Here’s the five most significant challenges people face when implementing the AI Customer Agent, along with practical solutions:
1. Unclear Intent Setting and Role Definition
The Problem:
Without clear parameters, the Customer Agent tries to be everything to everyone, resulting in diluted effectiveness and confusing customer experiences.
Solution:
- Define a specific primary role (sales qualification, support triage, etc.) rather than trying to cover all bases
- Create a written 'job description' for your agent before configuring it i.e. know it's purpose very clearly
Note: It wouldn't surprise me if HubSpot released the availability of multiple Customer Agents before long i.e. meaning that you could have a different set of knowledge and purpose for each one.
2. Lack of preparation
The Problem:
Many users upload content without proper organisation, resulting in an agent that provides inaccurate or incomplete answers. The agent is only as good as the information it can access.
- Develop a list of 25-50 common questions it should be prepared to answer
- Don't just rely on using the information from your website
- Look at using the FAQ and short answer aspects of HubSpot as a way to control the information.
Note: "When working with the customer agent, it's recommended that you use content sources that... Use clear headers and subheaders to break down content into sections" - source
3. Poorly Organised Information Sources
The Problem:
Poor labelling of the information will make it very hard to understand 'which' content has 'what' information- making it hard to manage changes.
Solution:
- Create a spreadsheet that clearly explains what information is being used for your customer agent
- With this, have a systematic content architecture with clearly labelled categories (pricing, support, product info, etc.)
- Use descriptive file names that reflect the content (e.g., "2025_Pricing_Structure.pdf" instead of "Doc1.pdf")
- Include both comprehensive documents AND concise FAQ-style content for different query types
Note: regularly audit your knowledge base for outdated information
4. Poor handoff protocol
The Problem:
Sometimes a human is going to be the best option. The question is, when do you move from the agent to an individual on your team?
- Establish clear handoff protocols for when enquiries should transfer to human agents
- Then make sure that you test what this experience is like for the user.
5. Citation and Privacy Management Issues
The Problem:
Many users either expose too much internal information through citations or disable citations entirely, reducing the agent's credibility and usefulness.
i.e. you can see the source of the information being given if that setting is on.
Solution:
- Create public-facing versions of internal documents specifically for agent training
- Use the 'disable source citations' setting selectively rather than globally
- Implement a review process for all knowledge base content before uploading
- Create a separate knowledge base folder for sensitive but necessary information
- For proprietary information, use Short Answers feature instead of document uploads to maintain control
6. Inconsistent Brand Voice and Customer Experience
The Problem:
The Customer Agent often feels disconnected from the company's communication style, creating a jarring experience for customers who interact with both the AI and human representatives.
Solution:
- Include brand voice guidelines and examples in your knowledge base or FAQs
- Use the Short Answers feature to craft responses for common questions in your exact brand voice
- Add context documents that specifically describe your company's tone, terminology preferences, and communication style
- Configure multiple welcome messages that reflect different customer journey stages
- Test the agent with various team members to ensure consistent experience
7. Inadequate Testing and Refinement Process
The Problem:
Implementation may fail because organisations don't establish a systematic testing and improvement workflow, missing critical knowledge gaps and misalignments.
Solution:
- Review conversations weekly to refine the agent's purpose based on actual customer needs
- Dedicate time weekly (or daily, to begin with) to review the Knowledge Gaps report
- Create a cross-functional 'agent training team' with members from sales, marketing, and service
- Develop testing scripts that simulate different customer personas and enquiry types
- Implement A/B testing by creating variant agents with different knowledge bases
- Establish metrics to measure effectiveness (resolution rate, conversation length, handoff frequency)
- Use the 'train model' feature to correct misunderstandings in real-time
Bonus Tip: Managing Expectations
Remember that the AI Customer Agent is designed to complement your team, not replace it. The most successful implementations position the agent as a first-line assistant that can handle routine enquiries whilst escalating complex issues to the appropriate human team members. Clear internal communication about the agent's capabilities and limitations is essential for adoption and ongoing improvement.
Here’s the five most significant challenges people face when implementing the AI Customer Agent, along with practical solutions:
1. Unclear Intent Setting and Role Definition
The Problem:
Without clear parameters, the Customer Agent tries to be everything to everyone, resulting in diluted effectiveness and confusing customer experiences.
Solution:
- Define a specific primary role (sales qualification, support triage, etc.) rather than trying to cover all bases
- Create a written 'job description' for your agent before configuring it i.e. know it's purpose very clearly
Note: It wouldn't surprise me if HubSpot released the availability of multiple Customer Agents before long i.e. meaning that you could have a different set of knowledge and purpose for each one.
2. Lack of preparation
The Problem:
Many users upload content without proper organisation, resulting in an agent that provides inaccurate or incomplete answers. The agent is only as good as the information it can access.
- Develop a list of 25-50 common questions it should be prepared to answer
- Don't just rely on using the information from your website
- Look at using the FAQ and short answer aspects of HubSpot as a way to control the information.
Note: "When working with the customer agent, it's recommended that you use content sources that... Use clear headers and subheaders to break down content into sections" - source
3. Poorly Organised Information Sources
The Problem:
Poor labelling of the information will make it very hard to understand 'which' content has 'what' information- making it hard to manage changes.
Solution:
- Create a spreadsheet that clearly explains what information is being used for your customer agent
- With this, have a systematic content architecture with clearly labelled categories (pricing, support, product info, etc.)
- Use descriptive file names that reflect the content (e.g., "2025_Pricing_Structure.pdf" instead of "Doc1.pdf")
- Include both comprehensive documents AND concise FAQ-style content for different query types
Note: regularly audit your knowledge base for outdated information
4. Poor handoff protocol
The Problem:
Sometimes a human is going to be the best option. The question is, when do you move from the agent to an individual on your team?
- Establish clear handoff protocols for when enquiries should transfer to human agents
- Then make sure that you test what this experience is like for the user.
5. Citation and Privacy Management Issues
The Problem:
Many users either expose too much internal information through citations or disable citations entirely, reducing the agent's credibility and usefulness.
i.e. you can see the source of the information being given if that setting is on.
Solution:
- Create public-facing versions of internal documents specifically for agent training
- Use the 'disable source citations' setting selectively rather than globally
- Implement a review process for all knowledge base content before uploading
- Create a separate knowledge base folder for sensitive but necessary information
- For proprietary information, use Short Answers feature instead of document uploads to maintain control
6. Inconsistent Brand Voice and Customer Experience
The Problem:
The Customer Agent often feels disconnected from the company's communication style, creating a jarring experience for customers who interact with both the AI and human representatives.
Solution:
- Include brand voice guidelines and examples in your knowledge base or FAQs
- Use the Short Answers feature to craft responses for common questions in your exact brand voice
- Add context documents that specifically describe your company's tone, terminology preferences, and communication style
- Configure multiple welcome messages that reflect different customer journey stages
- Test the agent with various team members to ensure consistent experience
7. Inadequate Testing and Refinement Process
The Problem:
Implementation may fail because organisations don't establish a systematic testing and improvement workflow, missing critical knowledge gaps and misalignments.
Solution:
- Review conversations weekly to refine the agent's purpose based on actual customer needs
- Dedicate time weekly (or daily, to begin with) to review the Knowledge Gaps report
- Create a cross-functional 'agent training team' with members from sales, marketing, and service
- Develop testing scripts that simulate different customer personas and enquiry types
- Implement A/B testing by creating variant agents with different knowledge bases
- Establish metrics to measure effectiveness (resolution rate, conversation length, handoff frequency)
- Use the 'train model' feature to correct misunderstandings in real-time
Bonus Tip: Managing Expectations
Remember that the AI Customer Agent is designed to complement your team, not replace it. The most successful implementations position the agent as a first-line assistant that can handle routine enquiries whilst escalating complex issues to the appropriate human team members. Clear internal communication about the agent's capabilities and limitations is essential for adoption and ongoing improvement.
