India Agentic AI Market (2025-2030)

RESEARCH METHODOLOGY

The research methodology for the global agentic AI market report involved extensive secondary sources and directories, as well as reputed open-source databases, to identify and collect information for this technical and market-oriented study. In-depth interviews were conducted with various primary respondents, including agentic AI software providers, agentic AI service providers, individual end users, and enterprise end users; high-level executives of multiple companies offering agentic AI software & services; and industry consultants to obtain and verify critical qualitative and quantitative information and assess the market prospects and industry trends.

Secondary Research

In the secondary research process, various secondary sources were used to identify and collect information for the study. The secondary sources included annual reports; press releases and investor presentations of companies; product documentation; technical white papers; developer documentation; and certified publications such as Journal of Artificial Intelligence Research (JAIR), Autonomous Agents and Multi-Agent Systems, Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), Artificial Intelligence, IEEE Intelligent Systems, IEEE Transactions on Artificial Intelligence, ACM Transactions on Autonomous and Adaptive Systems, ACM Transactions on Intelligent Systems and Technology, and Machine Learning. Articles, technical papers, standards, and guidance from recognized associations, conferences, and government organizations were also referred to, including but not limited to the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Conference on Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR), AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), Linux Foundation, Agentic AI Foundation, National Institute of Standards and Technology (NIST), International Organization for Standardization/International Electrotechnical Commission (ISO/IEC), Organisation for Economic Co-operation and Development (OECD), and European Commission.
Secondary research was used to obtain key information about the industry’s value chain, the market’s monetary chain, the overall pool of key players, market classification and segmentation according to industry trends to the bottom-most level, regional markets, and key developments from market- and technology-oriented perspectives.

Primary Research

In the primary research process, a diverse range of stakeholders from both the supply and demand sides of the agentic AI ecosystem were interviewed to gather qualitative and quantitative insights specific to this market. From the supply side, key industry experts, such as chief executive officers (CEOs), vice presidents (VPs), marketing directors, technology & innovation directors, as well as technical leads from vendors offering agentic AI software & services, were consulted. The study also included system integrators, service providers, and IT service firms that implement and support agentic AI. On the demand side, input from IT decision-makers, product managers, and business heads of prominent enterprise end users was collected to understand the user perspectives and adoption challenges within targeted industries.

The primary research ensured that all crucial parameters affecting the agentic AI market—from technological advancements and evolving use cases (customer service & support, sales & revenue operations, marketing & customer engagement, IT operations & software engineering, data, analytics & business intelligence, cybersecurity & risk operations, etc.) to regulatory and compliance needs (GDPR, CCPA, Europe AI Act, AIDA, etc.) were considered. Each factor was thoroughly analyzed, verified through primary research, and evaluated to obtain precise quantitative and qualitative data for this market.

Once the initial phase of market engineering was completed, including detailed calculations for market statistics, segment-specific growth forecasts, and data triangulation, an additional round of primary research was undertaken. This step was crucial for refining and validating critical data points, such as agentic AI offerings (agentic AI software & services), industry adoption trends, the competitive landscape, and key market dynamics like demand drivers (Growing enterprise focus on measurable productivity and cost outcomes; Enterprise shift from assistive copilots to autonomous workflow execution; Embedding AI agents into core business platforms, reducing adoption friction; Advances in reasoning, tool use, memory, and orchestration expanding viable use cases), challenges (Securing agents with access to enterprise systems, data, and transaction rights; Defining accountability and human oversight for autonomous decisions and actions), and opportunities (Industry-specific agents built around regulated and high-value workflows; Agent orchestration, interoperability, and marketplace ecosystems; Governance, security, observability, and identity layers for enterprise-scale deployment).

In the comprehensive market engineering process, the top-down and bottom-up approaches, along with several data triangulation methods, were extensively employed to perform market estimation and forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed throughout the market engineering process to capture critical information/insights for the report.

Note: Three tiers of companies are defined based on their total revenue for the year ended 31st December 2025; Tier 1 companies’ revenue is more than USD 500 million; Tier 2 companies' revenue ranges between USD 500 million and 100 million; and Tier 3 companies’ revenue is less than USD 100 million
Source: MarketsandMarkets Analysis

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Market Size Estimation

The top-down and bottom-up approaches were employed to estimate and forecast the agentic AI market, as well as its dependent submarkets. This multi-layered analysis was further reinforced through data triangulation, which incorporated primary and secondary research inputs. The market figures were also validated against the existing MarketsandMarkets repository for accuracy.

Agentic AI Market : Top-Down and Bottom-Up Approach

Data Triangulation

The market was divided into several segments and subsegments after determining the overall market size using the market size estimation processes described above. To complete the overall market engineering process and determine the exact statistics for each market segment and subsegment, data triangulation and market segmentation procedures were employed, wherever applicable. The overall market size was then used in the top-down approach to estimate the size of other individual markets by applying percentage splits to the market segmentation.

Brand/Product Comparative Analysis

Geographic Analysis

Company Information