Agentification: the art of transforming manual processes to an orchestration of AI Agents The reason AI Agents are so popular is because they can in principle be applied to any enterprise process that is executed manually today. So we can basically agentify everything from a Customer ServiceDesk, to Industrial processes, e.g., HVAC optimization; to even leveraging agents to build the underlying software / data / MLEngineering pipelines. To enable this agentification process, we need a new holistic discipline that should cover the full agentic lifecycle from: - capturing the agentic requirements - to designing the agents (what does a good agent hierarchy look like?) - to the implementation, governance and maintenance of the agents on an agentic platform.

1. Agentic AI &
Hyper Automation
Debmalya Biswas
3rd July, 2025

2. Agentic AI Evolution

3. Agentic AI Use-case: Email Marketing Campaign

4. Agentic AI Lifecycle

5. Agentification of Customer Service Desk

6. Agentic Reference Architecture
* D. Biswas. Agentic AI for Customer Service Desk.
Data Science Collective, 2025 (link)

7. Personalization of AI Agents
* D. Biswas. Personalizing UX for Agentic AI. AI Advances, 2025 (link)

8. Responsible AI Agents
* D. Biswas. Stateful & Responsible AI Agents. ICAART 2025 (link)

9. Agentification of Data Management /
Data Engineering
* D. Biswas. Agentic AI for Data Engineering. Data Science Collective, 2025 (link)

10. Learnings & Key Takeaways
1. Every enterprise process is a candidate for agentification.
2. Do not try to map manual processes 1-to-1 to agentic ones. This is an inefficient mapping.
Designers should keep in mind that an agent is not, e.g., bound by HR processes! It can do
different things, and do it differently than a human.
3. Do not get overwhelmed by agentic frameworks. Frameworks like A2A, MCP are good, and
help in standardization – but solve only part of the agentic lifecycle.
4. Functional evaluation of agents is key, but remains difficult for autonomous agents.
5. Security is key for enterprise adoption. We would not know whom to blame, fine, fire, etc. if
even a single agent went rogue. So, design all agents with the same utmost care, together with
logging, observability, and Responsible AI guardrails.

11. Thanks
&
Questions
Debmalya Biswas
https://www.linkedin.com/in/debmalya-
biswas-3975261/
https://medium.com/@debmalyabiswas