The Rise of Agentic AI in India
A transformative shift is underway in India's technology landscape as 58% of Global Capability Centers (GCCs) report active investment in agentic AI—autonomous systems capable of understanding, planning, and executing complex decisions independently.
This represents the next evolution beyond generative AI, moving from tools that assist humans to agents that can act autonomously on their behalf.
What is Agentic AI?
Unlike traditional AI systems that require human input for each task, agentic AI systems can:
| Capability | Description |
|---|---|
| Understand | Comprehend complex, multi-step instructions |
| Plan | Break down goals into actionable sub-tasks |
| Execute | Perform tasks across multiple systems |
| Learn | Improve from outcomes and feedback |
| Adapt | Adjust plans based on changing conditions |
GCC Investment Patterns
India hosts over 1,700 GCCs representing global corporations. Their agentic AI investments span:
Primary Use Cases
-
IT Operations Automation
- Incident detection and resolution
- Infrastructure provisioning
- Security monitoring and response
-
Finance & Accounting
- Invoice processing end-to-end
- Compliance monitoring
- Fraud detection and investigation
-
HR Operations
- Candidate screening and scheduling
- Employee query resolution
- Training program management
-
Customer Service
- Complex query resolution
- Multi-channel case management
- Proactive customer outreach
The Technology Stack
GCCs are building agentic AI systems using:
Foundation Models
- GPT-4 and GPT-5 APIs
- Claude 3.5 and Claude 4
- Gemini 2.0 and 3.0
- Open-source models (Llama 3, Mistral)
Orchestration Frameworks
- LangChain and LangGraph
- AutoGen by Microsoft
- CrewAI
- Custom frameworks
Infrastructure
- Cloud AI services (AWS, Azure, GCP)
- On-premises GPU clusters
- Hybrid deployments
Workforce Implications
The agentic AI revolution is reshaping work in GCCs:
Task Redistribution
Before Agentic AI:
Human: 100% of tasks
After Agentic AI:
Human: Strategic, creative, relationship tasks
AI Agent: Routine, repetitive, rule-based tasks
Human+AI: Complex, judgment-requiring tasks
New Roles Emerging
- AI Agent Supervisors: Monitoring and correcting agent behavior
- Prompt Engineers: Designing agent instructions
- AI Orchestrators: Managing multi-agent workflows
- Ethics Reviewers: Ensuring responsible AI deployment
Case Studies
Major IT Services Company
- Deployed agents for 40% of L1 support tickets
- Reduced resolution time by 65%
- Improved customer satisfaction by 22%
Global Bank's India Centre
- Automated 70% of account reconciliation
- Reduced processing time from days to hours
- Zero increase in error rates
Pharma Company GCC
- Agents monitor 200+ clinical trials
- Automatic regulatory document preparation
- 50% faster compliance reporting
Challenges and Concerns
Technical Challenges
- Reliability: Agents occasionally make incorrect decisions
- Observability: Difficulty tracking agent reasoning
- Integration: Connecting with legacy systems
- Security: Preventing agent misuse
Organizational Challenges
- Change Management: Employee anxiety about automation
- Skills Gap: Need for AI-literate workforce
- Governance: Establishing oversight frameworks
- Accountability: Determining responsibility for agent actions
Looking Ahead
Industry analysts predict:
- 2026: 75% of GCCs will have agentic AI in production
- 2027: Average employee working with 3+ AI agents
- 2028: Agentic AI enabling new GCC service offerings
"Agentic AI represents the biggest shift in knowledge work since the spreadsheet. GCCs that embrace it will thrive; those that don't will struggle to remain competitive."
India's GCCs are positioning themselves at the forefront of this revolution, potentially establishing the country as a global hub for agentic AI expertise.
