Agentic AI - Plan well before you implement

2024 started enough buzz on Agentic AI. This year we will see  lot more activities around organizations jumping into riding this new wave of AI. Certainly it looks promising , thanks to LLMs getting stronger by the day, freakishly getting smarter with reasoning skills like human brain and cognitive abilities, GPUs continue to push the limit on compute power.

It’s not an imagination anymore, we are about to live in a world of autonomous digital agents working in tandem, independent, inter-dependent, well-orchestrated; to resolve multi-step/multi- function enterprise workflows. With Agentic AI, organizations are looking forward to significantly improve workflow efficiencies; there by enhance CX, increase sales, improve OpeX, increase cash flow etc.

But the question is .. how ready are we to manage these digital AI agents with right guardrails for their ethical and responsible behavior?

Here are my quick thoughts…I welcome your views.

Governance:

Establish strong governance framework with ethical AI practices valued by Emotional Intelligence (EI), inclusive and purpose driven task management. This is fundamental to enterprise AI transformation initiatives.

Transparency  - Ensure transparency on digital agents functional boundaries, what they can do and can not do.  

Accountability – Organizations will face challenges of managing digital agents similar to human resources. End of the day, these agents are part of an enterprise function and associated workflows. Define clear role and responsibility with specific data access boundaries. Ensure human in loop so departmental leaders can take swift action if there is any misstep.

 Bias mitigation – Ensure regular auditing of algorithms to avoid bias in autonomous decisions and bring diversity / inclusivity aspects in algorithm design. Yes, DEI is applicable to digital agents :)

 Regulatory compliances – GDPR, HIPAA, CCPA .. there are many more regulatory protocols. Ensure adherence of algorithms to regulatory protocols based on use case needs.

Inter-agents Collaboration:

Ensure agent to agent collaboration with seamless hand offs enabled by right API contracts, sufficient data parameters and human in loop for swift action in case of any errors.

Ecosystem Integration: Most important element in any AI solution is underlying data. Extremely important to ensure digital agents are enabled by well orchestrated ecosystem with APIs, system of records, data hubs, adjacent enterprise workflows etc

Security:

According to Gartner -   by 2028, 15% of daily business tasks are expected to be handled by Agentic AI.  Also by that time, 25% of enterprise breaches could be linked to abuse of AI agents. Hence design agents to protect sensitive customer  data, minimize data collection, follow data privacy protocols, continuously audit agents for potential vulnerabilities and threats.

Scalability and Adaptability:

Ensure agents are on continuous learning mode with evolving decisions. Work loads are seasonal (for ex: holiday season and associated customer support call volume) in nature, so agents are capable of handling peaks and lows of tasks volume.

Experience-led:

We are talking about human like interactions.. ensure agents are designed with high degree of user experience, trust and emotional quotient. Continuously seek feedback from users to improve agent performance

Cost management:

Computational cost needs of Agentic AI is still high. Cost management is critical. Hence do your due diligence on right infrastructure, platform, LLM provider etc for financial efficiency in order to justify ROI on Agentic AI investments.

It is certainly an exciting time to bring in Agentic AI but before we embark on this game changing transformation, let’s set the stage with right guardrails. A thoughtful Agentic AI readiness framework much before we jump into implementation action is critical to success of the program.

I may have missed any other important considerations. I welcome your views further..