Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.subverseai.com/llms.txt

Use this file to discover all available pages before exploring further.

A Background Agent is a separate, independently configured agent that handles heavy or complex tasks on behalf of the main communication agent. The main agent — typically a small, fast model — stays focused on the conversation, while background agents running large, capable models do the heavy lifting in parallel.

​
How It Works

Customer-facing agents need to be fast and responsive. But some tasks — analysing an image, generating a PDF, processing a video, or running complex reasoning — are better suited to large, powerful models like Claude Sonnet or Gemini 2.5 Pro. Running those tasks inline would slow down the entire conversation. Background Agents solve this by separating concerns:
  • The main agent uses a lightweight, low-latency model to talk directly to the customer.
  • Background agents use large, capable models to execute complex tasks in parallel.
  • The main agent delegates tasks to background agents based on instructions in its prompt, then communicates the outcome back to the customer once available.
You can attach multiple background agents to a single main agent — each specialised for a different kind of task. Example architecture: 
Customer
   ↕
Main Agent (fast model — e.g. GPT-4o mini)
   ├── Background Agent A: Image Analysis (Claude Sonnet)
   ├── Background Agent B: Document Generation (Gemini 2.5 Pro)
   └── Background Agent C: Video Processing (Claude Sonnet)
The main agent routes the task to the right background agent based on its prompt. The customer receives a holding response while the background agent works, and the result is returned once ready.

​
Use Cases

TaskBackground Agent Role
Image analysisAnalyse an uploaded image and return a structured summary
PDF / document creationGenerate formatted documents from conversation data
Video processingTranscribe, summarise, or extract data from a video file
Complex data reasoningRun multi-step analysis that would exceed a small model’s capability
Long-context tasksProcess large documents or datasets without impacting conversation latency

​
Configuring a Background Agent

  1. Navigate to Integrations → Agentic Functions and select your agent.
  2. Choose the phase where you want to add the background agent (Pre-Session, During-Session, or Post-Session).
  3. Click + Add Function and choose Background Agent.
  4. Select the Target Background Agent from your workspace — this is a fully configured agent with its own model, prompt, and tools.
  5. Map any input variables that should be passed to the background agent.
  6. Save the function.
In your main agent’s system prompt, explicitly define when and how to delegate to each background agent. For example: “When the customer uploads an image for analysis, delegate the task to the Image Analysis agent and tell the customer you are processing it.”

​
Availability

PhaseAvailable
Pre-SessionYes
During-SessionYes
Post-SessionYes

​
Pre-Session

Trigger a background agent before the session starts — for example, use a powerful model to enrich or analyse customer data before the conversation begins, then pass the results back as dynamic variables.

​
During-Session

The main agent delegates tasks mid-conversation. The customer receives an acknowledgement while the background agent processes the request, keeping conversation latency low regardless of the complexity of the underlying task.

​
Post-Session

Trigger a background agent after the session ends to act on the full session analysis — for example, generating a detailed report, creating a follow-up document, or running a post-call analysis with a large model.

​
Next Steps

Custom Functions

Call your own API with session or LLM-extracted data

Agent Handoff

Route the active conversation to another agent