Composite AI and multi-agent systems: Towards modular and orchestrated AI

In the world of artificial intelligence, capability is not determined by scale.

Large-scale models, such as LLMs, certainly offer significant computational and reasoning capabilities. At the same time, however, they can also introduce greater complexity: for example, they may make hallucinations harder to control and be less adaptable to contexts where resources are limited.

Composite AI, and even more so multi-agent systems, or MAS, represent a major paradigm shift. They combine multiple AI systems or entities, each specialized in complementary tasks, to reduce costs and improve performance.
Relying on a single AI model can become limiting over time. These new approaches allow both private companies and public organizations to achieve better and more scalable results.

In highly regulated sectors, they also represent a choice that ensures reliability, accuracy, and security.
In this article, we will explore what Composite AI and multi-agent systems are, how they work, their main benefits, and future prospects.

Composite AI refers to an integrated approach that combines different AI technologies, including:
• LLMs and SLMs
• ML/DL models
• NLP
• RAG
• Ontologies
• Knowledge graphs
• Symbolic rules

These technologies work together, with each one activated to perform the tasks where it is most effective.
To put it simply, Composite AI is like a soccer team. Just as each player has a specific role—defender, striker, goalkeeper—and is called upon when needed, each technology in Composite AI is activated only for the tasks it is best suited to perform.
Each model can deliver excellent performance on certain tasks, while being less suitable for others.
For example, knowledge graphs are highly effective for mapping concepts and relationships between data, but they cannot generate content.

Composite AI therefore makes it possible to overcome the limitations of individual technologies by activating, each time, the most suitable ones and fully leveraging their strengths.
Let’s look at a practical example.

In searches across corporate knowledge bases, the conversational component can be managed by an LLM, while the information search and retrieval component can be handled by specific technologies optimized for the application domain.
In other cases, using an LLM may even be excessive. This applies to contexts with significant infrastructure constraints, situations where full control over data management is essential, or cases where less expensive, ready-to-use alternative technologies can solve highly specific tasks in specialized domains.

Just as a soccer team needs a coach to organize play and strategy, moving from monolithic AI to the more complex workflows of Composite AI requires a mechanism to manage, supervise and coordinate flows.

This is where the AI orchestrator comes in: the component that allows the system to work by connecting models, data, and agents, while maintaining control across the different steps. It guides generative AI models and helps produce more accurate and appropriate results, both in terms of content and format.

Through intelligent routing mechanisms, the system uses all available technologies to optimize costs, performance, and output quality across broader systems, workflows, or applications.
The orchestrator fills gaps and connects all the AI technologies involved in the workflow, making it possible to achieve goals that would be too complex for a single model.

The system also enables the automated management of computational resources without human intervention. This means it can optimize how workloads are distributed across different components, activating or deactivating resources according to need and prioritizing the most urgent requests.
Finally, orchestration is a key element in supervising the entire lifecycle of applications, ensuring regulatory compliance, data security, and privacy protection.

Multi-agent AI works in a very similar way to the Composite AI approach described above, with one important difference: in this case, all the technologies involved rely on Generative AI.

More specifically, multi-agent AI is based on autonomous, specialized agents that collaborate with one another. These agents are computational entities capable of carrying out specific tasks.

Multi-agent systems also rely on an orchestrator, which directs operations and activates the various “expert” entities best suited to each task, reducing errors, costs and processing times.

This approach produces significant effects in terms of computational efficiency and, in some cases, also economic efficiency. Using a MAS means that it is not always necessary to rely on the latest or most expensive models. Instead, it becomes possible to choose more cost-effective models that perform well on a single task, rather than larger and more expensive models.
Multi-agent systems are one of the most promising developments in AI, as they make it possible to overcome the limitations and constraints of individual models by autonomously executing complex tasks more effectively and efficiently.

Here are a few examples:

• Email routing: agents can automatically classify and distribute customer care emails, directing them to the right department.
• Document management: agents can complete a file by drawing on multiple data sources at the same time and ensuring a consistent, homogeneous style.
• Automated document processing: activities such as managing delivery notes, orders, or invoices can be simplified through automatic data extraction and classification, reducing errors and operational time.

The benefits offered by these innovative forms of AI are numerous and clear.

• Cost reduction: These solutions make it possible to achieve high performance while using fewer resources. For certain tasks, for example, it is not necessary to rely on monolithic models that are expensive in computational and energy terms. Simpler, lighter systems can be used instead, including in edge environments.
• Lower energy impact: AI has a significant energy footprint. The servers and data centers required to run an LLM consume large amounts of energy and water. Composite AI systems, on the other hand, are designed to activate only the technologies required for each task, avoiding unnecessary waste.
• Greater efficiency: Reducing costs and energy impact while delivering more precise and contextual responses means maximizing efficiency. This is even more evident in MAS, where entire processes can be automated with reduced resource consumption, allowing professionals to focus on higher-value activities.
• Flexibility and scalability: The architecture of Composite AI and MAS makes it easy to adapt the system to different contexts and use cases. Individual components can be updated, replaced, or integrated without having to redesign the entire solution. This makes AI more modular and scalable, allowing it to evolve over time alongside the organization’s needs.
• Accuracy and reliability: Integrating different technologies—such as language models, knowledge graphs and symbolic rules—makes it possible to obtain highly accurate results. The AI orchestrator helps ensure governance by applying rules and guardrails that keep systems aligned with adopted procedures and regulations.
• Resilience and risk reduction: Unlike monolithic models, these systems do not depend on a single technology or provider. This reduces the risks associated with malfunctions, performance drops, or service unavailability. When needed, alternative solutions can be activated.
• Transparency and explainability: The integration of specific techniques can increase the transparency of decision-making processes. In these systems, it is possible to trace how decisions are made, making outputs easier to understand and support regulatory compliance, especially in highly regulated contexts.

These benefits are not just theoretical. They are already being applied in platforms designed according to a composite approach, such as Almawave’s AIWave platform.

AIWave is the artificial intelligence platform designed by Almawave to support companies and Public Administrations in implementing AI within operational processes, including in low-code/no-code mode.

It is a modular and scalable platform created to design and develop AI-based applications, integrate intelligent functionalities into existing business processes, or use ready-made solutions to enhance activities.

The AIWave platform is based on the Composite AI approach introduced above, whose generative capabilities — LLMs, RAG and NLQ, or Natural Language Query — are integrated with other technologies to build more efficient, controllable and contextual systems.

In this context, orchestration plays a central role: AIWave coordinates models, data and intelligent components, enabling each technology to be used in the most appropriate way and allowing complex flows to be managed dynamically.

Machine learning, deep learning technologies—including pre-trained models—knowledge graphs and RAG all contribute to improving system accuracy, efficiency and transparency, as well as the user experience.
Thanks to AIWave’s Composite AI, Almawave can provide specialized, high-performing solutions, particularly for companies and institutional bodies looking for highly reliable and secure systems.

By limiting LLM usage to only where it is truly needed, these multi-model AI systems can also strengthen security and privacy compliance, delegating other operations to internal technologies that are easier to control.

Want to discover the benefits our Composite AI can bring to your business?