Choosing the right orchestration pattern for multi-agent systems

Choosing the right orchestration pattern for multi agent systems

Published Date:

October 3, 2025

Last Updated ON:

July 31, 2026

As enterprises evolve from deploying individual agents to building interconnected agentic AI systems, the challenge shifts from building AI agents to coordinating them effectively.

Choosing the right orchestration pattern is one of the most important architectural decisions in designing a multi-agent AI system. A well-designed orchestration strategy turns a collection of intelligent agents into a high-performing multi-agent AI architecture that can scale and operate with enterprise-grade reliability.

Why does orchestration matter?

The orchestration pattern defines how agents interact, share context, and collaborate to complete complex tasks. The choice directly affects four fundamental dimensions of enterprise AI performance:

Types of orchestration patterns

Kore.ai provides three distinct orchestration patterns that enable enterprises to balance control, scalability, and speed of innovations:

  1. Supervisor pattern – centralized command and control

  2. Adaptive agent network pattern – decentralized collaboration

  3. Custom pattern – programmatic flexibility and control

Each pattern addresses the diverse needs and technical maturity of an organization. The following sections explain how they function in practice, along with guidance on when to use them and when to avoid them.

Supervisor pattern: Centralized command and control

The Supervisor pattern employs a hierarchical architecture in which a central orchestrator coordinates all multiagent interactions. The orchestrator receives the user request, decomposes it into subtasks, delegates work to specialized agents, monitors progress, validates outputs, and synthesizes a final unified response.

Example: Loan payoff in enterprise banking

Consider a customer interacting with a banking assistant who says,

“Pay off my car loan using my savings account.”

While the request appears simple, it involves multiple backend systems, sequential dependencies, and compliance checks. The supervisor orchestrates the process as follows:

  1. The orchestrator receives the user input and parses its components:

  1. It maps the workflow into four key actions:

    • (a) retrieve loan payoff quote → (b) verify available funds → (c) execute transfer → (d) generate confirmation.

  2. The orchestrator identifies three specialized agents for the job:

    • Loan Agent – retrieves payoff amount, accrued interest, and penalties.
    • Transaction Manager – verifies balance, daily limits, and fraud thresholds.
    • Payment Processor – executes the transfer and confirms settlement.

  3. The orchestrator provides each agent with only the data necessary to complete its task securely:

    • The Loan Agent receives masked loan details and customer attributes to calculate the total payoff.
    • The Transaction Manager accesses balance and policy thresholds but not loan data.
    • The Payment Processor receives tokenized identifiers and transfer details without exposure to sensitive PII.

  4. The Loan Agent and Transaction Manager operate in parallel - retrieving payoff data and verifying funds simultaneously. Once both return results, the orchestrator initiates the Payment Processor.

  5. The orchestrator validates that:

    • The payoff amount matches available funds.
    • The transaction complies with internal policies.
    • The payoff quote remains valid.

  6. If inconsistencies arise, it replans - for example, requesting an alternative account or refreshing data.

  7. The orchestrator aggregates all outputs into a single, validated response:

    • “Your car loan payoff of ₹X has been processed. Transaction ID: 12345. Settlement expected within 24 hours.”

When to use supervisor pattern

When to avoid supervisor pattern

Adaptive agent network pattern: Decentralized collaboration

The adaptive agent network pattern eliminates centralized control, enabling agents to collaborate and transfer tasks directly based on expertise and context. Each agent can determine whether to execute, delegate, or enrich the task before passing it forward.

Example: Employee payroll assistance workflow

An enterprise employee reaches out to an internal AI assistant and says:

“I can’t access my payslip.”

In this scenario, agents interact autonomously without an orchestrator mediating each step.

  1. The Welcome Agent receives the message, performs intent classification, and determines that the request may relate to either system access or payroll data.

  2. The Welcome Agent routes the task to the IT Assistant, sharing structured context:

    • Employee ID (masked)
    • Channel of interaction (Teams or intranet)
    • Error message or HTTP code
    • Session metadata

  3. The IT Assistant uses this data to continue the conversation seamlessly, avoiding redundant questions.

  4. The IT Assistant investigates logs and finds that authentication is successful, but synchronization with the payroll database has failed - indicating the issue lies in the finance system.

  5. The IT Assistant transfers the enriched context to the Finance Assistant, providing:

    • Investigation summary
    • Detected system of failure
    • Masked identifiers for the relevant payroll record

  6. The Finance Assistant resumes directly, without re-prompting the employee.

  7. The Finance Assistant regenerates the payslip, syncs the payroll record, and confirms that the issue is resolved, either responding directly or handing the message back to the Welcome Agent for delivery:

    • “Your payslip access issue has been resolved. The document is now available on the HR portal.”

When to use

When to avoid

Custom pattern: Programmatic flexibility and control

The custom pattern provides enterprises with full programmatic control over orchestration. Using the Kore.ai Agent SDK, developers can design orchestration logic, agent relationships, and execution rules tailored to their organization’s compliance, performance, and integration needs.

This pattern is ideal for highly regulated industries or advanced AI engineering teams that require deterministic control and deep system integration.

Example: Regulatory risk review in financial services

A global bank implements an automated loan risk review workflow that must comply with internal and external regulatory frameworks.

Off-the-shelf orchestration patterns cannot enforce the institution’s proprietary models and audit requirements, so developers implement a custom orchestration pipeline using the Kore.ai SDK.

  1. Developers define specialized agents and their dependencies in code:

    • Data Retrieval Agent – collects customer and loan data from internal systems.
    • Risk Analysis Agent – applies proprietary risk models.
    • Compliance Agent – validates outcomes against jurisdictional and audit rules.
    • Report Generator Agent – compiles the final, certified report.

  2. Developers design a shared context object carrying only essential metadata:

    • Loan identifiers, jurisdiction, and audit references.
    • Model parameters and compliance tags.
    • Execution timestamps for full traceability.

  3. All sensitive data is masked or tokenized per organizational policy.

  4. The orchestration logic dynamically routes between agents:

    • If the Risk Analysis Agent flags high exposure, the controller triggers a Manual Review Agent for secondary assessment.
    • Otherwise, it proceeds to Compliance Agent for validation.
    • Failures or timeouts are handled programmatically through error-catching routines.

  5. The Risk Analysis and Compliance agents can execute in parallel once the data retrieval completes. Synchronization points ensure all results are received and validated before final aggregation.

  6. The Report Generator Agent compiles a signed, versioned report including:

    • Risk score, compliance validation results, and reasoning summary.
    • Complete audit log and regulatory references.

All agents operate within Kore.ai’s managed, sandboxed environment, enforcing encryption, RBAC, and least-privilege access. The orchestration logic itself is version-controlled, logged, and monitored for compliance audits.

This multi-agent system example highlights how the Custom pattern combines enterprise-grade security and scalability with full programmatic flexibility - empowering organizations to operationalize unique, high-stakes business logic under complete control.

When to use custom pattern

When to avoid custom pattern

Implementation recommendations

Selecting the right orchestration pattern depends on your organization’s goals, technical maturity, and operational priorities. The core principle is to choose the simplest pattern that effectively meets your business requirements.

Most enterprise implementations achieve optimal results using the Supervisor or Adaptive Network patterns, reserving the Custom pattern for workflows demanding full programmatic control.

Recommended approach

Conclusion

Each orchestration pattern represents a distinct balance between control, performance, and flexibility:

By offering all three orchestration patterns within a unified platform, Kore.ai empowers enterprises to design, deploy, and scale multi agent systems that align precisely with their operational requirements, technical maturity, and long-term AI strategy.