Sub-agents & Multi-agent systems

Some operational tasks are too complex, multi-faceted, or broad for a single agent to handle effectively. Rival Enterprise allows multiple agents to collaborate in structured teams, a pattern known as a multi-agent system.

Instead of relying on one generalist agent trying to perform every step, multi-agent architecture operates like a specialized team: a coordinator manages the high-level workflow while specialist agents handle focused, specific tasks.

The orchestrator and specialist pattern

The primary structural pattern for multi-agent collaboration consists of an orchestrator managing dedicated specialists:

• The orchestrator agent — receives the initial objective, analyzes the requirements, breaks the job into smaller sub-tasks, and delegates work to appropriate sub-agents. Once sub-agents report back, the orchestrator synthesizes their outputs into a final deliverable.
• Specialist sub-agents — built to handle narrow, well-defined functions (such as document parsing, policy verification, or external system updates). Because each specialist focuses on a single domain, it is easier to test, refine, and trust. Dividing complex processes into specialized sub-agents improves execution reliability and reduces overall processing errors.

Agent-to-agent handoffs and context sharing

In a multi-agent network, agents pass data, task status, and execution context to one another as work progresses:

• Natural checkpoints — every handoff between agents serves as an operational checkpoint where system logs, data validation, or security checks occur.
• Targeted system access — rather than granting a single agent broad access to all company systems, individual sub-agents only receive connectors and tools required for their specific role.
• Audit transparency — every delegation step, from the orchestrator’s initial request to each sub-agent’s output, is recorded in audit activity logs.

Escalation patterns and human oversight

Human oversight is a manual setting you configure on an agent: you choose the step where it should pause and send its work for approval before continuing. The pattern below illustrates how escalation across multiple sub-agents is intended to work as your automations grow more advanced. It describes the intended design, not a built orchestration feature. Review happens through a manual pause-and-approve step you set on an individual agent, not through automatic handoffs between separate sub-agents.

• Threshold escalations — operations involving financial amounts above set limits or sensitive data fields are intended to automatically pause for human review.
• Exception routing — if a specialist sub-agent flags conflicting information or an error during execution, the task is intended to route to a human operator.
• Policy protection — escalation patterns are designed so routine volume is handled automatically while human decision-makers stay focused on exceptions and high-value judgment calls.

Design pattern, not a feature

The escalation behavior described above is the intended design. Today you achieve it by setting a pause-and-approve step on an individual agent — see Personality and guardrails and Approval workflows.

Example: multi-agent invoice processing

Consider an automated invoice processing pipeline managed by a multi-agent team:

1. Orchestrator intake — an orchestrator agent receives an incoming invoice file and initiates the processing pipeline.
2. Specialist delegation
   • Data extraction specialist — reads the document, extracts line items, and structures the financial figures.
   • Policy verification specialist — cross-references invoice totals against vendor contracts and corporate expense rules.
   • Payment setup specialist — drafts the payment transaction payload inside the accounting system.
3. Escalation logic — routine invoices under $1,000 that pass all policy checks are queued for execution automatically. Any invoice exceeding $1,000, or any item with mismatched line items, is escalated directly to a finance manager for approval.

The system processes thousands of routine vendor invoices automatically, while human staff only review flagged exceptions.

Principles for designing multi-agent systems at scale

Design principle	Core focus	Operational benefit
Narrow agent scope	Give each sub-agent one specific job	Higher accuracy and simpler troubleshooting
Targeted system tools	Attach only required connectors to each specialist	Enforces the principle of least privilege
Strategic checkpoints	Place human approvals at high-risk handoffs	Complete governance over sensitive system actions
Explicit exception handling	Design explicit escalation paths for errors	Prevents failed executions from cascading

Next steps

• Learn how individual agents are configured → What is a Rival Agent
• Set up human sign-off checkpoints → Approval workflows
• Plan large-scale organizational rollouts → Enterprise deployment playbook