Artificial intelligence has moved beyond passive assistance into a new phase of autonomy, often referred to as Agentic AI. These systems can independently make decisions, execute tasks, and interact with other digital environments without constant human input. From managing workflows and analysing contracts to responding to customer queries or drafting code, agentic AI tools are transforming how businesses operate and redefining the boundaries of automation.
Yet, with greater autonomy comes greater legal complexity. Questions around accountability, ownership of outcomes, data protection, and regulatory compliance are becoming central to their deployment. In India, where AI adoption is accelerating across industries, understanding the legal and policy implications of such systems is critical. Businesses, developers, and regulators alike must navigate this evolving landscape responsibly to ensure innovation aligns with law and ethics.
Applications of Agentic AI
Agentic AI is steadily transforming how organisations execute tasks and make decisions. Unlike conventional AI systems that respond only to prompts, agentic models operate with a degree of independence: setting goals, adapting to new information, and interacting across digital systems. Their applications are expanding across industries, revealing both their potential and the legal questions they raise.
1. Operational Automation: Enterprises are deploying agentic AI to handle routine yet critical operations for example, automating customer support through self-learning chat agents, or managing compliance workflows using AI assistants that track regulatory deadlines and filings.
2. Knowledge Work Augmentation: In professional domains such as law and finance, tools like AutoGPT or LangChain-based agents can draft contracts, summarise research, or generate investment reports with minimal human oversight.
3. Decision Support Systems: Sectors like fintech and healthcare are exploring autonomous agents that evaluate loan eligibility, flag suspicious transactions, or recommend treatment plans based on patient data.
These applications demonstrate the power of agentic AI but they also highlight the need for careful governance to ensure human accountability, explainability, and compliance with India’s evolving regulatory frameworks.
Emerging Business Models
As agentic AI matures, businesses are experimenting with varied models to integrate these systems into commercial and enterprise workflows. The structure of these models often determines not just scalability, but also the legal responsibilities associated with their use.
1. Software-as-a-Service (SaaS) Platforms: Many startups now offer agentic AI tools as subscription-based platforms—allowing users to automate tasks like data analysis, marketing, or contract review. These platforms raise questions around liability when the AI acts autonomously and produces inaccurate or unintended outcomes.
2. Enterprise Integration: Larger organisations are embedding agentic layers within internal systems, such as HR management or legal compliance tools. This enhances efficiency but also requires strong internal governance, particularly where AI agents make operational decisions without human approval.
3. API-based AI-as-a-Service Models: Some developers provide API access to agentic frameworks built on foundational models (like GPT or Claude), enabling businesses to build their own autonomous agents.
Across all these models, one common thread persists i.e., determining who bears accountability when an autonomous agent errs, breaches policy, or acts beyond its intended scope remains an unresolved legal frontier.
Legal and Contractual Aspects
The growing autonomy of agentic AI tools has reshaped traditional notions of control, responsibility, and contractual accountability. Businesses deploying or offering such tools must therefore revisit how their legal terms allocate risk and define ownership.
1. Terms of Use and Liability: Contracts and user policies should clearly outline the extent of the AI’s autonomy, specify where human oversight is required, and limit the provider’s liability for unintended outcomes. Clauses on disclaimers, indemnities, and limitation of liability become critical particularly where the AI performs actions that could have commercial or legal implications.
2. AI Acting as a User’s Agent: A defining feature of agentic AI is its ability to perform real-world tasks on behalf of the user—such as making bookings, scheduling appointments, or executing online transactions. In effect, the AI acts as an agent of the user, with the user being legally bound by the contracts the system enters into. Accordingly, terms and conditions should expressly state that any such actions are undertaken on the user’s behalf and that the user remains responsible for resultant obligations. They must also specify liability allocation in cases of breach or error and include mechanisms for promptly notifying the user of each transaction executed autonomously.
3. Intellectual Property and Output Ownership: Determining who owns AI-generated content remains complex. Under current Indian copyright law, only works created by a human author qualify for protection. Businesses using agentic systems must therefore define ownership rights contractually, especially for creative or analytical outputs.
4. Algorithmic Accountability: Transparency and traceability are fast becoming compliance expectations. Maintaining audit trails, documenting data sources, and explaining decision logic can help mitigate disputes and establish credibility.
Ultimately, as AI agents start acting independently, contracts need to evolve from merely governing service use to managing shared responsibility between developers, deployers, and users within an autonomous ecosystem.
Data Protection and Privacy Considerations
Agentic AI systems thrive on data; they learn, adapt, and act based on information gathered from multiple digital environments. This inherent reliance on data brings into sharp focus the obligations imposed under India’s evolving data protection regime, particularly the Digital Personal Data Protection Act, 2023 (“DPDP Act”) and the Information Technology Act, 2000 (“IT Act”), along with its allied rules and directions.
1. Obligations under the DPDP Act, 2023:
Under the DPDP Act, entities that determine the purpose and means of data processing qualify as ‘Data Fiduciaries’, while those processing data on their behalf are ‘Data Processor’s. Agentic AI developers or deployers often fall within these definitions, especially where the system autonomously collects, analyses, or shares user data. They must obtain free, specific, informed, and unambiguous consent before processing personal data, in line with Section 6 of the DPDP Act.
The principles of purpose limitation and data minimisation under Sections 5 and 7 restrict the use of personal data solely for the legitimate purpose for which it was collected. The Act also mandates the implementation of reasonable security safeguards[1] and provides users with rights to access, correction, and erasure of their data[2]. For agentic AI, this means embedding compliance “by design”, through consent dashboards, human-in-the-loop validation, and the ability to trace how data is used or transformed by autonomous actions.
2. Obligations under the IT Act, 2000 and Related Rules:
Section 43A of the IT Act obligates corporate entities to adopt “reasonable security practices and procedures” to protect sensitive personal data, as elaborated under the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011. These rules emphasise notice, consent, data retention limits, and transfer restrictions to ensure user privacy.
In addition, the CERT-In Directions (April 2022)[3] require organisations to report cybersecurity incidents, including breaches caused by AI-driven processes, within prescribed timelines. For agentic AI systems that operate autonomously across networks, ensuring compliance with these reporting and monitoring obligations becomes particularly important.
Collectively, these frameworks highlight that autonomy does not exempt accountability. Developers and businesses using agentic AI must ensure that privacy, security, and human oversight remain integral to every stage of design, deployment, and ongoing operation.
AI Policy and Governance Landscape in India
India’s approach to artificial intelligence has gradually shifted from fostering innovation to establishing structured oversight mechanisms that ensure responsible and ethical deployment. The Government’s policy direction now rests on two key pillars i.e., the IndiaAI Mission (2024) and the recently introduced India AI Governance Guidelines, 2025, both reflecting a broader vision to align technological advancement with public accountability.
1. IndiaAI Mission (2024)[4]:
Introduced by the Ministry of Electronics and Information Technology (MeitY), focuses on building national capabilities in compute infrastructure, datasets, and research collaboration. It promotes the development of scalable AI models while embedding principles of ethics, transparency, and inclusion through the “AI for All” framework. For agentic AI developers, this mission provides the foundational ecosystem — access to data, innovation platforms, and policy support, while underscoring that autonomy in design must coexist with human oversight and societal accountability.
2. India AI Governance Guidelines, 2025:[5]
These guidelines provide a governance framework for the responsible development and deployment of AI systems. They outline key principles of Accountability, Safety, Transparency, Fairness, and Non-discrimination, and mandate risk classification of AI systems based on their potential impact. Developers and deployers of agentic AI are expected to implement AI Risk Assessment Frameworks, maintain explainability documentation, and ensure human-in-the-loop oversight for high-impact applications.
The Guidelines also encourage adoption of AI governance committees within organisations and recommend establishing sectoral regulatory sandboxes for testing complex AI models in controlled environments. Together with the DPDP Act, these initiatives mark India’s transition from AI promotion to AI regulation—placing responsibility and governance at the core of innovation.
Key Legal and Ethical Risks
The rapid evolution of agentic AI brings with it a new layer of legal and ethical uncertainty. As these systems act with limited or no human intervention, identifying accountability becomes a core challenge: who is responsible when an AI agent makes a mistake, breaches data rules, or executes an unintended action?
Autonomous functioning also amplifies data protection and privacy risks, especially when agents collect or share information without explicit user consent. Bias in training data can lead to discriminatory decisions, while opaque algorithms make it difficult to ensure transparency and explainability.
From a cybersecurity standpoint, agentic systems can be exploited or manipulated, posing potential threats to both users and organisations. The absence of a clear liability framework under Indian law further complicates these risks, making it essential for developers and deployers to adopt strong governance, documentation, and oversight measures at every stage of system design.
The Way Forward
1. Building a Responsible Framework
Agentic AI marks a new era in technology one where systems can think, plan, and act independently. For India, the focus must now shift from experimentation to responsibility. Developers and businesses should integrate compliance and ethical safeguards into every stage of design and deployment. Some key practices include:
a. Embedding AI governance mechanisms such as internal oversight committees and human-in-the-loop checks.
b. Maintaining explainability documentation and audit trails for AI-driven actions and outputs.
c. Conducting periodic risk assessments to evaluate data use, decision accuracy, and potential harm.
d. Implementing responsible AI by design principles to ensure fairness, non-discrimination, and data minimisation.
2. Bridging Law and Innovation:
India’s regulatory framework, anchored in the DPDP Act, IT Act, and India AI Governance Guidelines, provides a foundation, but clearer rules are needed for agentic systems. Until then, legal safeguards should focus on:
a. Detailed contractual allocation of liability between developers, deployers, and end users.
b. Indemnity clauses to protect against unintended or harmful autonomous actions.
c. Transparent terms of use clarifying limits of AI autonomy and human responsibility.
As India’s AI ecosystem matures, aligning innovation with accountability will be crucial. The success of agentic AI will ultimately depend on how effectively law, policy, and technology converge to ensure trust, transparency, and safety.
[1] Section 8 of the Digital Personal Data Protection Act, 2023
[2] Sections 11-13 of the Digital Personal Data Protection Act, 2023
[3] Indian Computer Emergency Response Team (CERT-In) Directions on Information Security Practices, April 28, 2022
[4] Ministry of Electronics and Information Technology, “IndiaAI Mission Framework,” 2024
[5] India AI Governance Guidelines,” Government of India, November 2025