What began as simple task automation, such as chatbots answering basic queries, systems processing forms, and algorithms sorting data, has evolved into something far more sophisticated. Now, it thinks, decides, and acts autonomously and synchronously across complex business scenarios.
Agentic AI represents the next frontier in business operations. Combining intelligence with execution will fundamentally change how organizations deliver customer value.
A recent survey of IT decision-makers worldwide from key industries, including finance, healthcare, retail, manufacturing, and telecommunications, revealed that 96% of participants have decided to expand into Agentic AI.
Given Coforge’s strong footprint in offering AI-led Business Operations, it is now a natural fit to continuously leverage the subsequent growth set by strategizing on Agentic AI-led offering, tightly coupled with our strong domain-led Human-in-the-Loop (HITL).
Agentic AI is like imagining an operational agent on the floor performing a specific task, working synchronously, making decisions, and achieving a defined goal and objective.
We continuously evaluate operational processes to identify the automation opportunity and a Point of View (POV) which can further lead to a Proof Of Concept and eventually the actual automation of the process. We have successfully used this approach to develop a Point Solution to solve industry-specific challenges through AI-led intervention.
What is Agentic AI?
To the uninitiated, what is Agentic AI?
Processes with repetitive tasks that are rule-based can be automated through an RPA-led traditional approach.
Agentic AI represents the culmination of decades of technological advancement, combining prediction, creation, and autonomous action into a unified operational force in these processes.
Traditional AI excels at analyzing data patterns to predict outcomes, such as answering the question, "What might happen?"
Generative AI expanded possibilities by answering the question, "What could be said or made?" It generates content such as text and images from large data sets.
Agentic AI transcends both by autonomously determining "What should be done?" These systems orchestrate entire workflows, make contextual decisions, and execute actions based on complex business logic.
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AI Type
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Core Function
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Business Application
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Key Capabilities
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Human Involvement
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Business Impact
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Example Use Case
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Traditional AI
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Prediction & Classification
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Risk assessment, pattern recognition, anomaly detection
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- Data analysis
- Pattern identification
- Probability scoring
- Rule-based decisions
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High - requires interpretation and action on insights
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Improved accuracy in decision support
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Credit scoring system flags high-risk applications for human review
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Generative AI
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Content Creation & Transformation
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Document generation, creative assistance, and data synthesis
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- Natural language processing
- Content generation
- Text summarization
- Creative ideation
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Medium - needs guidance on context and application
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Enhanced productivity and creativity
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AI assistant drafts customer service responses for human approval
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Agentic AI
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Autonomous Decision-Making & Action
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End-to-end process orchestration, case management
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- Contextual understanding
- Multi-step workflow execution
- Dynamic decision trees
- System integration
- Learning from outcomes
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Low - operates independently with oversight checkpoints
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Complete workflow automation with adaptive intelligence
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AI agent receives customer email, analyzes intent, queries systems, resolves issues, and sends response autonomously
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While we interchangeably use AI agents and Agentic AI, it is important to distinguish them at a broad level, as below.
- AI Agent - Autonomous software designed for one specific task. It has high autonomy within that task, with optional memory or tool use. It typically executes prompt-triggered goals within a fixed scope.
- Agentic AI - In a way, multiple AI agents work together to complete complex, multi-step goals. The system learns from outcomes and shares memory and context across agents. Goals initiate the workflow and adapt as they progress.
With 62% of US, UK, and Australian organizations expecting over 100% ROI from agentic AI deployments, the business case for this evolution becomes compelling.
The Evolution of Business Automation
The automation journey that began with Robotic Process Automation (RPA) has reached a critical inflection point. While RPA revolutionized routine task execution through rule-based systems, its deterministic nature has exposed fundamental limitations in an ever-changing, dynamic business environment.
Organizations now confront the reality that less than 1% of enterprise
software utilized agentic AI in 2024; a figure poised for dramatic
transformation by 2028 as businesses recognize the constraints of
traditional automation approaches.
RPA's strength lies in its predictability given identical inputs; it produces identical outputs with unwavering consistency. However, its limitations become particularly evident in scenarios requiring interpretation and judgment.
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For Example: When a customer submits a complaint that spans multiple product categories, contain emotional undertones, and references previous interactions, traditional RPA systems falter. Without extensive pre-programming for every conceivable scenario, they cannot synthesize context, prioritize urgency, or determine appropriate escalation paths.
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This operational gap has created a deployment crisis where most AI projects fail to reach operational deployment due to a lack of orchestration capabilities. Organizations invest heavily in automation initiatives only to discover that their deterministic systems cannot handle the complexity of real-world business processes. The result is a fragmented automation landscape where multiple point solutions address individual tasks but fail to create cohesive, end-to-end workflows.
Agentic AI emerges as the solution to this challenge.
Unlike RPA's binary decision trees, agentic AI systems employ dynamic reasoning to evaluate context, assess multiple variables simultaneously, and adapt their approach based on situational nuances. This capability addresses repetitive and complex tasks within a unified framework, eliminating the need for separate automation solutions across different process types.
The business case for this evolution becomes clear when considering that organizations globally recognize process orchestration as essential for successful AI deployment.
Coforge’s Approach to Agentic AI-led Email Automation
An email automation in banking demonstrates how agentic AI transitions from task-based automation to comprehensive case management, orchestrating complex customer service workflows that previously required multiple human touchpoints and system interactions. Different AI agents & tools are used to read the email content, monitor emails, their classification, and respond to customer requests, eventually through API integration into the banking system. Infact, AI-led prediction from such historical data can actually further eliminate the need for customers to write to a bank for fulfillment of such requests in first place.
The challenge facing modern banking operations centers on email volume and complexity. Customer inquiries arrive in unstructured formats, often containing multiple requests, emotional undertones, and references to historical interactions.
An AI agent can interpret natural language, make contextual decisions, and execute appropriate actions autonomously. In a way, it understands the “Content,” “Context,” and “Intent.”
Our solution revealed critical implementation considerations, distinguishing successful agentic AI deployments from traditional automation projects. Fine-tuning decision boundaries proved essential; the system required careful calibration to avoid redundant responses while maintaining comprehensive coverage. Additionally, the human-in-the-loop design enabled graceful degradation for edge cases, ensuring operational continuity even when encountering unprecedented scenarios.
Industry-Agnostic Applications of Coforge’s Agentic AI-led Solution
Unlike traditional automation that addresses isolated processes, agentic AI orchestrates complete workflows that span multiple systems, departments, and decision points.
Banking and Financial Services -
Financial institutions exemplify agentic AI's case management capabilities through complex scenarios like loan origination. When a customer applies for a mortgage, the AI agent can orchestrate document collection, credit verification, property valuation coordination, regulatory compliance checks, KYC remediation, and fraud management. In Mortgage, Agentic AI-led automation is helpful in document classification, data extraction, property search reports, and typing automation.
Insurance Claims Processing -
Upon claim submission, agentic AI immediately analyzes policy coverage, coordinates with medical providers, schedules inspections, and processes payments. Complex cases receive intelligent preparation that equips human adjusters with complete context and recommended action plans, transforming fragmented handoffs into seamless workflows.
Customer Service Excellence Through Intelligent Orchestration -
When customers present interconnected issues spanning billing, technical support, and account management, the AI maintains a unified case context while orchestrating resources across departments, eliminating repetitive explanations and ensuring consistent resolution.
Operations and Strategic Decision Support -
Here, AI systems monitor regulatory changes, assess procedural impacts, implement necessary updates, and generate required documentation while maintaining audit trails and escalation protocols for complex situations.
Operations and Strategic Decision Support -
Here, AI systems monitor regulatory changes, assess procedural impacts, implement necessary updates, and generate required documentation while maintaining audit trails and escalation protocols for complex situations.
Implementation Considerations
The shift from experimental pilots to full-scale deployment strategies marks a critical maturation point for agentic AI adoption. Hence, organizations require strategic implementation approaches that balance rapid deployment with operational sustainability.
Deployment Velocity and System Integration -
Modern enterprises prioritize platforms that enable deployment within 90 days, recognizing that prolonged implementation cycles diminish competitive advantages. However, speed cannot compromise integration quality. Organizations must connect agentic AI systems seamlessly with existing infrastructure, including legacy RPA implementations, core business systems, and established data workflows.
The integration challenge extends beyond technical connectivity to operational orchestration. Process orchestration is the critical success factor that prevents stalled pilot programs and ensures scalability. Rather than replacing existing automation investments, successful implementations layer agentic AI capabilities over current systems, creating unified case management platforms that leverage all available resources.
Operational Readiness and Change Management -
Balancing autonomy with oversight requires sophisticated governance frameworks that enable agentic AI systems to operate independently while maintaining appropriate human supervision. This balance proves particularly crucial in regulated environments where compliance requirements demand transparent decision-making processes and an audit trail.
Implement change management strategies that position human employees as case orchestrators rather than task executors. Organizations that invest in redefining roles, creating positions like "AI case managers" or "digital workflow supervisors," achieve smoother transitions and higher adoption rates.
Strategic Starting Points -
Successful implementations begin with carefully selected pilot programs demonstrating case management capabilities while building organizational confidence. Starting small with high-impact scenarios allows organizations to refine processes, establish governance protocols, and develop internal expertise before scaling enterprise-wide.
Conclusion
The window for strategic advantage remains open but narrows as adoption accelerates. Organizations that begin their agentic AI journey now will establish competitive moats that become increasingly difficult for followers to bridge. The question is not whether agentic AI will transform business operations, but which organizations will lead this transformation.
Readiness assessment begins with identifying high-impact case management scenarios where automation falls short. Organizations should evaluate their process orchestration capabilities, data integration maturity, and change management capacity to determine optimal implementation strategies.
Key readiness indicators:
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- Complex workflows requiring multiple system interactions
- High-volume processes with variable decision requirements
- Customer service scenarios demanding contextual understanding
- Compliance processes requiring adaptive rule interpretation
The most successful implementations start with clear use cases demonstrating immediate operational impact while building foundation capabilities for broader deployment.
To know more about our offerings, write to us at coforgebps@coforge.com.
