The ever-evolving nature of AI brings new capabilities and opportunities for intelligence, insights, and automation. Modern companies need an AI strategy that can adapt to these constant changes, and this strategy should be grounded in robust data and supported by secure, stable integrations.
Composability is a crucial element in adapting to and leveraging new AI advancements – but what is composability, and how does it contribute to successful AI implementation? Let’s start with defining what composability means and how it helps organizations keep pace with a constantly changing AI landscape.
What is composability?
Composability is the capability to create modular, reusable components that can be rapidly assembled to form new customer experiences or business solutions. It’s a fundamental concept to the idea of a composable enterprise, wherein businesses can quickly adapt and innovate by leveraging a combination of pre-built and custom components.
It involves integrating various systems and data sources through APIs, automation, and other tools to create a flexible and scalable architecture, helping organizations reduce complexity by breaking down monolithic systems into manageable, interconnected modules.
Composability essentially means reusing what you’ve already built so you don’t have to start from scratch. It improves efficiency by allowing organizations to reuse components to streamline development processes and eliminate the need to repeatedly build functionalities from the ground up.
The ongoing evolution of AI
The changes in AI capabilities can be divided into four distinct waves: predictive, generative, autonomous agents, and artificial general intelligence (AGI). Each wave represents a progressive step in AI capabilities and the level of human intervention required.
- The first wave, predictive AI, focuses on forecasting outcomes based on data. This stage requires human input to analyze and interpret the predictions, ensuring they are applied effectively.
- The second wave, generative AI, advances by generating content or responses on behalf of the user, reducing the need for human involvement in the creation process.
- As we move into the third wave, autonomous agents, AI systems begin to act independently, performing tasks on behalf of humans. This stage demands even less human intervention as the AI systems become more proficient in executing actions autonomously.
- The final wave, artificial general intelligence (AGI), represents AI systems that can operate almost entirely on their own, extending their capabilities independently. AGI requires minimal human input and can learn and adapt to various tasks and environments.
Each wave of AI development necessitates a foundation of data, insights, and the ability to integrate this information. A primary difference between the waves is the extent of human involvement needed to achieve outcomes. Predictive AI relies heavily on human input, while AGI functions with minimal intervention.
As AI systems need less human input, they require more advanced training in skills such as best practices, anomaly detection, processes, and subject matter expertise.
With this advancement, they build upon existing skills, allowing them to apply capabilities to various tasks without needing to relearn everything from scratch. This ability to leverage previously developed skills is the core purpose of composability in AI.
3 ways composability helps users adapt to new AI waves
With a composability approach, every action is supported by an API that serves as a modular component that helps developers accelerate the building of new features and services.
1. Compose human skills into reusable actions
Organizations can upskill their AI systems to work effectively with various systems and applications by teaching AI to perform tasks that humans can do. This involves understanding processes, employing best practices, detecting issues, grasping context, and ensuring security. For successful AI transformation, providing AI with a comprehensive context about processes and the ability to operate across different systems and architectures is essential.
2. Implement custom actions using an AI skills framework
Human workers often navigate the fragile architecture of legacy systems, custom coding, unstructured data, and external partners. While humans can manage manually in this environment, their way of working isn’t ideal for AI adoption.
Composability helps create a framework wherein AI can perform human-like actions through reusable components, or “Lego bricks,” that can be combined in various ways at the system or application layer.
The key components of this framework include a robust data foundation like Salesforce Data Cloud, a generative AI framework with a conversational user interface like Agentforce, and an AI skills interface that leverages connectivity and integration with a solution, like MuleSoft’s Anypoint Platform.
MuleSoft also facilitates the implementation of custom actions with tools like robotic process automation (RPA) and intelligent document processing (IDP), which help eliminate many of the repetitive, tedious tasks that humans normally handle, such as incident reporting, refund processing, customer communication, and more.
3. Package reusable actions as APIs
Once skills are composed into reusable actions, they can be packaged as APIs and shared in environments like Anypoint Exchange. In this marketplace, available APIs and multiple AI agents can access and upskill through an API or a human engineer. This allows organizations to enhance their AI capabilities by leveraging a wide range of skills and actions, ensuring AI systems are continually evolving and capable of performing diverse tasks.
Prepare for AI with MuleSoft
MuleSoft’s platform allows organizations to create composable architectures primed for adapting to new waves of AI capabilities. Learn more about the best practices for underpinning AI capabilities so your team can amplify the impact of AI across your organization in our whitepaper, Blueprint for Implementing AI.
