India’s Pursuit for Global South AI Leadership through Agentic AI Systems: Navigating Intellectual Property Challenges

Written by: Dr. Ajai Garg and Siddhant Chamola

As Artificial Intelligence has now emerged as a disruptive ecosystem reshaping Global economy,  India’s vision for a sovereign space and a Global South leadership position mainly through our deep tech startups faces stiff challenges.

Whereas, talent and a large market is our biggest strength but large scale risk capital,  infrastructure and a strategy around leveraging Intellectual property are challenges we still struggle to navigate. Because of these challenges though we do not own a significant LLM but can still play a significant role if we plug our shortcomings in a fast evolving Agentic AI ecosystem.

Agentic AI combines the reasoning capability of LLMs with other capabilities, such as memory, planning mechanisms, use of tools, and decision-making frameworks, allowing for an AI agent to decompose large goals into smaller activities, determine the most efficient path to achieving the goal, interact with external systems, and continuously assess its progress.

Agentic AI applications solve digital fragmentation by connecting disconnected systems and automating complex business decisions that previously required human intervention. While Generative AI models respond to prompts; agentic AI decides what to do next. These systems can define problems, gather data, design experiments and iterate toward solutions with minimal human input.

Most organisations struggle with isolated software applications that cannot share data or coordinate workflows. Agentic applications bridge these gaps by accessing multiple data sources simultaneously and making decisions based on complete information rather than partial views.

Agentic AI applications vary based on data sources, decision complexity, and automation objectives, with each area requiring different levels of human oversight and system integration.

In under two years, over 120 startups have emerged across India with a focus on building agentic AI systems—tools designed not just to understand prompts but to take autonomous actions. From assistants to workflow agents, the promise looks strong, but the reality is different: consumers aren’t showing up. Despite a nation of 750 million smartphone users, adoption of consumer-facing AI agents remains negligible, with most traction still coming from enterprise use cases. Players like  Krutim, Fractal, Sarvam, Puch AI, and nani.AI have rolled out personal assistants, voice bots, and image generators aimed at India’s mobile-first population. Krutrim’s Kruti, for instance, promises to book cabs, order food, and generate images, while Fractal launched tools like Kalaido and Vaidya, and Gnani’s Inya AI enables plug-and-play agents. Yet, many of these remain proof-of-concept apps with little data on users, retention, or monetisation.

Some of the use cases include:

IT Service Management

Using conversational tools to create more informative tickets, software provisioning, incident detection, VPN troubleshooting, device monitoring.

Security Operations

Network monitoring, threat detection, automated incident response, vulnerability scanning, compliance reporting.

Sales and Marketing

Lead scoring, pipeline management, campaign automation, customer segmentation. Mention Boomi’s integration with Amazon Q Business for grounding agents in company knowledge bases.

Customer Service

Ticket routing, case management, knowledge base automation. An Australian Red Cross example: scaled from 30 to 300,000 incidents/day during wildfire emergencies in under 24 hours.

Engineering and Development

Code generation, integration and deployment monitoring, bug prioritisation, documentation automation, testing workflows.

Operations and Supply Chain

Inventory management, demand forecasting, route optimisation, supplier monitoring, warehouse automation.PN troubleshooting, device monitoring.

Human Resources

Resume screening, benefits administration, onboarding workflows, PTO tracking, employee data updates.

Financial Operations

Expense report automation, compliance monitoring, invoice processing, payment approvals.

Agentic AI and Intellectual Property Risks

Agentic AI and Intellectual Property Risks are becoming a critical concern for enterprises adopting intelligent automation at scale. As organisations leverage enterprise-ready solutions, the creation, deployment, and orchestration of autonomous agents introduce new legal and compliance challenges. Intellectual property (IP) frameworks were not designed initially to address generative and agentic AI, making it essential for businesses to understand how copyright, patents, and trade secrets are impacted in this new context.

The rapid adoption of Agentic AI and Buisness Workflows, from knowledge automation to customer engagement and product design—raises questions about ownership, licensing, and liability. For instance, who owns content generated by autonomous agents? How should enterprises protect proprietary datasets and models used to train these systems? Without clear strategies, organisations risk disputes, regulatory penalties, and reputational damage. Addressing IP risks in agentic AI adoption requires a proactive approach that balances innovation with compliance.

To navigate this landscape, businesses must combine decision intelligence, context-first agentic workflows, and strong IP governance frameworks. Companies can ensure compliance while accelerating innovation by integrating IP protection into the agent orchestration process. This blog explores the key intellectual property challenges of agentic AI, strategies to mitigate risks, and best practices for building secure, compliant, and future-ready AI ecosystems.

Intellectual Property Challenges with Agentic AI

Intellectual property (IP) has long been the foundation of protecting innovation, creative works, and proprietary technology. However, the rise of Agentic AI platforms is reshaping how IP is created, shared, and enforced. Unlike traditional automation, Agentic AI agents can generate original content, designs, and code, raising questions about who owns the rights and how businesses can protect their assets. The shift requires governance models that integrate compliance, ownership frameworks, and risk mitigation directly into enterprise operations.

1. Ownership of AI-Generated Content

A central concern is the ownership of outputs generated by autonomous agents. When Agentic AI workflows create text, images, or software code, determining the rightful owner—whether the enterprise, AI provider, or end-user—becomes complex. Inconsistent global IP laws add further challenges, with some jurisdictions recognising AI-generated works as protectable, while others restrict ownership to human creators.

2. Copyright Infringement

AI-driven workflows often rely on large-scale datasets that include copyrighted material, licensed content, or proprietary media. Without robust compliance measures, outputs risk resembling or reusing protected works, opening enterprises to infringement claims. For organisations leveraging agentic AI, dataset validation, metadata tagging, and automated compliance checks are essential to ensure copyright-safe content generation across marketing, product development, and enterprise knowledge systems.

3. Patentability of Agentic Innovations

When autonomous agents design solutions, draft algorithms, or simulate prototypes, questions arise about patent eligibility. Current patent systems are designed for human inventors, creating uncertainty about whether inventions generated through decision intelligence agents qualify for protection. Enterprises need clear strategies for documenting human involvement to strengthen patent claims.

4. Trade Secrets and Data Security

Agentic AI thrives on data-driven insights. Yet, when proprietary datasets and business-sensitive information are fed into autonomous workflows, the risk of leakage or unauthorised use increases. Protecting trade secrets requires secure infrastructure, encryption, and policies.

5. Liability and Accountability

Determining accountability becomes challenging if an AI agent generates infringing content or violates third-party rights. Enterprises must establish contractual frameworks with solution providers and build audit trails within orchestration pipelines to define responsibilities clearly.

Regulatory Landscape for Intellectual Property and AI

The global regulatory environment is evolving to address AI-specific IP challenges. The European Union’s AI Act, U.S. Copyright Office guidelines, and World Intellectual Property Organisation (WIPO) consultations all highlight growing scrutiny on AI-generated content. For businesses, this means adopting Agentic AI solutions without compliance strategies, which exposes them to legal and financial risks. It is imperative that legal support is sought early for compliance monitoring so that organisations can ensure intellectual property rights are respected at scale.

Strategies to Mitigate IP Risks in Agentic AI

Embed Governance into Orchestration Frameworks

Rather than treating compliance as a separate function, organisations should embed IP protection as part of the Buisness strategy itself. This ensures that every autonomous workflow—whether generating marketing content, analysing data, or developing software—adheres to legal requirements from the start.

Establish Clear Ownership Policies

Enterprises must define policies on AI-generated outputs, specifying whether rights belong to the organisation, customers, or partners.

Audit and Curate Training Data

To minimise copyright infringement risks, companies should implement data lineage tracking and dataset auditing. This includes transparent data curation methodologies, reducing reliance on unverified third-party sources.

Strengthen Trade Secret Protection

Protecting proprietary datasets requires encryption, access controls, and isolation of sensitive information.

Integrate Legal Teams into AI Workflows

Legal and compliance experts must play an active role in the adoption of Agentic AI. Embedding them into orchestration processes allows real-time validation of outputs, patents, and licensing agreements—minimising disputes and strengthening enterprise resilience.

Some Business Use Cases Impacted by IP Risks: Understanding Agentic AI IP risks is essential when deploying intelligent automation at scale.

1. Marketing and Content Automation

Enterprises using Agentic AI for market campaigns can face copyright risks when generating ad creatives, blogs, or product visuals. With proper audits, businesses can ensure automated content is cross-checked against licensing databases before publication. Addressing the Intellectual property challenges of Agentic AI requires appropriate governance.

2. Product Design and Prototyping

The article therefore addresses the wider Intellectual property challenges of Agentic AI.

Autonomous agents that design prototypes or optimise manufacturing processes may generate patentable innovations. Companies need frameworks to document human contributions, enabling stronger patent filings. Clear documentation can also support analysis of Agentic AI and patentability.

3. Knowledge Automation and Enterprise Search

This also raises important questions around Agentic AI and patentability.

AI-driven knowledge bases often repurpose content from internal and external sources. Without IP-aware workflows, organisations risk violating copyright laws. By implementing audit framework, businesses can ensure compliance while enhancing productivity. Enterprises must therefore consider Agentic AI and copyright infringement when designing compliance measures.

4. Software Development Automation

These concerns demonstrate the growing importance of Agentic AI and copyright infringement.

Agentic AI agents capable of generating code introduce risks of copying open-source components without proper licensing. Enterprises adopting agents must integrate license scanning and compliance validation within pipelines. These concerns form part of the broader Agentic AI IP risks that enterprises must address.

As India continues to expand its AI footprint, the imperative is clear: innovation must walk in tandem with accountability and a clear IP strategy. Regulatory oversight must evolve from static rulebooks to dynamic compliance systems that can audit algorithmic behavior in real-time. Institutions must invest not only in IP creation and usage but strategically managing the same through a comprehensive IP Management systems.  Understanding  is essential when deploying intelligent automation at scale.