knowledge Q&A, cross-system collaboration, and processes requiring judgment	Repetitive operations with stable rules and fixed steps

System integration, knowledge base practices, and projects already implemented.

Frequently Asked Questions

Chatbots mainly answer questions. AI Agents break down steps based on goals and call business systems such as CRM and ERP to complete actions, while leaving auditable records.

Have more questions? Contact us

Ready to embed AI Agents into business workflows?

Tell us about your scenario and existing systems, and our consultants will arrange the integration.

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Enterprise AI Agent Development