AI Agents: Known Limitations and Workarounds

AI Agents are powerful, but there are some platform-level limitations to be aware of. Use the recommended workarounds below to ensure your Agent behaves consistently and reliably.

Conversation visibility

AI Agents have limited visibility into conversation metadata, history, and internal context.

Channel and source metadata

AI Agents cannot see channel-specific details, such as:

• Which channel the Contact is connected to

• Where the Contact came from (e.g., Ads vs Comments)

• Which TikTok Ad the Contact came from

This means AI Agents should not rely on channel or source metadata when making decisions.

Recommended workaround:

If your use case depends on knowing where a Contact came from, use a Workflow to capture that information and store it in a Contact field.

For example:

• Create a Workflow with a Conversation Opened trigger

• Add conditions based on channel or source (e.g. TikTok Ads)

• Update a Contact field such as Lead Source or Campaign Name

You can then instruct your AI Agent to reference that Contact field in its instructions.

This approach allows AI Agents to use source-related context reliably, even though they can’t access channel metadata directly.

Assignment history

AI Agents cannot view assignment history or past assignment actions, including whether another AI Agent assigned the conversation to them.

This means:

• If AI Agent 1 assigns a conversation to AI Agent 2, AI Agent 2 does not know it has just been assigned.

• AI Agent 2 cannot distinguish between messages sent by AI Agent 1 vs. messages sent by a human agent.

• As a result, AI Agent 2 may start responding without knowing the conversation context or may repeat actions unnecessarily.

Recommended workaround:

If you are chaining multiple AI Agents, include a clear handoff message that signals the next Agent to start.

Example: In AI Agent 1’s instructions

Before assigning the conversation to AI Agent 2, send a message: ‘I’m transferring you to AI Agent 2 who will help you next. Then assign to AI Agent 2.

Example: In AI Agent 2’s instructions

Only start your top-level flow when you see the message: ‘I’m transferring you to AI Agent 2 who will help you next.’ When you see it, greet the user and begin your qualification/support flow.

This ensures:

• AI Agent 2 knows the handoff happened

• AI Agent 2 starts at the right moment

• Users get a smooth transition between agents

Internal context and message history

AI Agents cannot see internal comments and can only access the most recent 20 messages in a conversation.

Because of this, AI Agents should not rely on channel origin, internal notes, or older conversation context when making decisions.

Recommended workaround:

Store important context in Contact Fields or Lifecycle stages, and instruct the AI Agent to reference those instead.

For example:

• Use Workflows or human processes to keep fields like Plan Type, Customer Status, or Issue Category up to date

• Use Lifecycle stages (e.g., New Lead, Qualified Lead, Customer) to guide how the Agent responds

• In your AI Agent instructions, explicitly reference these fields (e.g., “If Lifecycle is Customer, prioritize support-related responses.”)

This ensures the AI Agent can make consistent decisions using reliable, structured data, even with limited access to older messages or internal comments.

External capabilities

AI Agents currently operate within respond.io and do not have access to external tools. As such, they can’t perform web searches.

Any logic that requires external data or API calls should be handled through Workflows or Integrations instead.

Note: Support for HTTP requests is coming soon.

Message types and attachments

AI Agents can only send text messages and are currently unable to send files or media, such as:

• Files (e.g., PDFs, documents)

• Images

• Videos

• Voice notes

• Stickers or other rich media

This means AI Agents should not be used for processes that require sending attachments (e.g., brochures, onboarding documents, invoices, or screenshots).

Recommended workaround:

Use a Workflow step or assign the conversation to a human agent when the Contact requests files or media. You can also configure a Workflow to automatically send the required file (if supported by the channel) before returning control to the AI Agent.

Model configuration

AI Agents do not support manual temperature settings because newer AI models no longer expose temperature controls.

If you want to adjust the AI Agent’s “creativity” (similar to temperature), the best approach is to prompt for it directly in your Instructions. For example, you can guide the AI to behave more creatively or more strictly depending on your use case.

High creativity (higher “temperature”):

Respond warmly and creatively in the customer’s tone.

Low creativity (lower “temperature”):

Answer the customer’s question using only the provided company knowledge sources and the conversation history. Do not guess or add new information.

This gives you better control over tone and consistency without needing a dedicated temperature setting.