Transforming Industries with Vertical AI Agents
A recent study explored the pivotal role AI Agents will play in various industry verticals. It also introduced the concept of Cognitive Skills, designed to bridge the gap between standard architectures and industry-specific implementations, ensuring more tailored and effective AI solutions.
Why AI Agents?
AI Agents integrate context-awareness, real-time data and adaptability into workflows, enabling businesses to operate more effectively in complex environments.
These systems are designed to understand dynamic environments by incorporating real-time data, bridging raw data to actionable decisions, and adapting to unforeseen conditions or emerging trends, ensuring seamless and efficient operations.
Vertical AI Agents for Enterprises
As enterprises and industries encounter increasingly complex, domain-specific challenges, the limitations of general purpose, non-bespoke AI Agents have become apparent.
Vertical AI Agents have emerged as a bespoke solution, embedding tailored industry expertise into flexible, real-time systems.
By merging the adaptability of general systems with specialised knowledge, these AI Agents enable businesses to address unique challenges with precision and efficiency.
They fill the gap between generic technologies and the specific demands of modern industries, offering real-time responsiveness and custom problem-solving capabilities.
This evolution represents a significant shift in intelligent system design, allowing enterprises to streamline operations, enhance decision-making, and meet dynamic requirements with unparalleled effectiveness.
Vertical AI agents are tailored for specific industries, utilising domain-specific reasoning engines (LLMs) fine-tuned for specialised knowledge and workflows to address complex challenges effectively. ~ Source
This places significant emphasis on AI Agent development frameworks which allow organisations to build bespoke and specific AI Agents and also be able to ingest data and documents to act as a contextual reference.
Throughout this study, there has been special focus on RAG and Agentic RAG or RAG orchestration, as an avenue to incorporate organisational data and documents.
AI Agent Architecture
What Defines An AI Agent?
An AI Agent is software powered by one or more language models, especially large action models (LAMs), which enable the AI Agent to understand and address complex tasks.
Unlike traditional automation, an AI Agent can break down problems into sequential steps/sub-tasks, handling each individually.
Through iterative cycles of thought, evaluation, action, and observation, the AI Agent adapts its responses based on feedback.
AI Agents also utilise a range of tools for interacting with systems like APIs or web searches, with their effectiveness shaped by the variety of these tools, enabling them to handle diverse tasks and execute intricate workflows.
Each tool has a description in natural language, the AI Agent then matches the sub-task at hand with the tool description to know which tool to match with which sub-task.
Tools can include functionality like Web Search APIs, Data Retrieval APIs, Code Execution Environments, Browser Automation Tools, Natural Language Processing (NLP) APIs, File Management Systems, ACI-Tools, vision, etc.
Cognitive Skills
However, this study introduce a new module to the core building blocks of AI Agents called cognitive skills.
The aim of cognitive skills is to bridge the gap between pre-trained or fine- tuned reasoning, external tools for interacting with the environment, and new inference models.
This module ensures that AI Agents are equipped with purpose-built models tailored to specific tasks, enhancing their ability to operate effectively across various domains and challenges.
This module ensures that LLM agents are equipped with purpose-built models tailored to specific tasks, enhancing their ability to operate effectively across various domains and challenges. ~ Source
Tools
The Tools module provides the AI Agent with various tools to enhance its contextual and environmental awareness and interaction.
These tools help the agent access, retrieve and process information from different sources, ensuring its actions are informed, adaptive and aligned with operational goals.
For example…
Knowledge Retrieval Systems: Using Retrieval-Augmented Generation (RAG) to access structured and unstructured knowledge, integrating relevant domain-specific information.
Dynamic API Integration: Enabling interaction with live data, proprietary platforms, and external systems for real-time decision-making and adaptive responses.
Legacy System Interfaces: Connecting with traditional data systems, like relational databases, to incorporate historical data into current tasks.
Contextual Awareness Tools: Providing situational and environmental context to tailor the AI Agent’s actions to specific scenarios.
Agentic Systems Categories
- Task-Specific Agents
- Multi-Agent Systems
- Human-Augmented Agents
Task-Specific Agents
A Task-Specific AI Agent is an autonomous system built to perform a specific function or solve a narrowly defined problem within a particular domain.
These AI Agents serve as specialised components that efficiently handle individual tasks, contributing to larger systems.
Multi-Agent Systems
A Multi-Agent System is a network of autonomous agents that collaborate to solve interconnected problems or achieve common goals.
These systems function as distributed modules, coordinating and communicating to manage complex workflows with scalability and adaptability.
Depending on the application, the agents may share a common memory or operate with separate, isolated memories to enhance task execution.
Human-Augmented Agents
The autonomous nature of Agentic Applications has often been viewed as a threat and a barrier to the implementation of agents.
However, recently frameworks demonstrated how human oversight can be effectively integrated as checkpoints before tasks are executed, addressing these concerns.
Future Directions
The study expanded its focus to multi-agent and human-augmented agentic environments, highlighting how these advanced frameworks integrate vertical intelligence to transform software optimisation, design and automation.
With their architectural flexibility and diverse applications, agentic systems have proven their capacity to revolutionise industries by enhancing operational efficiency and intelligent decision-making.
This integration of agentic systems and vertical intelligence marks a paradigm shift in business approaches to software and automation, embedding contextual awareness and adaptability into intelligent AI Agents to enable unprecedented scalability, responsiveness and ethical innovation.
As industries confront increasingly complex challenges, Agentic Systems will be instrumental in shaping the future of intelligent workflows, unlocking groundbreaking opportunities for innovation and growth.
Key future directions for agentic systems include developing standardised frameworks to improve interoperability and scalability, expanding domain-specific intelligence for greater adaptability, advancing human-agent collaboration to enhance reliability and trust and addressing ethical and regulatory concerns to ensure responsible use.
By focusing on these priorities, agentic systems have the potential to revolutionise industries and address complex societal challenges, driving innovation and delivering meaningful benefits across various domains.
Embodiment
I love the picture below taken from Jensen Huang’s presentation at CES…were is currently making the leap from Generative AI to Agentic AI, the next stop is Physical AI.
Physical AI is also being referred to in studies as Embodiment.
This leap of AI Agents to embodiment involves AI Agents being the operating system of physical entities, enabling them to interact with the world autonomously through sensors and actuators, bridging the gap between abstract intelligence and tangible action.
Embodiment is not only cars, or general humanoid robots, but as I saw recently at a partner event in Milan, it will be eyewear, appliances and more…
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
