Agentic AI: Strategic Intelligence

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Published: August 27, 2025 Report Code: GDTMT-TR-S654-TH

Report Overview
Key Players
Table of Contents

Able to communicate and collaborate, AI agents are being developed for various consumer, enterprise, scientific, and industrial purposes. Highly complex environments, such as industrial plants or hospitals, might be served by multiple AI agents, each with a different purpose. According to a June 2024 Capgemini survey of 1,100 large enterprises, one in 10 organizations is deploying AI agents, with more than 50% planning to explore their use in the next year.

Scope

Agentic AI refers to advanced AI systems that act autonomously, making decisions and taking actions with limited or no human supervision. An AI agent is a software program that interacts with its environment, collecting data to perform specific tasks, answer questions, and automate processes for users.

This report provides analysis of this emerging AI technology, including an overview of how AI agents work, guidance on how enterprises can implement agentic AI, and agentic AI use cases by industry.

Key Highlights

Leading tech companies are building upon existing AI assistants by creating platforms and tools to support developers. Specialist development companies are emerging with the expertise to work with large language models (LLMs) and the various machine learning models needed to implement AI agents. Agentic AI is expected to shape the future of DevOps (the set of practices around software integration and delivery).

Combined with the ability to interact with both LLMs and the external environment, AI agents are empowered to execute more general-purpose work. Retrieval-augmented generation (RAG) is a design approach that aims to make LLMs more reliable by automatically retrieving information from external knowledge bases. It can ensure more accurate and contextually relevant outputs.

Reasons to Buy

Although it is still early days for agentic AI, initial best practices are starting to emerge that can help enterprises that choose to develop their own agentic AI strategies or collaborate with agentic AI vendors. This report includes simple but essential steps that will help to streamline the creation and adoption of agentic AI into existing processes.

Ada
Adept.ai
Affineon
Agenttech
AIQ
Amazon
AMD
Anterior
Anthropic
Avantia
Automation Anywhere
AWS
Axanol AI
Bizdata
Boosted.ai
ChatDev
Cisco
Cohere
Composable
Concourse
Connex AI
Context.ai
Counterpart
Crew.ai
Databricks
DataRobot
DeepSeek
Deviniti
Didero
Elastic
Ema
Ericsson
Finnomena
Fourkites
Ghostwriter
Google
Gretel
H Company
HappyRobot
Harvey
HCL Tech
HSO.ai
Harness
Hippocratic.ai
IBM
Indem
Indico Data
Innovaccer
Intuit
K Health
LangChain
Legora
Luminance
Lumber
Kanerika
Maisa
Meta
MetaGPT
McGill
Microsoft
Mistral
NASA
Netdocument
Neutrinos
Nokia
Norm AI
Nvidia
Nuclia
Open AI
Optica
Oracle
Pando
Perplexity
Procore
Project 44
Rapidminer
Relevance AI
Salesforce
SAS
Servicenow
Shopsense
Spellbook
Synthpop
Sysdig
Talkdesk
Thoughtful.ai
Toyota
Twin Knowledge
Trunk Tools
University of Zurich
Vercel
Vida
Vstrom
Zapier

Executive Summary

Agentic AI: The Journey Begins

How Do AI Agents Work?

An Evolving Ecosystem

How Enterprises Can Implement Agentic AI

Agentic AI Use Cases by Industry

Case Studies

Challenges for the Future

Glossary

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