Automation 2.0 – The Rise of Intelligent AI Agents (Vol. 1)

Reasons to buy the ‘Automation 2.0’ market report:

• Innovation Insights: This report explores Agentic AI’s operational framework, comparative advantages, industry-wide impact, and real-world applications while analyzing trends, opportunities, adoption strategies, and challenges.
• Innovations: Presents real-world innovation examples related to agentic AI. It casts light on enterprises and startups using agentic AI across the sectors for use cases such as workflow automation, software development, and sales automation, among others.

How is our ‘Automation 2.0’ report unique from other reports in the market?

• Understand how ‘Agentic AI’ has emerged as a key enabler in today’s world, disrupting sectors from BFSI to healthcare and retail.
• While applications like workflow automation, real-time decision-making, and predictive analytics are leading the charge, areas such as personalized customer engagement, operational optimization, and compliance management are quickly catching up. Businesses can draw valuable insights from success stories in these areas, adapting proven strategies to achieve transformative outcomes tailored to their unique needs.
• Against this backdrop, enterprises need to understand which Agentic AI innovations are relevant to their sector and how companies are leveraging them to address efficiency, cost, and scalability challenges. The Automation 2.0 report, published by GlobalData, provides real-world examples of how leading companies and startups are advancing the adoption of cutting-edge Agentic AI solutions.

We recommend this valuable source of information to anyone involved in:

• AI Technology Leaders and Startups
• Business Development and Market Intelligence
• Investment Analysts and Portfolio Managers
• Professional Services – Investment Banks, PE/VC Firms
• M&A/Investment, Management Consultants, and Consulting Firms

To Get a Snapshot of the Automation 2.0 Report
Download a Free Sample Report

Automation 2.0 – Rise of Intelligent AI Agents Report Overview

Agentic AI redefines enterprise automation by integrating perception, reasoning, action, and learning to enable real-time adaptability and autonomous decision-making. The “Automation 2.0 – The Rise of Intelligent AI Agents (Vol. 1)” Innovation Radar report explores the evolution and impact of Agentic AI across industries such as BFSI, healthcare, retail, and energy. It highlights transformative use cases, including workflow automation, predictive analytics, and personalized customer interactions, showcasing innovations from various technology vendors.

GlobalData’s report provides strategic insights into adopting Agentic AI, emphasizing modular integration, workforce upskilling, and ethical governance. It examines the challenges and opportunities in deploying these systems, from reducing operational costs to enabling faster product development. The report equips enterprises to drive efficiency, scalability, and innovation in a rapidly evolving digital landscape by offering actionable guidance on leveraging Agentic AI.

Buy the Full Report for More Insights on Automation 2.0 – The Rise of Intelligent AI Agents (Vol. 1)

Download a Free Report Sample

Agentic AI - Key Features

Agentic AI refers to autonomous systems that perceive, reason, act, and learn independently, proactively pursuing goals with real-time adaptability. These systems enhance decision-making, enable efficient collaboration, and are classified based on reflex, goal orientation, utility, learning, and hierarchical capabilities. A few of the key features of agentic AI are autonomy, goal-oriented behavior, adaptability, decision-making, and independent operation.

Autonomy: Operates independently, making decisions and executing tasks without human intervention.

Goal-Oriented Behavior: Pursues predefined objectives using techniques like reinforcement learning and evolutionary algorithms.

Adaptability: Dynamically adjusts behavior to environmental changes or novel situations.

Buy the Full Report to Know More Features of Agentic AI

Download a Free Report Sample

Agentic AI – Market Dynamics

The data flywheel in AI refers to a feedback loop where continuous use generates data that refine models and algorithms, improving performance and outcomes. This iterative process enhances scalability and efficiency in Agentic AI systems. Agentic AI automates processes, generating real-time feedback loops to enhance reinforcement learning and system performance. Operationalizing AI agents is challenging due to expertise and infrastructure gaps. Solutions like NVIDIA’s NIM Agent Blueprints offer customizable workflows.

Data Flywheel Process for Agentic AI

Buy the Full Report to Know More about Agentic AI
Download a Free Report Sample

Agentic AI - Emerging Concepts

BabyAGI and AutoGPT are among the concepts emerging within the Agentic AI theme.

BabyAGI: This open-source Python framework autonomously creates, prioritizes, and executes tasks by leveraging AI in a context-aware environment. BabyAGI operates in a continuous loop by pulling tasks from a list, executing them via agents, and generating new tasks based on outcomes.

BabyAGI Workflow

Buy the Full Report to Know More about the Emerging Concepts of Agentic AI

Download a Free Report Sample

Agentic AI - Competitive Landscape

A few of the key innovators by business function/application area within the Agentic AI theme are Commerce.AI, Origami Agents, Microsoft, Workday, and IBM.

Commerce.AI’s auto-AGENTS automate business interactions across voice, chat, and text, boosting productivity and streamlining processes. Meanwhile, Origami Agents streamlines lead generation and business research with AI-driven insights from web data, firmographics, and social intent signals.

Agentic AI Industry Analysis by Players

Buy the Full Report to Get More Player Insights into the Agentic AI Theme

Download a Free Report Sample

Segments Covered in the Report

Agentic AI Classifications Outlook

• Simple Reflex Agents
• Goal-Based Agents
• Learning Agents
• Multi-Agent Systems
• Model-Based Reflex Agents
• Utility-Based Agents
• Hierarchical Agents