Exploring the Impact of Agentic Workflows // Raj Rikhy // #268

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Oct 15, 2024

In this engaging discussion, Raj Rikhy, a Senior Product Manager at Microsoft AI + R, shares insights on deploying AI agents effectively. He highlights the importance of starting small with clear success criteria while maintaining human oversight to manage AI unpredictability. Raj dives into real-time applications like fraud detection and supply chain optimization, emphasizing the efficiency gains from agentic workflows. He also compares this transformative technology to innovations like the iPhone, encouraging listeners to embrace the future of AI.

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ADVICE

Constrain Agent Actions

Never give your agent raw web access out of the box, constrain its actions.
Vet outcomes yourself and ask the agent to explain next steps or simulate activities.

ADVICE

Start Small and Constrained

Start with a constrained environment, like using a cleaner on an inconspicuous spot first.
Test support agents with low-priority cases before giving them full access.

ADVICE

Iterate, Don't Over-Engineer

Don't over-engineer agents from the start, use off-the-shelf tools when possible.
Iterate and improve rather than immediately building custom solutions.

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Raj Rikhy is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.

// MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft.

// Abstract

In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications.

// Bio

Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/

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