Sentius White Paper
Executive summary
- Generative AI and large language models enable AI agents to reason, plan, and execute complex tasks autonomously, surpassing the limitations of traditional automation and Symbolic AI.
- By adopting autonomous agents, organizations can offload routine tasks to AI, allowing humans to focus on strategic, creative, and empathetic roles, thereby creating a new type of organization – Agentic Enterprise.
- The Sentius Teach & Repeat Platform empowers organizations to create autonomous AI agents that learn and replicate company-specific processes and knowledge — using tools like the Task Recorder, Multi-Agent Systems, Knowledge Graphs, and specialized agents — enhancing efficiency, reliability, and adaptability in business operations.
Humanity's cooperative evolution has continually pushed the boundaries of what's possible — from the pyramids to space stations, our drive to build and innovate has only intensified. Today, we enter another transformative era, driven by artificial intelligence. AI agents are emerging as a revolutionary force, pushing the development of the Agentic Enterprise — a new organizational paradigm enabling companies to thrive amid a rising wave of disruption and unprecedented opportunities.
In 2023, Bill Gates predicted that AI agents would reshape the tech industry within five years. Recently, Vinod Khosla forecasted that AI would perform 80% of the work in 80% of jobs. This vision is already materializing as organizations integrating AI are five times more likely to make fast, data-driven decisions. As of early 2024, 72% of business leaders report high productivity due to extensive AI integration. And this is just the beginning. The global AI market is expected to exceed $1.8 trillion by 2030, with AI agents projected to increase global GDP by 26% in the same timeframe. Adaptability is now critical; organizations leveraging AI agents will endure and flourish, while those that resist risk fading into obscurity.
Evolution of Work
From Lever to Digital Enterprise
Throughout history, we have adapted our environments, accumulated knowledge, and solved increasingly complex challenges. Ancient innovations like the lever allowed us to accomplish feats previously unimaginable. And each technological breakthrough — from stone tablets to the specialization of labor — has acted as a lever for progress.
Money, invented about 7,000 years ago in Mesopotamia created infrastructure for scaling human cooperation beyond a neighborhood. In ancient Egypt, the construction of the pyramids show the power of coordinated specialization: stone quarrying, transportation, and assembly were organized into an efficient system. This capability to divide labor laid the groundwork for larger, structured organization. Centuries later, the industrial loom brought a comparable shift, drastically reducing costs and creating global shifts in the production and trade of raw materials. These innovations, while boosting efficiency, have consistently introduced new layers of complexity to human labor structures.
The Information Age marked a defining shift in work structures. Acceleration of information flows within organizations made them more efficient. Bill Gates envisioned the "digital nervous system" carrying information flows across companies. Today, platforms like Microsoft 365 and Google Workspace have made this vision a reality, enabling organizations to operate "at the speed of thought."
However, cognitive overload remains a limiting factor. As technology advances rapidly, only those will win, who will delegate increasingly complex tasks to machines. The "ChatGPT moment" in 2022 marked a significant milestone, where assigning cognitive tasks to AI on a personal level — once considered science fiction — became a practical reality. Today, the capabilities of Generative AI enable individuals to achieve higher productivity and adaptability. This leap forward brings us closer to the vision of the Agentic Enterprise, where not just everyone has their own AI assistant, but a hybrid workforce of humans and autonomous agents collectively tackles organizational challenges.
Waves of Automation: From RPA to GenAI
The evolution of automation in modern organizations has unfolded in waves. Standardized software made document creation, calculations, and data sharing routine. Custom software enabled companies to address unique challenges, though it required engineering support. Robotic Process Automation (RPA) added further autonomy to business processes but was often cost-prohibitive and fragile.
Tasks within an organization vary: some can be performed by machines with minimal cognitive input, while others require complex coordination across multiple roles. Consider customer service in logistics. To book a flight or ship goods internationally, a company must coordinate between departments, consider tariffs, schedule transport, and manage legal compliance. Each step requires specialized expertise and the capacity to adjust to changing circumstances. While standardized processes exist, they inevitably involve variability and necessitate real-time adaptability.
Customer experience managers, for instance, are entrusted with overseeing client interactions, ensuring quality, and managing exceptions. Their role requires autonomy and a degree of "agency" to adapt to each scenario. As Generative AI continues to mature, these capabilities will extend to autonomous agents, capable of independently managing complex workflows across teams and departments. This is the foundation of the Agentic Enterprise.
The Rise of the Agentic Enterprise
Generative AI has unlocked the potential for autonomous agents to perform actions needed to achieve specific goals. Unlike prior RPA systems limited to predefined responses, these agents will be able to interpret dynamic situations, performing sequences of actions that span people, teams, and organizations. This independence will enable them to assist in tasks from gathering insights to interacting with systems on behalf of their human counterparts.
Consider a browser agent capable of autonomously navigating and interacting with websites, a prompt agent transforming unstructured data into insights, or a document agent with long-context capabilities to answer questions within procedural guidelines. Each agent brings unique capabilities, supporting faster, more informed decision-making while easing cognitive burdens on human team members.
Over time, these agents will collectively reshape organizational workflows. Teams of autonomous agents can collaborate much like human specialists, delivering tailored solutions in complex situations. As this AI workforce grows, so will organizational efficiency, scalability, and resilience. Humans will shift into more strategic roles, focusing on creativity, innovation, and empathetic decision-making, while agents handle routine but vital tasks. This heralds a transformation of traditional structures into what we can call an "Agentic Enterprise."
The Agentic Enterprise represents more than just an evolution in technology; it's a fundamental shift in how we perceive work and collaboration. By integrating autonomous agents into our operations, we unlock unprecedented levels of efficiency and adaptability. These agents will democratize expertise, making high-level skills and knowledge accessible across all tiers of an organization.
This transformation will redefine what it means to work and, ultimately, what it means to be human. Freed from routine tasks, we can focus on pursuits that inspire us, fostering a society that values creativity, empathy, and shared success.
Sentius Agentic Enterprise Stack
Teach & Repeat Platform
While autonomous AI agents are being built based on the ability of Generative AI to autonomously reason, plan, and act, existing large language models were not trained on organization-specific information like processes, best practices, and policies. The latest GenAI models are already smart enough to solve microtasks in many businesses, but to facilitate a smooth transition from human-led to agent-led action execution, we believe that a mechanism for teaching our autonomous agents the organizational knowledge is essential. This "Teach & Repeat" methodology ensures that agents are not just intelligent but also know "how things are done" and are aligned with the unique operational paradigms of each enterprise.
The Teach & Repeat platform functions by first employing a Task Recorder to observe and capture how an employee performs a specific task. Based on this recorded behavior, instructions for agents are created to mimic the employee's solution for that task. These instructions are then integrated into the Multi-Agent Systems & Workflow Engine, allowing it to be seamlessly merged into business processes and reused for similar tasks in the future. Furthermore, the organizational knowledge gained during the interaction with the user is stored in Knowledge Graphs and Dynamic Ontology, creating an organizational memory that enhances future performance and adaptability. Finally, a combination of Guardrails and Policies ensures that agents perform within the requirements of the organizations and only execute explicitly allowed actions.
Let's explore the essential elements of the Sentius Teach & Repeat Platform that bring the Agentic Enterprise vision to fruition.
Task Recorder
The Task Recorder observes and analyzes user interface activity and application logs. It generates workflow configurations and agent instructions, effectively learning from human actions to automate future tasks. This not only accelerates the automation process but also ensures that the agents are tailored to the specific workflows of the organization.
Multi-Agent Systems and Workflow Engine
Our platform supports multi-agent systems that can collaborate to execute complex workflows. Workflows can be composed manually or generated and can be further automatically optimized for a given task. The Workflow Engine orchestrates these agents, managing dependencies and optimizing task allocation as well as healing entire workflows when unexpected changes come. This mimics human teams working in unison, but with the added benefits of machine efficiency and scalability.
Knowledge Graphs & Dynamic Ontology
Organizations maintain structured representations of their essential business data—whether about products, customers, orders, or other key entities. To reduce AI hallucinations and ensure decision-making transparency, we anchor LLM responses to these data representations, forming knowledge graphs that allow clients to retain full control over their information. By combining knowledge graphs with dynamic ontology, we provide our agents with not only raw data but also a contextual understanding of relationships, hierarchies, and business-specific semantics within the information. This layered approach boosts reasoning capabilities, enhancing decision-making processes and making AI outputs more reliable and explainable.
No-Code Tools
To democratize access to AI automation, Sentius Teach & Repeat Platform offers no-code tools. Users without technical backgrounds can design, deploy, and manage agents, fostering innovation and enabling rapid adaptation to changing business needs.
Guardrails and Policies
The importance of having AI agents perform under strict control cannot be overstated, as it ensures that their actions are aligned with ethical standards and user expectations. By being guided by well-defined guardrails and steered using human-formulated policies, AI systems can operate safely and effectively, reducing the risk of errors or misuse. For instance, consider a policy that prohibits AI agents from transferring money while interacting with a banking app unless they have explicit user authorization. Such a policy ensures that sensitive financial transactions are conducted only with the user's direct consent, preventing unauthorized access and potential fraud. This not only protects users' financial security but also builds trust in the AI systems, demonstrating a commitment to responsible and transparent AI usage.
Conversational AI Orchestrator
The Conversational AI Orchestrator enables people to interact with the platform's components in a natural, conversational manner. It includes guardrails that have been battle-tested in Amazon Alexa Prize competitions, ensuring safe and effective communication between humans and agents. This fosters a more intuitive user experience, lowering the barriers to adopting AI solutions within the organization.
Sentius Agents
Browser Agent
The Browser Agent understands tasks defined in natural language and performs them on the web via the user's web browser. Imagine delegating routine online tasks—such as data entry, information retrieval, or even complex transactions—to an agent that executes them flawlessly and efficiently. This not only saves time but also reduces the potential for human error.
Prompt Agent
The Prompt Agent is designed to execute lightweight AI tasks on input data. A basic example includes sharing pre-existing knowledge, such as common-sense information about well-known products and services. The Prompt Agent can provide results either as text or in a structured format adhering to a specified data schema. This agent acts as a bridge between raw data and actionable insights, enabling faster decision-making processes.
OpenAPI Agent
The OpenAPI Agent uses natural language instructions to connect with internal and external APIs. This enables seamless integration across various platforms and services, breaking down silos and fostering interoperability. By translating human intentions into machine-executable actions, the OpenAPI Agent simplifies complex integration tasks that traditionally require specialized engineering expertise.
Document Agent
The Document Agent is engineered to answer in-domain questions by reasoning and connecting multiple facts over very large contexts. Utilizing Sentius' Associative Recurrent Memory Transformer technology, it demonstrates successful processing of tasks up to 50 million tokens, and it's potentially scalable to any length. It can be deployed in-house or on a private cloud, ensuring confidentiality and security. This agent revolutionizes how organizations handle vast amounts of textual data, making information retrieval and analysis more efficient than ever before.
Other Agents: Desktop, Code, SQL etc.
In addition to the core agents described above, we are actively developing and integrating top-tier autonomous agents that specialize in manipulating desktop applications, managing SQL databases, and generating code. These agents enhance our platform's versatility, making it capable of addressing a diverse array of tasks with high efficiency and precision. By automating interactions with desktop software, optimizing database operations, and facilitating code generation, we ensure that our solutions can seamlessly adapt to various business needs. Our commitment to innovation and excellence drives us to continually expand our suite of agents, ensuring our clients benefit from cutting-edge technology that streamlines operations and fosters productivity.
Custom Action Models
Reliability is paramount for enterprise systems. To achieve robust yet adaptive workflow automation, we are developing action models for target domains. As organizations start to use the Teach & Repeat platform, they begin to accumulate business know-how and other organizational knowledge. We are developing training methods to convert this accumulated knowledge into company-specific business action models. These in-domain action models will improve prediction quality and enhance the reliability of business process automation. Moreover, these models will be updated continuously and evolve alongside the company's business processes. This continuous evolution allows agents to replicate user behavior accurately, ensuring consistency and reliability in task execution.
AI Agents vs RPA
Workflow automation systems have been around for decades, even before the Generative AI era. These systems, influenced by Symbolic AI, relied heavily on explicitly defined rules and symbolic representations to perform computations and solve problems. While they offered high precision and reliability, they demanded extremely high accuracy in the information entering and exiting the automation systems. This rigidity made them less adaptable to the dynamic, real-world environments where data is often semi-structured or unstructured.
Generative AI paradigm shift allows systems to understand and handle semi-structured and unstructured information. Unlike the traditional Symbolic AI approach, Generative AI-powered systems can interpret nuanced data, though this comes with higher computational costs. At Sentius, our core philosophy revolves around harmonizing Symbolic AI with Generative AI and finding an equilibrium between human involvement and autonomous agents.
We advocate for equipping modern organizations with hybrid tools that leverage the strengths of both methodologies. For instance, when we can attain a level of accuracy ideal for Symbolic AI solutions, we utilize it for efficiency. Conversely, if processes driven by Symbolic AI falter due to changes, we harness the adaptability of Generative AI to dynamically fix these processes. This symbiotic relationship ensures that we maintain high reliability while also being agile enough to adapt to new challenges.
The Future is Agentic
The Agentic Enterprise represents more than just an evolution in technology; it's a fundamental shift in how we perceive work and collaboration. By integrating autonomous agents into our operations, we unlock unprecedented levels of efficiency and adaptability. These agents will democratize expertise, making high-level skills and knowledge accessible across all tiers of an organization.
This transformation will redefine what it means to work and, ultimately, what it means to be human. Freed from routine tasks, we can focus on pursuits that inspire us, fostering a society that values creativity, empathy, and shared success.
