Perspective
Leveraging the hive mind
Harnessing the Power of AI Agents
3-minute read
November 2, 2024
In brief
- Agentic architecture is a strategic approach that employs AI agents to orchestrate and automate complex business workflows.
- AI agents help enhance productivity, decision-making, and overall operational efficiency.
- Companies stepping into agentic architecture must build a strong AI and data management foundation.
Much has been written on the pervasiveness of generative AI (gen AI), and the promise that this exciting new technology holds for organizations. Today, leaders worldwide are experimenting with early proofs of concept. Many have yet to unlock the real value that occurs when gen AI is deployed at full scale across an enterprise.
The next step is building a network of AI agents with different purposes, ranks and roles – much like bees in a hive working separately but together, toward a common goal. We call this structure “agentic architecture”. These agents do more than just automate tasks; they choreograph entire business workflows. Here lies their true magic. Endowed with the power of reasoning, these AI agents work together in harmony, autonomously enhancing quality, productivity, and cost-efficiency.
Imagine a world where supplier negotiations are automated, customer support is not just responsive but predictive, and self-service capabilities are so advanced that we may soon see the first fully automated car manufacturing plants operated by teams of generative AI agents – faster, and with fewer errors and recalls.
Consider our beehive analogy. Inside the hive, each bee has a distinct role—workers, drones, and the queen—all crucial for the hive's survival and productivity. Similarly, in an agentic architecture, different AI agents have different purposes; some manage specialized tasks such as market research, some perform strategic functions such as building a market strategy, and some direct the overall system (akin to the queen) ensuring that all agents work towards the collective goal of the organization, optimizing operations and decision-making. But just as bees on their own can’t produce honey (value) without their hive, AI systems created by agentic architecture enable agents to tackle complex tasks that would be beyond the capabilities of any single agent.
Why the buzz around agentic AI?
Currently, 1 in 3 companies are pivoting towards innovating with agentic AI, and those who embrace this shift towards AI swiftly stand to secure a significant competitive edge. According to our recent report, “Accelerating reinvention to support growth with AI-powered operations,” the number of companies that have fully modernized, AI-led processes has nearly doubled from 9% in 2023 to 16% in 2024. Compared to peers, these organizations achieve 2.5x higher revenue growth, 2.4x greater productivity and 3.3x greater success at scaling generative AI use cases.
For example, Accenture’s marketing function is deploying autonomous agents to help create and run smarter campaigns faster. This will result in a 25-35% reduction in manual steps, 6% cost savings and is expected to achieve a 25-55% increase in speed to market.
As another example, Accenture and BMW teamed up to create a multi-agent system that uses generative AI to drive decisions across North America, accelerating productivity and experiences. The employee platform contains multiple AI-enabled applications (GPT agents) that intelligently choose the right data source and pull information based on a salesperson’s specifications question and enterprise-specific data, resulting in a 30-40% productivity increase.
The integration of gen AI and AI agents into existing business frameworks today is driven by several key catalysts:
- Advancements in Large Language Models (LLMs): Imagine AI with a human-like grasp of language, capable of engaging in conversations, understanding complex queries, and even creating content across various formats. These models process and analyze vast troves of data, identifying patterns and extracting insights that enable AI agents to reason across and to manage a broad spectrum of tasks.
- Need for complex problem solving: From assessing creditworthiness by analyzing financial data and public records, to detecting fraud and maintaining customer communications, agentic AI thrives in environments that demand sophisticated decision-making and adaptability. By enabling multiple AI agents to collaborate and share information, these systems can deliver faster, more accurate outcomes than traditional methods.
- Desire for scalability & autonomy: Enterprises are increasingly drawn to agentic AI for its ability to autonomously manage and optimize complex workflows. Picture a large retail chain where AI agents forecast demand, manage inventory, and plan delivery routes, ensuring efficiency and responsiveness across the supply chain.
How does agentic architecture work?
AI agents are autonomous AI programs that use large language models (LLMs) to reason through problems, plan solutions and execute a plan. They draw on “memories” of past user interactions and a set of tools to achieve specific goals. AI agents quickly grasp a human’s intentions, present pre-built workflows to automate complex tasks, provide personalized assistance and enhance human-computer interaction. They have important applications in various fields, from customer service to scientific research.
If agentic architecture is like a beehive, Accenture's AI Refinery™ is akin to a master beekeeper, focused on building systems designed to transform raw AI technologies into scalable, enterprise-level systems. This comprehensive platform enables clients to tailor AI systems that continuously reinvent critical enterprise capabilities and workflows. At the heart of this dynamic ecosystem is its flexible agentic architecture, supporting a spectrum of AI agent solutions—from ready-to-use tools to fully customized systems tailored to the complexity of the task at hand.
AI Refinery empowers companies to integrate AI agents from consumable and configurable tech sources for immediate deployment. However, for businesses chasing a true competitive edge and complete workflow transformation, building custom agentic AI solutions is the way to go. The platform allows the creation of bespoke agents adept at managing complexity, evolving tasks and workflows. This customization offers flexibility, scalability, and strategic differentiation, beyond what you can obtain from many off-the-shelf solutions.
Generative AI agent-to-agent communication isn't just about chatting; it's about creating a structured way for AI systems to collaborate on complex tasks, boosting their smarts and functionality. Enter the Distiller Framework—it's like a turbocharger for deploying agentic AI systems, speeding up the process and enhancing value. This framework adapts to various architectural styles—be it distributed, modular, or federated—to fit different needs and tasks. It allows for deep customization of each agent, tailoring them to specific goals and equipping them with the right tools. Plus, there's a shared memory hub that keeps all agents on the same page, ensuring smooth teamwork. Finally, responsible AI is at the heart of the framework, ensuring that everything runs smoothly, safely, and transparently, guarding against biases and errors. It's all about making AI both powerful and trustworthy.
Agent Hierarchy
When understanding how a platform's agentic architecture works, it’s useful to think of a beehive. At the base, we have the Utility Agents, akin to the diligent worker bees, each specialized and autonomous, driven by enterprise knowledge (instinct) to perform specific tasks critical for the system's operation, and including gathering and sorting unstructured data (perhaps we can think of this as pollen). Above them, the Super Agents function similarly to the queen bee, overseeing the workflow and ensuring that the Utility Agents are effectively managed to achieve collective goals. At the top, the Orchestrator Agents orchestrate the entire operation like the hive's sophisticated communication system, coordinating between Super Agents and sometimes directly with Utility Agents to maintain harmony and efficiency across complex workflows. This structured hierarchy, illustrated in Figure 1, ensures precise task allocation, decision-making, and execution:
Getting started: what do you need?
Companies stepping into agentic architecture must prepare on all technology fronts. It’s not just about ticking boxes; it’s about building a strong AI and data management foundation. What does this mean?
At the core, the accessibility of foundation models, particularly Large Language Models (LLMs), is crucial, requiring a robust enterprise platform architecture to unlock their full potential. Integrated enterprise data is another cornerstone, leveraging multi-modal models capable of processing diverse data types such as images, text, and video. This setup gives agents instant access to the data they need to make smart decisions, pulling from both simple data and the more complex stuff scattered across different systems like databases and NoSQL stores, all linked up through APIs or microservices. Plus, vector databases are key players in digging up unstructured data to boost and enhance agent responses. And don't forget, things like messaging services, blockchain tech, and systems for emails and alerts are all part of the mix, keeping the data flowing fast and furious when it counts.
Moreover, having a solid plan for data and knowledge governance is important to keep your data accurate, available, and secure. This means setting clear rules for how data is collected, stored, and used, plus making sure it's clean and ready to go. You'll also want a centralized knowledge store in this ecosystem, which helps you manage and refine information and understand how different pieces of data relate to each other. This not only keeps things consistent but also boosts learning adaptability.
Operationalized LLMOps is another essential element. It includes managing agent API controls to make sure they're used right, observability and performance tracking to monitor the system is working, and having ways to gather feedback, continuously learn, fine-tune models, and train them. This keeps everything running smoothly and effectively.
These components collectively ensure a seamless, efficient, and effective implementation of agentic architecture, mirroring the dynamic and intelligent operational framework necessary for modern enterprises.
How can you move forward?
Integrating generative AI into your business can align with your current digital transformation efforts without requiring significant extra effort. By embracing agentic architecture, organizations can unleash innovation, optimize their 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 gen AI – and just like a beehive, generate some sweet returns.
Please contact us to learn more about how AI agents can help your organization.
What are AI agents?
AI agents are systems designed to perform complex tasks autonomously
Unlike traditional AI systems, AI agents are programmed to achieve specific goals. They adapt their strategies to meet these objectives, even in dynamic environments, ensuring focused and effective actions.
AI agents can logically reason and plan their actions. They decompose complex tasks into manageable steps, making them ideal for scenarios requiring sophisticated decision-making.
These agents have the capability to remember past interactions and learn from them. This memory aids in refining their future actions based on previous experiences.
AI agents can communicate and collaborate with other agents. This ability is crucial for addressing complex issues that require coordinated efforts across multiple agents.