January 9, 2025
Managing Partner
Integrating generative AI across enterprises through a network of AI agents, known as agentic architecture, can significantly enhance business workflows, productivity, and cost efficiency.
Generative AI (Gen AI) has captured the imagination of many and promises transformative potential for organizations worldwide. While leaders are actively exploring early proofs of concept, the full value of Gen AI remains largely untapped, with many still considering it just a chatbot. The true potential emerges when Gen AI is integrated comprehensively across an enterprise.
The journey forward involves constructing a network of AI agents, each with distinct purposes, ranks, and roles. What are AI Agents? An AI Agent is an AI tool entity designed to perform tasks autonomously, leveraging large and small language models with custom tuning or instructions to make decisions, learn from data, and interact with its environment. These agents operate independently, executing tasks with limited or no human intervention, which is crucial for scalability and efficiency. AI Agents continuously improve their performance through machine learning algorithms by learning from data and experiences. AI Agents can also interact with users and other systems through natural language processing, enhancing user experience and system integration. AI Agents drive efficiency, innovation, and strategic advantage across various industries by streamlining processes, reducing operational costs, and improving productivity.
This network of AI agents resembles a beehive, where bees work independently yet collectively towards a shared objective. We refer to this framework as "agentic architecture." These AI agents do more than automate; they orchestrate entire business workflows, where their true power lies. Equipped with reasoning capabilities and integrated into core business systems like ERP, CRM, and Customer Service applications, they work autonomously, boosting quality, productivity, and cost-efficiency.
Picture a landscape where supplier negotiations are automated, customer support is responsive and predictive, and self-service capabilities are so sophisticated that we might soon witness fully automated car manufacturing plants run by teams of generative AI agents—operating with speed, precision, and minimal errors or recalls.
Reflecting on the beehive analogy, each bee within the hive has a specific role—workers, drones, and the queen—each essential for the hive's survival and productivity. Similarly, in an agentic architecture, various AI agents have unique functions. Some handle specialized tasks like market research, others perform strategic roles like crafting market strategies, while a few oversee the entire system (akin to the queen), ensuring all agents align with the organization's goals, optimizing operations and decision-making. Just as individual bees cannot produce honey (value) without the hive, AI systems structured by agentic architecture empower agents to tackle intricate challenges beyond the reach of any single agent.
The excitement surrounding agentic AI is palpable, with one in three companies now pivoting towards this innovative technology. Earlier this week at CES 2025, NVIDIA CEO Jensen Huang referred to the changes and advances with Agentic AI in 2025. Businesses can now gain a significant competitive advantage by quickly embracing agentic AI.
A recent report titled "Accelerating Reinvention to Support Growth with AI-Powered Operations" reveals that the percentage of companies with fully modernized, AI-led processes has surged from 9% in 2023 to 16% in 2024. These forward-thinking organizations are experiencing revenue growth 2.5 times higher than their peers, 2.4 times greater productivity, and a 3.3-fold increase in successfully scaling generative AI use cases.
Take, for instance, Santiago & Company's marketing function, which is deploying autonomous agents to craft and execute more innovative, faster campaigns and content delivery. This strategic move is anticipated to reduce manual tasks by 25-35%, cut costs by 21%, and boost speed to market by 25-55%.
First, advancements in Large Language Models (LLMs) and even Small Language Models (SLMs) have endowed AI with a human-like understanding of language. With OpenAI's most recent announcement of o3 exceeding the highest benchmarks, the use case for enabling it to engage in conversations, comprehend complex inquiries, and create content in various formats is now standard business practice. These models analyze vast amounts of data, identifying patterns and extracting insights that empower AI agents to tackle multiple tasks.
Second, the need for complex problem-solving is ever-present. Whether assessing creditworthiness by examining financial data and public records, detecting fraud, or maintaining customer communications, agentic AI thrives in environments requiring sophisticated decision-making and adaptability. By allowing multiple AI agents to collaborate and share information, these systems deliver faster and more accurate outcomes than traditional methods.
Lastly, the desire for scalability and autonomy draws enterprises to agentic AI. Imagine a large retail chain where AI agents forecast demand, manage inventory, and plan delivery routes, ensuring efficiency and responsiveness across the supply chain. This autonomous management of complex workflows exemplifies the transformative potential of agentic AI in modern business operations.
Agentic architecture revolves around autonomous AI programs, known as AI agents, which employ large language models (LLMs) to navigate complex problems. These agents excel at understanding human intentions, devising plans, and executing them efficiently. Drawing from past user interactions and equipped with various tools, they automate intricate tasks, offer personalized assistance, and enhance human-computer interactions across multiple domains, including customer service and scientific research.
If one likens agentic architecture to a bustling beehive, then Santiago & Company's AI consulting practice is the master beekeeper. This platform is dedicated to transforming raw AI technologies into scalable, enterprise-level systems. It allows clients to customize AI systems that continuously revolutionize vital enterprise capabilities and workflows, integrating them into common platforms and applications.
Central to this vibrant ecosystem is a flexible agentic architecture, which supports a broad range of AI agent solutions—from ready-to-use tools to bespoke systems designed to tackle complex tasks. Our AI consulting practice (Kinetic Digital) empowers companies to integrate AI agents from accessible, configurable tech sources for immediate implementation. Yet crafting custom agentic AI solutions is essential for businesses seeking a competitive advantage and comprehensive workflow transformation. The platform facilitates the creation of specialized agents capable of managing complex tasks and evolving workflows. Companies gain flexibility, scalability, and strategic differentiation through customization, surpassing what many off-the-shelf solutions offer.
Generative AI agent-to-agent communication transcends mere conversation; it creates a structured environment for AI systems to collaborate on complex tasks, enhancing their intelligence and functionality. Central to this is the Alchemist Framework, which acts as a turbocharger for deploying agentic AI systems. It accelerates deployment and augments value by adapting to various architectural styles, whether distributed, modular, or federated, to meet diverse needs and tasks. The framework allows for deep customization of each agent, aligning them with specific goals and equipping them with the necessary tools. A shared memory hub ensures all agents remain aligned, fostering seamless collaboration. Responsible AI is integral to the framework, providing smooth, safe, and transparent operations while safeguarding against biases and errors. Ultimately, it's about cultivating AI systems that are both powerful and trustworthy.
Imagine a beehive when considering how a platform's agentic architecture functions. At the base of this structure, we find Utility Agents, which resemble industrious worker bees. Each agent is specialized and operates independently, driven by enterprise knowledge—similar to an instinct—to perform specific tasks crucial to the system's operation. These tasks include gathering and sorting unstructured data, such as collecting pollen from bees. Above them, the Super Agents act like queen bees, overseeing the workflow and ensuring that Utility Agents are effectively managed to achieve collective goals. At the top tier, the Orchestrator Agents take on the role of the hive's sophisticated communication system. They coordinate between Super Agents and sometimes directly with Utility Agents to maintain harmony and efficiency across complex workflows. This structured hierarchy, depicted in Figure 1, ensures precise task allocation, decision-making, and execution.
Getting Started: What Do You Need?
Comprehensive preparation across all technology fronts is essential for companies venturing into agentic architecture. It's not merely about checking boxes; it's about constructing a strong foundation in AI and data management. What does this involve?
At the heart of this foundation lies the accessibility of foundational models, particularly Large Language Models (LLMs). These models require a robust enterprise platform architecture to unlock their full potential. Integrated enterprise data is another cornerstone, utilizing multi-modal models capable of processing diverse data types, such as images, text, and video. This configuration provides agents instant access to the data they need for informed decision-making. It draws from straightforward data and more complex information scattered across various systems, including databases and NoSQL stores, all linked through APIs or microservices. Additionally, vector databases are crucial in extracting unstructured data to enhance agent responses. Furthermore, elements like messaging services, blockchain technology, and systems for emails and alerts are vital components, ensuring the swift and efficient flow of data when it matters most.
Moreover, establishing a solid data and knowledge governance plan is crucial for maintaining data accuracy, availability, and security. This involves setting precise data collection, storage, and usage guidelines while ensuring data cleanliness and readiness. A centralized knowledge store within this ecosystem aids in managing and refining information and understanding the interrelationships between different data pieces. This not only maintains consistency but also enhances learning adaptability.
Operationalized LLMOps is another essential component. It encompasses managing agent API controls to ensure proper usage, observability, and performance tracking to monitor system functionality and mechanisms for gathering feedback, continuous learning, fine-tuning models, and training. These elements collectively ensure the seamless, efficient, and effective implementation of agentic architecture, mirroring the dynamic and intelligent operational framework necessary for modern enterprises.
Integrating generative AI into your business can seamlessly align with your existing digital transformation efforts without demanding significant additional effort. By embracing agentic architecture, organizations can unlock innovation, optimize operations, enhance decision-making, and foster collaboration between humans and AI.
Leaders who recognize the strategic imperative of agentic architecture and proactively invest in its development and adoption will be well-positioned to shape the future of their industries and pave the way for future growth with generative AI. Much like a beehive, they will generate sweet returns.
Please contact us to learn more about how AI agents can benefit your organization.
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