Exploring Distributed Modular AI Systems: Architecting Agentic Workflows and Agent Graphs

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As AI systems continue to evolve, distributed modular architectures are emerging as a promising solution for integrating complex processes with improved recall and adaptability. In this thought piece, we delve into the technical underpinnings of these systems, discussing agentic workflows, the integration of commercial use cases with base models, future-proofing design, and the role of agent graphs.

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Agentic Workflows in Distributed AI

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Traditional AI pipelines typically process data in a linear or monolithic fashion. In contrast, agentic workflows introduce a decentralized structure in which individual “agents” are responsible for distinct tasks. This model enables the system to handle complex integrations by:

• Cognitive Layering: Each agent is designed to execute specific functions, such as data extraction, transformation, or decision-making, while collaborating to maintain contextual continuity.
• Enhanced Recall: The modular nature allows the system to recall and reuse past information, improving overall efficiency and accuracy in multi-step processes.
• Dynamic Task Management: Agentic workflows facilitate the delegation of tasks in a manner akin to human problem-solving, where high-level planning is paired with the execution of specialized sub-tasks.

Integrating Commercial Use Cases with Base Models

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Engineered AI systems are not solely about cutting-edge algorithms; they are also about practical application. One key goal for us @ SageX is to integrate commercial use cases with robust base models:

• Seamless Integration: Instead of designing bespoke models for every task, a base model provides core functionalities, while modular components tailor these functions to specific commercial challenges.
• Versatility: The integration approach supports diverse industry applications—from connecting disparate financial data assets from multiple unstructured sources to extending an actionable 360 degrees risk perspective —by leveraging common AI capabilities and extending them with domain-specific logic.
• Sustainable Development: This design paradigm reduces redundancy and accelerates deployment, enabling rapid iteration and adaptation to new business requirements.

Future-Proofing AI Architecture

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A major challenge in AI system design is ensuring that improvements in base models enhance rather than obsolete existing systems. The key strategies include:

• Modular Replacement: By decoupling system components, improved models can be integrated without overhauling the entire infrastructure. Modules can be updated independently, ensuring that the overall system remains state-of-the-art.
• Interoperability: Designing with standard interfaces and protocols allows different models and tools to communicate effectively, paving the way for continuous improvement and scalability.
• Incremental Enhancement: Instead of building monolithic systems that risk becoming obsolete, modular architectures embrace an evolutionary approach. As new models emerge, they can be plugged into existing workflows, thereby enhancing capabilities while preserving previously built functionality.

Agent Graphs: The Modular Backbone

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At the heart of distributed modular AI systems lies the concept of agent graphs. These are interconnected networks of specialized modules (agents) that work collaboratively to achieve complex tasks. Key aspects include:

• Structural Flexibility: Agent graphs enable developers to construct and reconfigure systems based on evolving requirements. This approach allows for the creation of tailored workflows that can be optimized for specific use cases.
• Layered Intelligence: Each node (or agent) in the graph performs a dedicated function, yet the network as a whole exhibits emergent properties—such as increased contextual awareness and enhanced decision-making capabilities.
• Integration with Existing Tools: Agent graphs are built on top of existing AI tools and models, ensuring that advances in individual components contribute to the performance of the entire system. This modular design is central to building AI systems that improve over time without necessitating a complete redesign.

Business Use Cases at SageX - Using agentic systems to build reliability

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Here at SageX, we value reliability of information greatly, since true business use cases of AI cannot suffer from improperly addressed or hallucinated responses generated from a language model’s output. One key area where we are integrating agentic AI workflows is in the verification of risk outputs for private debt (including liquidity, operational, default risk etc). Specialized agents cross-reference identified risk with source data and even regulatory requirements, ensuring contextual accuracy and actively preventing factual inaccuracies or hallucinations before the information is finalized. This multi-agent validation enhances the trustworthiness of our automated risk management processes.

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Implementing Agentic systems for advanced compliance with tool-calling

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Tool calling capabilities allow language model based systems to access external tooling, to allow knowledge retrieval, action based items like writing to a document, database, among any other arbitrary capability that can be accessed via code. At SageX, we aim to utilize this capability of the model, in conjunction with the internal tooling we have developed for NLP (Natural Language Processing) techniques over the years like GPM and FSR, to increase the agent’s abilities in terms of knowledge retrieval, actionable intelligence and automated workflows. Given enough time for any unstructured data extraction based use case, the agent could be set up to automatically read a database of unstructured data sources including documents, emails etc, parse them into a structure that enables it to identify key components of data, and then write records extracted from this data, all using a multi-tool loop. The diagram below highlights one such possible use case:

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Conclusion

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Distributed modular AI systems represent a significant step forward in building flexible, scalable, and sustainable AI architectures. By leveraging agentic workflows and agent graphs, these systems not only address complex integrations with enhanced recall but also ensure that the deployment of better base models translates into continuous improvement. As research and development in AI accelerates, such modular approaches will likely become the cornerstone for solving real-world problems across various commercial domains.

This technical exploration highlights how thoughtful system architecture can bridge the gap between cutting-edge AI research and practical, commercial applications—ensuring that as technology evolves, our solutions evolve with it.