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.
Read more
AI Summary
AI Chapters
Episode notes
auto_awesome
Podcast summary created with Snipd AI
Quick takeaways
Establishing clear goals and success parameters is crucial for the effective development of agent-driven systems, significantly enhancing their operational success.
Practical applications of AI agents range from fraud detection to healthcare monitoring, emphasizing their versatility and real-time response capabilities in diverse industries.
Deep dives
Understanding Agents
Agents are defined as entities that can take autonomous actions and possess decision-making capabilities. The concept of agents has been studied since the 1950s, especially within reinforcement learning, which explores how agents can operate effectively within a given environment. Understanding the history and functionality of agents is crucial for defining their roles and capabilities in various applications, including AI development. Effective use of agents requires clarity in the objectives and desired behaviors they are intended to achieve.
Defining Goals and Success Criteria
Establishing clear goals and success parameters is essential when developing an agent-driven system. By documenting intended outcomes, developers create a framework within which agents can operate effectively, significantly improving their chances of success. Each agent must be designed to fulfill specific functions within constrained environments, which serve as the parameters for its actions. Properly defining these objectives ensures that the agent's performance can be accurately evaluated based on success metrics.
Iterative Design and Feedback Loops
Quick feedback loops and iterative design are crucial for refining agent functionality and addressing potential failure states. Prioritizing human oversight during the early stages of agent development helps to identify issues before full deployment. Developers are encouraged to start with simple applications, testing in controlled environments before scaling up. This approach allows for incremental improvements, reducing the risk of major failures due to unforeseen complexities.
Practical Applications and Industry Impact
Agents have a multitude of practical applications across various industries, including fraud detection and supply chain optimization. By operating in real time, agents can effectively respond to challenges that arise within their environments, significantly improving operational efficiency. Other innovative uses include personal shopping assistants and automated monitoring systems in healthcare, showcasing the flexibility and potential of agent-driven solutions. Understanding the integration of small language models and traditional algorithms can enhance the reliability and speed of agent workflows.
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/
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Raj on LinkedIn: https://www.linkedin.com/in/rajrikhy/
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode