Tracxn has released its latest thematic report, “India and the Agentic AI Shift: Market Signals, Funding Trends, and SaaS Monetization,” highlighting how autonomous AI systems are beginning to reshape enterprise software economics, startup formation, and the future trajectory of India’s IT services sector.
The Agentic AI shift is emerging as a structural transition in how enterprises consume software, automate workflows, and rethink pricing and delivery models.
Recent market volatility coincided with Anthropic’s enterprise AI release featuring advanced “computer-use” capabilities that enable systems to independently execute professional workflows across tools and software environments.
Unlike traditional copilots that assist users, these agentic systems can autonomously complete multi-step operational tasks – from documentation and analytics to compliance and customer support – signaling a transition from user-operated software toward intelligence-operated execution.
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Agentic AI Shift: Market Signals and Technology Inflection
Anthropic’s enterprise release, powered by Claude Opus 4.6, introduced enterprise agentic capabilities including workflow execution, file organization, document drafting, enterprise software navigation, and operational task automation with user authorization.
The introduction of workflow plug-ins across productivity, sales, marketing, finance, data analysis, customer support, product management, and biology research further expanded enterprise automation potential.
A legal automation plugin capable of contract review, NDA triage, compliance checks, and legal brief generation became a symbolic trigger for market reaction.
Even with disclaimers requiring licensed attorney oversight, the release signaled AI’s movement beyond assistance into execution across regulated knowledge domains.
Public markets responded sharply as investors reassessed long-term automation risks. Indian IT services companies – including Infosys, TCS, Tech Mahindra, HCLTech, and Wipro – experienced valuation pressure as agentic systems demonstrated the ability to automate documentation, analytics, compliance, and customer operations workflows.
The repricing reflected concerns around potential reductions in billing volumes and slower headcount-driven growth.
Software vendors reliant on seat-based licensing models also faced valuation pressure as investors questioned whether AI agents could reduce enterprise dependence on human software users.
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Funding Trends Behind the Agentic AI Shift
The Tracxn report highlights that the current transition follows a multi-year capital buildup across global and Indian AI ecosystems.
Globally, AI funding has expanded dramatically over the past decade, reaching nearly $140B by 2025. Much of this investment flowed into model developers, compute infrastructure, orchestration frameworks, and enterprise deployment tooling – forming the intelligence layer required for autonomous enterprise systems.
India’s AI funding trajectory showed steady growth through the late 2010s, peaking at approximately $754M in 2021.
These investments helped build foundational enterprise AI capabilities across analytics, automation infrastructure, and deployment platforms.
Funding moderated afterward, declining to around $518M in 2022 and tapering between 2023 and 2025, but cumulative capital deployment enabled applied AI commercialization.
Post-2022, Agentic AI activity accelerated, with funding scaling to roughly $247M and increasingly flowing toward autonomous workflow agents, enterprise copilots, and orchestration platforms.
Startup formation has also accelerated, with more companies being architected around AI-native automation rather than traditional software interfaces.
Agentic AI Shift: Implications for India’s IT Services Industry
India’s IT services sector sits at the center of this transition due to its structural reliance on execution-layer knowledge work such as documentation, compliance, analytics, testing, and operational workflows.
Agentic AI introduces automation into precisely these service lines, challenging time-and-material billing structures and pyramid workforce models.
However, the disruption is not uniformly negative. According to the Economic Survey’s positioning referenced in the report, AI represents a general-purpose productivity technology capable of expanding economic capacity when deployed strategically.
India’s comparative advantage lies in application-layer deployment, enterprise integration, governance, and sector-specific AI solutions rather than frontier model development.
Constraints around compute infrastructure, energy requirements, and capital access make large-scale model training less viable domestically, while India’s engineering talent and digital public infrastructure support its emergence as an AI implementation hub.
The long-term labor impact is expected to reflect role reconfiguration rather than systemic displacement.
Execution-heavy roles in testing, documentation, customer support, and compliance processing face automation exposure, while demand increases for AI supervision, exception management, governance oversight, and regulatory validation.
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Agentic AI Shift: Monetization and Software Economics
Autonomous agents introduce new economic pressures for AI companies themselves. These systems require persistent reasoning, tool invocation, workflow memory, and screen interpretation, resulting in high inference costs at enterprise scale.
Investor expectations for returns have accelerated enterprise agent releases, workflow automation products, and commercial plug-in ecosystems. Revenue generation, enterprise contracts, and ROI realization are becoming central priorities.
As agentic systems scale, monetization models are evolving beyond traditional SaaS pricing toward diversified frameworks combining subscriptions, enterprise licensing, outcome-based pricing, and ad-supported access models.
Agentic AI Shift: Strategic Outlook
The report concludes that the Agentic AI shift represents a structural reordering of enterprise technology economics rather than a cyclical market event. Autonomous agents capable of executing workflows signal a transition from user-operated software to intelligence-operated systems.
For India, the agentic AI shift presents dual implications: margin pressure on outsourcing-based operational workflows alongside long-term growth opportunities in AI deployment, orchestration, customization, and governance services.
Funding is increasingly concentrating in compute infrastructure providers, model developers, and orchestration platforms that enable intelligence execution.
Meanwhile, application-layer software firms reliant on seat-based usage models face relative pressure unless supported by proprietary datasets, compliance integration, or system-of-record positioning.
