Building the Agentic Enterprise
In today’s enterprises, it’s common to have hundreds of software applications, most of which operate in silos, creating a tangled web of inefficiencies that hampers productivity.
AI-powered multi-agent systems (MAS) and autonomous agents offer a solution to these inefficiencies, bringing three key advantages to the enterprise landscape: productivity gains, cost-efficiency, and adaptability and forming the backbone of what we call the agentic enterprise — an organisation built on AI systems that are collaborative, adaptable, and capable of independent reasoning.
By enabling real-time data flow and decision-making across previously isolated systems, these networks streamline workflows and reduce manual intervention, freeing up valuable time and resources. Their adaptability allows organisations to build solutions tailored to specific operational needs, reducing reliance on rigid, one-size-fits-all SaaS tools. By moving towards proprietary multi-agent systems and setting the groundwork for the agentic enterprise, businesses can build flexible, customised workflows that better align with their goals.
The adoption of AI agents is happening faster than you may think.
MAS are composed of AI agents, which range from basic task-specific tools to more advanced autonomous agents capable of agentic reasoning, planning, and adapting to changing conditions. While not all AI agents are autonomous, the integration of even basic agents can streamline operations and improve efficiency across complex workflows.
The adoption of AI agents is happening faster than you may think. According to a recent survey of over 1,300 professionals, 51% of companies already have AI agents in production, with 78% actively planning to implement them soon. Mid-sized companies (100–2000 employees) are leading the way, with 63% already deploying agents. Interestingly, this momentum isn’t confined to the tech sector; 90% of non-tech companies are using or planning to use agents, nearly matching the adoption rates of tech companies at 89%.
Elsewhere, with the arrival of multi-agent frameworks like OpenAI’s Swarm, LangGraph, and ReAct, enterprises can now bridge disconnected agents through intelligent, collaborative systems, creating a cohesive network that transforms decision-making and operational efficiency.
These frameworks are driving multi-agent systems to become increasingly agentic, meaning they are powered by autonomous agents capable of reasoning, planning, learning from past interactions, and communicating with each other. This shift allows MAS to go beyond simple task coordination, fundamentally reshaping how software integrates within enterprise environments.
At the same time, the fact that companies like Stripe are launching API payments services for AI agents — with the ability to assign agents temporary single-use virtual cards to take controlled purchasing actions — further signals how AI agents are becoming increasingly productionised and are no longer a theoretical trend.
In this white paper, we outline the strategic opportunities unlocked by agents and multi-agent systems and explore how they lay the foundation for the agentic enterprise.
— Leon Gauhman
What are Multi-Agent Systems?
Multi-agent systems have been a foundational topic in AI research for decades, but in simple terms, a multi-agent system is made up of multiple decision-making entities — whether AI agents, robots, or even humans — working together in a shared environment to achieve their goals.
The core challenges in multi-agent systems revolve around enabling agents to plan, learn, and collaborate effectively. This involves designing systems where agents can communicate, share information, and even adapt their behaviours to align with overarching objectives.
As Generative AI adoption accelerates, multi-agent systems are shifting from theory to practice.
When talking about artificial intelligence, at the heart of these systems are the AI agents themselves — the individual entities responsible for executing tasks, making decisions, and interacting within their environment.
As Generative AI adoption accelerates — Wharton recently found that there are now 73% weekly AI users in 2024 (up from 37% in the year prior) — multi-agent systems are shifting from theory to practice, making their widespread application inevitable. Driven by productivity gains, cost savings, and streamlined operations, these systems are poised to transform business operations, enhancing efficiency, fostering innovation, and enabling smarter decision-making across all functions.
AI Agents
When we talk about multi-agent systems, we’re referring to a network of AI agents working interchangeably and concurrently. But what exactly is an AI agent? In essence, AI agents are digital systems designed to perform tasks, make decisions, and solve problems by interacting with their environment — sometimes fully autonomously and sometimes with a human-in-the-loop.
These agents, often serving as copilots, take on an assistive role, automating routine tasks, processing data, and providing recommendations. By adopting a ‘jobs-to-be-done’ perspective — focusing on the specific tasks they are designed to accomplish — they are seen as complementing rather than replacing human roles.
In our previous report on Generative UI, we explored how AI-powered interfaces simplify complexity and make cutting-edge AI tools accessible to the broader workforce. These assistive agents dynamically generate user interfaces, offering real-time responses tailored to specific tasks, further reducing cognitive strain and enhancing decision-making
Central to the success of these agents is selecting the right model for each job. Rather than relying on large, resource-intensive models for every task, multi-agent systems benefit from a range of model types that suit different functions. Small Language Models (SLMs) are especially well-suited for targeted tasks, delivering advantages in speed, energy efficiency, and lower memory requirements. This makes them ideal for tasks requiring rapid, specific responses where the power of a full-sized Language Model (LLM) is unnecessary.
For instance, SLMs can power prompt-and-response agents that handle straightforward interactions, offering real-time processing that fits seamlessly within a multi-agent ecosystem while LLMs take on sophisticated tasks, such as analysing large datasets or generating in-depth insights. By focusing on specific language tasks, these smaller models achieve faster response times and lower costs, providing practical and efficient solutions for businesses.
But this is just the beginning of the tale, and the emerging agentic enterprise is one where every organisation will have a constellation of agents — ranging from simple prompt-and-response systems to fully autonomous AI — working together to drive productivity.
Far from replacing people, AI agents are master orchestrators within the enterprise.
Far from replacing people, AI agents are master orchestrators within the enterprise. Acting on behalf of the organisation, they tackle repetitive tasks and coordinate seamlessly with other systems, enabling employees to concentrate on higher-value work and fostering a collaborative, intelligent ecosystem.
Semi-Autonomous Agents and Human-in-the-Loop
Central to the success of multi-agent systems is the ability for agents to interact with their environment in dynamic and intelligent ways. Claude 3.5’s ‘Computer Use’ feature from Anthropic is a prime example, demonstrating how an AI agent can control a user’s desktop, move the cursor, and type in real time, executing multi-step tasks based on human instructions.
It is worth mentioning here how the “Computer Use” feature highlights the emerging capabilities of multimodal agents. Unlike traditional LLMs confined to text-based interactions, these agents can interpret and act upon various forms of data — such as text, images, and screenshots. According to initial reports, Claude 3.5 selects from pre-defined tools (e.g., clicking, typing) based on the user’s prompt, generating structured commands that allow it to seamlessly interact with computer interfaces.
The ‘Computer Use’ works by relying on an iterative agent loop, where each action is executed, analysed, and fed back to the model to determine the next step. This allows Claude to perform tasks like navigating websites, interacting with desktop applications, and gathering information from documents or spreadsheets — essentially bridging the gap between AI and human-computer interaction (HCI).
Despite its advanced capabilities, Claude’s “Computer Use” feature does not yet reach full autonomy because it cannot independently define objectives or take action without human prompting. While Claude can perform complex tasks and execute commands efficiently, it requires explicit instructions from a human operator to initiate actions. This means Claude operates within the scope defined by user input, responding to requests rather than proactively engaging in tasks (unlike a fully autonomous system, wherein the agent would be capable of self-directed decision-making, more on this below).
Amazon’s Q Developer Agents are another strong example of semi-autonomous agents, providing productivity boosts by performing complex, multi-step tasks in software development autonomously. These agents, used by companies like BT Group and National Australia Bank, generate and troubleshoot code, compile updates, and even flag security issues independently before presenting their results for human review.
This approach allows semi-autonomous agents to handle lower-stakes, repetitive tasks independently, while reserving high-stakes decisions for human oversight, ensuring that quality control is maintained. This balance between autonomy and human-in-the-loop review highlights the potential of semi-autonomous agents to drive efficiency across business functions without compromising on critical decision-making.
Fully Autonomous Agents and Memory
Which takes us to fully autonomous agents: unlike semi-autonomous agents, which operate within structured boundaries and rely on prompts or human guidance, fully autonomous agents function independently once a task is initiated. They represent a significant advancement in AI, capable of perceiving, planning, acting, and learning across elaborate environments with minimal need for external input.
In short, while semi-autonomous agents require input when encountering unknowns, fully autonomous agents are designed to adapt dynamically to new and unpredictable scenarios, operating independently without human intervention.
What this all ladders up to is the emergence of the agentic enterprise — a cohesive, self-optimising network.
Autonomous agents do this by continuously learning from their surroundings, wherein they can refine their approaches and update their strategies based on past experiences, evolving to handle increasingly complex tasks with greater efficiency. This level of autonomy makes them ideal for applications where conditions and requirements are in constant flux, enabling them to respond in real-time and make decisions based on their accumulated knowledge.
While memory is a feature across all AI agents, autonomous agents rely heavily on it to retain and use knowledge from past experiences. This reliance on memory enables them not just to follow instructions but to adapt independently, learning from each interaction and evolving their strategies. Over time, this memory-driven learning process allows autonomous agents to handle increasingly complex and evolving tasks with greater skill, making them adept at navigating new situations and refining their approaches as they gather more insights.
To advance even further, some AI Agent systems benefit from tapping into external knowledge sources to address sophisticated queries and solve problems more effectively. This is where Retrieval-Augmented Generation (RAG) comes in, empowering agents to access real-time information for more precise, contextually relevant responses.
Integrating RAG into AI workflows enables these agents to engage in more analytical, planning and reasoning as they engage with diverse data sources. Known as Agentic RAG, this approach provides agents with the capacity to assess, prioritise, and strategically leverage information — much like a skilled researcher.
When memory and RAG are integrated, agents not only retain their own experiences but also gain the ability to draw on vast external data sources — enabling them to adapt fluidly within a networked system of intelligence.
What this all ladders up to is the emergence of the agentic enterprise — a cohesive, self-optimising network, where each agent contributes specialised expertise while drawing from the collective intelligence of the entire ecosystem.
The Agentification of the Enterprise
In the agentic enterprise, AI agents don’t operate in isolation — they must communicate and collaborate to truly unlock their potential. This is often achieved an agentic AI system architecture and intelligent multi-agent framework — such as LangGraph and ReAct — where agents independently handle specialised tasks across functions while coordinating to solve complex challenges.
This multi-agent system architecture enables agents to operate autonomously, handling distinct tasks across various functions while being ready to engage with human oversight when required.
In such an agentic system, each autonomous entity is capable of independent planning, reasoning, and tool usage. Agents maintain autonomy but collaborate through structured coordination to address layered challenges.
Effective inter-agent communication and distributed problem-solving are essential for creating responsive, adaptable multi-agent systems. Through multi-agent reinforcement learning (MARL), agents share real-time information — such as sensor data, actions, and episodic memories of past interactions — streamlining knowledge transfer and reducing redundant learning. This coordinated information-sharing enhances adaptability, allowing agents to make informed, collaborative decisions that drive efficiencies across functions.
Research on MARL highlights the compounding benefits of this approach. Studies show that when autonomous agents interact and share information, they naturally develop cooperative behaviours, even in competitive contexts. This continuous, collaborative learning allows each agent to build on the experiences and insights of others, creating a compounding knowledge effect. As a result, multi-agent systems become increasingly adept at tackling complex, real-world challenges, constantly adapting through interactions with their environment and each other. Their modular design further enhances resilience, allowing agents to evolve or gain new capabilities without disrupting the system.
With flexibility, scalability, and modularity as core advantages, organisations can update and scale individual agents seamlessly, ensuring that each component evolves independently within a resilient, adaptable infrastructure.
But what does this look like on the ground? What use cases and opportunities do multi-agent systems bring to the enterprise today? What types of agents could this modular ecosystem compose of?
Customer Service Agents
In the agentic enterprise, customer service agents act as strategic, conversational partners that enrich every phase of the customer experience. These agents, like Alaska Airlines’ travel assistant or Best Buy’s troubleshooting assistant, autonomously manage complex queries and adapt across channels, creating seamless, multi-platform engagement. By handling intricate interactions with minimal human oversight, customer agents elevate brand loyalty, increase customer satisfaction, and streamline operational efficiencies.
As we discussed in How to Design for Conversational AI, the implementation of safeguards like an “LLM-as-a-Judge” is crucial for creating reliable, brand-aligned and accurate AI experiences. This safeguard layer, a real-time LLM-based system, evaluates AI-generated responses for accuracy, relevance, and coherence, ensuring they meet stringent quality standards.
Ultimately, the agentic enterprise relies on such customer agents not only to reduce response times and operational costs but to fundamentally reshape customer relationships. Here, AI agents drive continuous engagement, learning from each interaction to enhance personalisation, anticipate needs, and ensure a unified experience that reflects brand values across every touchpoint.
Assistant Agents
Intelligent assistant agents in an agentic enterprise operate as essential collaborators, streamlining workflows and bolstering productivity across departments. With AI-driven agents managing routine tasks — such as scheduling meetings and setting reminders — employees can focus on strategic projects, fostering a work environment geared towards innovation and efficiency.
One example is how Bell Canada’s AI contact centre solutions support customer service teams by handling routine inquiries, and generating real-time conversation summaries, allowing live agents to dedicate full attention to customer needs.
These employee agents reshape team dynamics, not by replacing human input but by enhancing it.
Engineer Agents
In March 2024, Cognition introduced Devin, dubbed ‘the world’s first fully autonomous AI software engineer’, setting a new benchmark for code agents in development. Devin could reportedly streamline complex workflows, autonomously analyse code, troubleshoot errors, and optimise performance, giving developers the freedom to focus on creative and strategic tasks.
But Devin was not alone, across industries, companies are already harnessing similar agentic workflows and autonomous engineering agents to enhance productivity and code quality.
Wayfair has improved efficiency by using autonomous engineers to accelerate development setups and refine unit testing, while Best Buy’s code summarisation tool reduces call times and enhances developer focus on high-value work.
By implementing agentic workflows and streamlining processes such as setup, debugging, and code analysis, code agents reduce the manual burden on developers, enabling them to shift their focus to more creative and high-impact projects. Perhaps more importantly, they allow many companies to bring coding capabilities in-house, meaning that they are better equipped in creating their own enterprise software and solutions from the ground up.
Analysts Agents
RAG-powered analyst agents in an agentic enterprise serve as intelligent analysts, synthesising vast amounts of information to provide actionable insights. These agents operate across both internal databases and external data sources via methods such as RAG, delivering real-time answers and highlighting patterns that traditional methods might miss.
Companies like Bayer Crop Science harness data agents to transform agricultural data into precision-driven recommendations, boosting efficiency and sustainability in crop management.
Similarly, SURA Investments leverages AI to gain deeper insights into customer preferences, enhancing service personalisation and customer satisfaction. By bringing together comprehensive data processing capabilities, data agents make it possible for enterprises to act on insights swiftly, ensuring that decisions are not only data-driven but contextually relevant to evolving business landscapes.
Security Agents
Autonomous security agents in an agentic enterprise act as omnipresent guardians, continuously monitoring and responding to potential threats. These agents go further than detecting anomalies; they analyse patterns, learn from past incidents, and adjust responses based on evolving risks.
Apex Fintech deploys security agents that cut down threat analysis and response times from hours to seconds, allowing the company to manage risks proactively instead of reactively. By automating these critical functions, security agents enhance organisational resilience, safeguarding data integrity, ensuring compliance, and allowing human teams to focus on strategy.
Creative Agents
In an agentic enterprise, creative agents are instrumental in transforming content creation, personalising marketing, and enhancing customer engagement. Agents like those deployed by Puma generate localised product images and advertisements tailored to diverse markets, while Radisson Hotel Group uses AI to personalise ad campaigns, achieving faster production and higher engagement.
At the other end of the capabilities of creative agents, you have ideation, moodboarding, and concept development, where agents contribute to the early stages of creative work across various mediums (from image generation to 3D assets to video). By assisting in brainstorming and visual planning, creative agents provide inspiration and frameworks that can jumpstart the creative process.
Strategically, creative agents ensure brand consistency and adaptability across global markets by taking on repetitive creative tasks, allowing teams to focus on higher-value innovation.
The Agentic Enterprise
At Elsewhen, we envision a future where every company is an AI-driven, modular agentic enterprise, with autonomous and semi-autonomous agents embedded across every function. These modular agents — whether customer-facing, employee-supporting, code-optimising, data-analysing, or security-enforcing — work collaboratively, each contributing specialised expertise while drawing from shared intelligence and memory to achieve seamless coordination.
The system remains adaptable, with human-in-the-loop oversight when needed to guide complex decision-making and ensure alignment with strategic goals. This shift from isolated operations to an interconnected, agent-powered infrastructure will empower businesses to personalise customer interactions, enhance productivity, and make agile, data-informed decisions across the enterprise.
This is a vision that represents AI not just as a tool but as an essential, strategic partner, fostering agentic workflows and enterprise resilience in every area of the modern organisation.