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QuivaWorks supports two types of multi-agent behaviour: assistant-to-assistant communication, where you explicitly link specialists together, and automatic sub-agents, where QuivaWorks handles tool-heavy or large-document tasks behind the scenes.

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Assistant-to-Assistant Communication

Any assistants in your account can be linked together. This lets one assistant delegate work to another — enabling you to build systems where a coordinator routes tasks to the right specialist.

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How It Works

When assistants are linked, the primary assistant can call another assistant as a tool. The called assistant receives the request, performs its task using its own instructions, knowledge, and integrations, and returns the result. Each assistant in a multi-agent system can have:
  • Different instructions and expertise
  • Different knowledge sources
  • Different integrations and tool access
  • Different permission scopes
  1. Open the assistant you want to use as the coordinator (the primary assistant that delegates work)
  2. Navigate to the Integrations tab
  3. Under Assistants, enable the specialists you want this assistant to be able to call
  4. In the coordinator’s instructions, describe when and how to delegate to each specialist
Example instruction for a coordinator: 
You are a research coordinator. When a user asks a question:

- For questions about our product features, delegate to the Product Expert assistant
- For questions about competitors, delegate to the Market Research assistant  
- For general questions you can answer directly, respond yourself

Always synthesise the responses from specialists into a clear, unified answer.

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When to Use This Pattern

A coordinator receives requests and routes them to the right expert based on topic, intent, or complexity. Each specialist has deep knowledge in its domain.Example: A customer-facing assistant delegates product questions to a product expert, billing questions to a finance assistant, and technical questions to a support engineer assistant.
One assistant processes output from another in a pipeline. The first extracts or transforms data; the second makes decisions or generates the final output.Example: A data extraction assistant parses incoming documents, then passes structured data to an analysis assistant that generates the final report.
A coordinator dispatches multiple research tasks to different specialists simultaneously, then combines the results.Example: A due diligence assistant delegates financial analysis, legal review, and market analysis to three different specialists, then synthesises a summary.
When a front-line assistant encounters something outside its scope, it escalates to a more capable specialist rather than to a human.Example: A tier-1 support assistant handles common questions directly but escalates complex technical issues to a senior technical assistant before involving a human engineer.

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Automatic Sub-Agents

For tool-heavy workflows or large document processing, QuivaWorks automatically uses sub-agents to prevent context window overload. This happens transparently — no configuration required.

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Context Window Management

Every AI model has a limit on how much context it can hold in a single interaction. When an assistant needs to use many tools or process a large document, the accumulated context can exceed this limit. QuivaWorks handles this automatically:
1

Task Detection

QuivaWorks identifies that a task would exceed the context window — either because many tools will be called, or because a large document needs to be processed.
2

Sub-Agent Dispatch

Individual tool calls or document sections are handled by dedicated sub-agents. Each sub-agent works within its own context window.
3

Result Consolidation

Sub-agent results are returned to the main assistant, which synthesises them into a coherent response.

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Large Document Processing

When a large document is added to an assistant’s knowledge base, sub-agents index it in sections. At query time, a sub-agent retrieves the relevant sections and passes them to the main assistant. This enables accurate processing of:
  • Long technical documents and specifications
  • Extensive legal contracts
  • Large codebases
  • Book-length research reports
The assistant can answer specific questions about the document without loading the entire thing into context.

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Design Principles for Multi-Agent Systems

Specialist assistants work best when their scope is narrow and well-defined. A “Customer Support” assistant that tries to handle sales, billing, and engineering questions will be less effective than three focused specialists.
The coordinator needs clear guidance on when to delegate and when to handle requests directly. Include specific criteria: topic areas, complexity thresholds, or explicit trigger phrases.
Each specialist should only have access to the integrations and knowledge it actually needs. Keeping specialists focused makes them more accurate and easier to debug.
Before testing the full multi-agent system, test each specialist assistant on its own. It’s much easier to identify and fix issues in isolation than in a complex pipeline.
If you need deterministic, ordered processing across multiple assistants, build a Flow with multiple assistant steps rather than using assistant-to-assistant communication. Flows give you explicit control over data passing, branching, and error handling.

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Multi-Agent vs. Flows

Both multi-agent systems and flows can orchestrate work across multiple assistants. Choose based on your needs:
Multi-AgentFlows
Routing logicAssistant decidesExplicit conditions
Data passingVia conversationVia variable mapping
Error handlingAssistant handlesExplicit error steps
Best forDynamic, reasoning-based routingPredictable, structured pipelines
Use multi-agent when the routing logic requires judgment — the task is complex enough that an AI should decide who handles it. Use flows when the pipeline is predictable — you know in advance which assistants run in which order.

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Next Steps

Tools & Connectors

Connect assistants to external systems via MCP

Flows Overview

Build structured pipelines with assistant steps

Capabilities

Built-in tools, image analysis, and file generation

Best Practices

Design patterns for effective assistants