September 30, 2025

The Agentic Orchestra: Multi-Agent Systems in Action

By Ignacio Aristimuño

AI agents have become increasingly capable, but real-world problems often require multiple agents working together, each handling part of a task and coordinated under a shared objective. This is where the agentic orchestra comes in: the idea of orchestrating agents into a unified system. At Marvik, we have faced these challenges across different industries and developed orchestration best practices that allow systems to move quickly from prototypes to production-ready systems.

Why Orchestration Is Critical

In the agentic orchestra, orchestration agents (planners) manage conversation flows, delegate tasks, and maintain system logic. They prevent agents from working in silos and instead coordinate them into a coherent system. Without orchestration, inconsistencies, inefficiencies, and misalignments with user goals are common. With orchestration:

Tasks are routed to the right specialized agents.
System state and conversation context are preserved.
User validation and re-planning keep intent aligned.
Outputs remain predictable and business-focused.

Orchestration Models: Single-Step vs Multi-Step

Two approaches dominate agent orchestration:

Multi-step orchestration: The orchestrator decomposes tasks into multiple stages, delegating across agents. This adds autonomy but increases latency and complexity.
Single-step orchestration: The orchestrator makes a single decision and delegates the task end-to-end. This minimizes latency and simplifies workflows, ideal for customer-facing contexts.

At Marvik, we favor single-step orchestration as it balances speed, simplicity, and user satisfaction, except for cases where fully autonomous systems with little-to-no human interaction is preferred. This aligns with 2025 trends in chatbots and assistants, where users value fast and accurate responses.

Diagram showing multi-agent platform architecture with orchestration layers, adaptable for web and mobile. — *High-level architecture of a platform powered by multi-agent systems, supporting both orchestration approaches, adaptable across web and mobile applications.*

Conversation Flow

We designed an example flow that demonstrates effective orchestration. In a customer support use case, the orchestrator agent interacts with specialized agents for tackling different requests, from responding to FAQs, to gathering information in real time to solve complex issues. This, added to troubleshooting, or escalation. The following example highlights how routing and coordination works in this kind of system.

Workflow diagram illustrating the logic flow of a multi-agent system. — Workflow graph logic of the Multi-Agent System

System State and Prompt

In LangGraph, conversation state ensures continuity and context across interactions. Prompts define each agent’s role and objectives, ensuring consistent responses.

Code snippet example showing WorkflowAgentState handling state keys for consistent conversations. — Example of WorkflowAgentState managing state keys for conversation consistency.

Planning and Re-planning

Diagram of a feedback loop with user validation, plan confirmation, and re-planning when intent changes. — Explanation: Feedback loop showing user validation, plan confirmation, and re-planning when intent changes.

The orchestrator evaluates messages by checking if they are relevant, not forbidden, and actionable. It validates planned steps with the user and re-plans if needed. This dynamic adjustment improves accuracy and satisfaction.

Handling Non-Valid Messages

Agents also identify irrelevant, forbidden, or unclear inputs. Depending on classification, the system redirects, requests clarification, or blocks inappropriate requests.

Explanation: Examples of how the orchestrator handles forbidden, irrelevant, and unclear instructions.

Agents as Subgraphs

Specialized agents are organized within a subgraph of the main workflow. This modular design makes the system easier to understand, maintain, and extend.

Key benefits:

Reusability: Sub-agents can run independently for specific tasks (e.g., triggering a Q&A agent from a web app).
Clarity: Isolating agent logic keeps orchestration focused, simplifying debugging and scaling.

This clean separation of responsibilities lays the foundation for more complex workflows and AI at scale.

Workflow Initialization and Finalization

Two nodes frame conversations:

Initialization: Resets transient variables.
Finalization: Updates state, marks tasks as completed, and clears temporary variables.

State variable snapshots before initialization and after finalization.

Warnings and Pitfalls

State updates: Partial updates are safer than full overwrites.
Parallel nodes: Require careful handling to avoid race conditions; LangGraph’s Send API provides safe execution.
LangGraph Studio vs production: Studio is ideal for prototyping, but production requires backends like Redis or DynamoDB, API integration, and manual deployment. Avoid underestimating this effort when prototyping.

Different approaches to updating LangGraph’s state variables at each node

Good Practices and Tips

From our implementations, with the agentic orchestra, these patterns emerged:

BaseNode class: Standardizes LLM calls, JSON parsing, and schema validation.
Standard logic structure: Organizing actions, graphs, models, prompts, and routes consistently.
Structured outputs: Using Pydantic models and JSON for clarity and downstream reliability.
Sub-agents for modularity: Improving debugging, reusability, and clarity.
Unified LLM abstraction: Using LangChain’s chat models for provider-agnostic orchestration. This allows also for easily switching across different third-party providers.
State management: Redis for prototypes, DynamoDB or PostgreSQL for scaling.

How the BaseNode handles LLM output parsing, JSON extraction, and schema enforcement.

Folder structure standardization for scalability and maintainability.

Business Impact of Orchestration

When implemented through the agentic orchestra, orchestrated multi-agent systems create real business value:

Efficiency: Automating workflows end-to-end.
Cost reduction: Lowering manual workloads.
Scalability: Supporting enterprise growth.
Trust: Providing reliable, transparent AI interactions.

Conclusion: Why Marvik

Building robust multi-agent workflows is complex, but Marvik has developed practices that make them scalable and production-ready. We deliver:

End-to-end AI development
Custom AI solutions aligned with business workflows
AI team extension and augmentation
Fractional CAIO leadership for strategy
Proven methodologies validated across industries

Marvik bridges advanced orchestration with business strategy, ensuring AI delivers measurable outcomes.

Key Takeaways

Single-step orchestration reduces latency and improves alignment in many real-world systems, although multi-step orchestration might be more suitable for cases where more automation and less human intervention is preferred.
LangGraph enables modular, stateful agent workflows when paired with good design.
Best practices in state management, modularity, and structured outputs improve reliability.
Orchestration is not just technical infrastructure but a competitive business differentiator.
It is important to ensure best practices why building these multi-agent solutions in order to avoid pains in production, or while transitioning from prototype to production.

At Marvik, we believe the future of AI is orchestral: systems of coordinated agents working seamlessly to deliver scale, reliability, and impact. If your organization is ready to move from pilots to production-ready AI, we are ready to help.