mobile logo


The rise of Agentic AI Workflows: A game-changer in digital efficiency


Prasad Prabhakaran

Head of AI practice



post hero image


As AI technology continues to advance, new and powerful large language models (LLMs) and foundational model architectures are emerging. Among these, the concepts of “agents” and “agentic workflows” are particularly exciting and hold the potential to revolutionize AI applications in the near term. Renowned computer scientist Andrew Ng has emphasized the immense potential of these advancements, urging the AI community to pay close attention. Here’s why agentic workflows are set to be the next big thing in AI.  


Understanding AI Agents and Agentic Workflows 

What are AI agents?  

AI agents are autonomous systems that can perform tasks, make decisions, and interact with users or other systems similarly to humans. Unlike traditional AI systems that generate a single output from a given prompt, AI agents collaborate, share goals, and make collective decisions, leading to more sophisticated interactions and enhanced efficiency. 

What are agentic workflows?  

Agentic workflows refer to an iterative, interactive AI development approach where AI agents engage in dynamic and self-reflective processes. This contrasts with the traditional “non-agentic” approach, where AI models generate outputs in a single pass. 


Key design patterns enabling Agentic Workflows 


AI agents can review their own work, identify improvements, and refine their outputs iteratively. For instance, an AI agent tasked with writing code can generate, evaluate, and refine the code through multiple iterations, leading to higher-quality results. 

Tool use 

Agentic workflows enable AI agents to leverage various external tools and functions, such as web searches or code execution, to gather information and perform tasks. This expands the AI’s capabilities, allowing it to integrate seamlessly with external tools to achieve more complex tasks. 


AI agents can develop and execute multi-step plans to achieve specific goals. For example, an AI agent conducting online research can break down the task into subtasks, such as researching subtopics, synthesizing findings, compiling a report, and demonstrating strategic planning and problem-solving capabilities. 

Multi-Agent collaboration 

Multiple AI agents can work together, each playing a distinct role, to achieve better solutions than a single agent could. This collaborative approach involves autonomous decision-making, communication, coordination, and specialized roles among the agents, leading to more complex problem-solving and improved results. 


Practical scenario use cases for Agentic Workflows 

Banking: Customer service automation 

Scenario: A customer needs assistance with a loan application. 


  1. Initial inquiry: An AI agent handles the customer’s initial inquiry through a chatbot, collecting the necessary information. 
  2. Document collection: The agent instructs the customer on the required documents and helps upload them. 
  3. Eligibility check: Another agent assesses the customer’s eligibility by analyzing the provided documents and the customer’s credit history. 
  4. Approval and feedback: The AI agents collaborate to process the loan application, provide feedback, and notify the customer of the application status. 

Outcome: This multi-agent approach improves response times, accuracy, and customer satisfaction. 


Asset Management: Portfolio optimization 

Scenario: An investment manager seeks to optimize a client’s portfolio. 


  1. Data gathering: An AI agent collects and analyses market data, historical performance, and the client’s investment preferences. 
  2. Risk assessment: Another agent evaluates the risk associated with current and potential investments. 
  3. Strategy development: Agents collaboratively develop multiple investment strategies, iteratively refining them through simulations and risk assessments. 
  4. Recommendation: The agents present the optimized portfolio strategy to the investment manager, along with a detailed rationale for the proposed changes. 

Outcome: Enhanced investment strategies with reduced risk and improved returns. 


Asset Service: Predictive maintenance 

Scenario: A facility manager needs to ensure the optimal performance of critical machinery. 


  1. Monitoring: AI agents continuously monitor the performance data of the machinery. 
  2. Anomaly detection: When an anomaly is detected, an AI agent analyses the data to predict potential failures. 
  3. Maintenance planning: The agents plan and schedule maintenance activities, coordinating with human technicians. 
  4. Feedback loop: Post-maintenance, agents review the results to refine future predictive models. 

Outcome: Reduced downtime and maintenance costs, and improved equipment reliability. 


Insurance: Claims processing 

Scenario: An insurance company needs to process a large volume of claims efficiently. 


  1. Initial submission: An AI agent assists customers in submitting their claims, ensuring all required information is collected. 
  2. Fraud detection: Another agent analyses claims for potential fraud by comparing them against historical data and known patterns. 
  3. Assessment and approval: Agents evaluate the validity of claims, calculate payouts, and approve legitimate claims. 
  4. Customer communication: The AI agents keep customers informed throughout the process and provide support as needed. 

Outcome: Faster claim processing, reduced fraud, and improved customer satisfaction. 


Public Service: Citizen services automation 

Scenario: A government agency aims to streamline service delivery to citizens. 


Service request handling: AI agents manage citizen requests for various services, such as license renewals or benefits applications. 

  1. Document verification: Agents verify submitted documents for authenticity and completeness. 
  2. Service delivery: The agents coordinate with different departments to process the requests and deliver the services. 
  3. Feedback collection: Post-service, agents collect feedback from citizens to improve future interactions and service quality. 

Outcome: Enhanced efficiency in service delivery, reduced wait times, and higher citizen satisfaction. 


The implementation of agentic workflows in various sectors, such as Banking, Asset Management, Asset Service, Insurance, and Public Service, can significantly enhance efficiency, accuracy, and user satisfaction. By leveraging AI agents to handle complex tasks, these industries can optimize their operations and deliver superior results. The future of AI is indeed agentic, and the potential applications are vast and transformative. 


The implications for the future of AI 

The adoption of agentic workflows will be a crucial driver of AI progress in the coming years, potentially surpassing the impact of the next generation of foundation models. This shift towards more interactive, self-reflective AI agents holds significant implications for software development, business strategy, and the overall trajectory of artificial intelligence. 



As AI continues to evolve, the insights and design patterns of agentic workflows provide a valuable roadmap for researchers, developers, and business leaders to harness the full potential of these advanced AI systems. The future of AI is poised to be transformed by these advancements, making it essential for everyone in the field to stay informed and embrace this exciting new frontier. Agentic workflows symbolize a future where AI is not just a responder but a thinker and a doer, opening doors to innovative AI collaborations that were once the realm of science fiction. 


 To explore potential use cases and understand the value and on AI agentic workflow, please sign up for a workshop.