Agentic AI exploded in 2025, but how do businesses move beyond prototypes to deploy reliable, valuable agents at scale?

Join host Conor Bronsdon and Lyzr AI CEO Siva Surendira as they discuss the complexities of building and managing AI agents for enterprises. Siva shares his journey creating Lyzr, focusing on making powerful agent frameworks accessible and trustworthy for enterprise developers. They discuss the critical hurdles businesses face, including productionization challenges, ensuring responsible AI, and bridging the gap between rapid innovation and the stringent requirements of regulated industries.

Listen as Siva explains Lyzr's approach to embedding safety guardrails natively and learn about the nuances of multi-agent orchestration, including managerial, DAG, and hybrid flows. Siva also offers insights into the limitations of "vibe coding" for enterprise use cases and stresses the crucial role of robust evaluation (evals) and choosing the right models—from local open-source options to frontier LLMs. Explore the bottlenecks hindering adoption, like custom application integration and data readiness, and learn why Siva believes the biggest opportunity for agent companies may not lie in replacing SaaS platforms but rather in automating the mundane work currently performed by humans.

Chapters

00:22 Introduction and Guest Welcome

00:52 Enterprise Agent Framework

02:48 Building Enterprise-Friendly AI Frameworks

04:56 Enterprise Concerns with Vibe Coding

09:23 Safe and Responsible AI Implementation

11:05 Multi-Agent Orchestration

14:13 Challenges in Multi-Agent Systems

14:22 Enterprise Integration Bottlenecks

17:37 The Role of Low-Code and No-Code Solutions

19:55 Inter-Agent Communication Standards

21:49 Future of AI Agents in Enterprises

29:37 Evaluating AI Agents

36:34 Conclusion and Final Thoughts

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Website: lyzr.ai

LinkedIn: Siva Surendira

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Agent Leaderboard

Are we on the verge of removing all language barriers with AI?

Olga Beregovaya, VP of AI at Smartling, joins host Conor Bronsdon to tackle this question, discussing the evolution from rule-based NLP to today's powerful LLMs. Together, they confront the persistent challenges that stand in the way, like the English-centric nature of AI, domain-specific inaccuracies, and the unpredictability of model hallucinations. Olga unpacks the difficulties faced when striving for accurate, nuanced translation across all languages, especially under-resourced ones.

Beyond these hurdles, the conversation explores the cutting-edge opportunities and technical innovations driving progress, including RAG, the rise of purpose-built models, agentic AI workflows, and the potential of multilingual multimodality. Olga shares insights into boosting translator productivity, achieving more predictable quality, and the path toward human parity in translation, examining how technology and human expertise will shape the future of global communication.

Chapters

00:00 Introduction and Guest Welcome

01:14 Evolution of NLP: From Rule-Based to Machine Learning

02:40 Challenges in AI Translation

04:21 Biases in Language Models

05:28 Inference Time and Latency

05:44 English-Centric AI Models

08:53 Opportunities in AI Translation

09:14 Industries Benefiting from Language AI

10:36 Human-in-the-Loop Translation

12:06 Architectural Innovations in Language AI

16:20 Success with RAG Architectures

17:58 Multilingual Vectorization

19:54 Agentic AI in Translation

24:35 Data Sets and Data Privacy

28:30 Using Smaller, Purpose-Built Models

32:10 Future of AI in Translation

36:37 Conclusion and Farewell

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LinkedIn Olga Beregovaya

LinkedIn ⁠Smartling

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What if you could turn a requirement document into a full enterprise application in just minutes?

Rodrigo Coutinho, co-founder and AI Product Manager at OutSystems, joins hosts Conor Bronsdon and Atin Sanyal to explore this new reality of AI-driven development. Rodrigo shares insights from OutSystems' nearly 25-year journey, detailing their early adoption of AI and the development of their AI platform, Mentor. Discover how the pairing of AI and low-code empowers developers, accelerates the creation of enterprise applications, and shortens the cycle from idea to deployment.

But this newfound speed brings its own set of challenges. The discussion addresses the hurdles of managing AI-generated code, contrasting experiences with traditional versus low-code approaches. Learn why a dev's focus pivots from syntax to strategy, pinpointing human creativity and ideation as the crucial limiter in today's development lifecycle.

Chapters

00:00 Welcoming Rodrigo Coutinho of OutSystems

01:30 OutSystems' Early AI Journey (Pre-LLM)

03:30 The LLM Revolution & OutSystems Mentor Emerges

07:30 The Critical Need for Validating AI-Generated Apps

12:00 The Shifting Role of the Modern Developer

13:30 Quality Control & Accountability in the AI Era

16:00 Low-Code's Edge in AI Validation

18:30 OutSystems Mentor: A Deeper Look

23:30 Choosing the Right AI Models (In-House vs Public)

27:30 Future Opportunities: Speed, Experimentation & Multimodal AI

37:00 The Use Case Hurdle & Final Thoughts

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Website www.outsystems.com

OutSystems Mentor

LinkedIn Rodrigo Sousa Coutinho

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Generative AI dominates the conversation, but does it actually make it easier to build, lead, and sustain high-performing engineering teams?

Host Conor Bronsdon sits down with Charity Majors, co-founder and CTO of Honeycomb (.io), and the mind behind charity.wtf. Known for her sharp insights and unfiltered opinions, Charity kicks off the discussion by expanding on her popular article: 'Generative AI is not going to build your engineering team for you.' Together, they explore how AI has altered the dynamics for engineering teams and leaders. 

The discussion navigates the complex dynamics of hiring in an AI-enabled era, challenging the "senior-only" trend and championing the vital role of junior engineers in creating learning organizations. Charity also explains why writing code is often the "easy part" compared to the full lifecycle of owning and operating systems, a challenge amplified by AI-generated code. 

Finally, Conor and Charity discuss the risk of "cognitive decay" from over-reliance on AI tools and why fostering deep system understanding remains paramount for engineers and leaders.

Chapters

00:00 Introduction and Guest Welcome

01:51 Generative AI and Engineering Teams

02:26 The Writing Process and Inspiration

03:49 AI's Impact on Hiring and Team Building

05:30 Embracing AI and Automation

07:43 The Role of Junior Engineers

09:33 Building Effective Engineering Teams

17:01 Future of AI in Code Generation

20:07 High Performing Engineering Teams

21:48 Evolving Expectations for Engineering Managers

22:41 Cognitive Decay

25:00 Feedback Loops in Software Systems

26:56 Hiring for Potential vs. Experience

29:17 The Future of Observability

39:50 Closing Thoughts and Advice for Engineers

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Follow Charity: charity.wtf

Learn more about Honeycomb: www.honeycomb.io

Read: Generative AI is not going to build your engineering team for you

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Building trustworthy, scalable AI isn't just about models; it's about navigating a complex ecosystem of tools and regulations. 

Join hosts Conor Bronsdon and Atindriyo Sanyal as they explore these challenges with Dr. Maryam Ashoori, Head of Product for watsonx AI at IBM. To meet these challenges, Maryam explains how watsonx simplifies the AI stack, automates pipelines, and empowers enterprises to scale their AI operations while optimizing costs rapidly.

Maryam also explores IBM's strategy for leveraging open-source and commercial models, enabling the potential of agentic systems. Plus, she shares insights from a recent survey of 1,000 developers, revealing key takeaways about the current landscape for enterprise AI implementation, and what results mean for both developers and the enterprises they support.

Chapters

00:00 Introducing Dr. Maryam Ashoori

01:13 Overview of IBM's AI Strategy

01:47 Enterprise AI Challenges and Solutions

04:40 IBM's Approach to AI Models and Tooling

09:52 Simplifying the AI Stack

12:20 Challenges in Agentic AI

15:55 Importance of Data Management and Lineage

21:11 IBM's Strategy for Gen AI Products

23:43 Scaling Challenges with Agents

27:40 Effective Agent Evaluation Systems

35:18 Gaps and Opportunities in AI Tooling

41:35 Success Stories with watsonx

44:00 Closing Remarks

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watsonx.ai

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What if everyone in your organization had equal information at all times? Would meetings even exist? 

This week, we dive into the concept of information symmetry with Manoj Agarwal, co-founder and president of DevRev. Manoj, along with hosts Conor Bronsdon and Yash Sheth, explores how DevRev is connecting data, personalizing schemas, and automating complex tasks, offering a glimpse into the next generation of AI-driven workflows. This is revolutionizing enterprise data and decision-making by breaking down the silos that create information asymmetry.

Learn how AI is reshaping business outcomes and collaboration, moving us closer to a world where everyone has the information they need.

Chapters:

00:00 Welcome to Chain of Thought

00:57 Information Symmetry in Enterprises

02:03 Challenges of Decision Making

03:41 Recency Bias and Product Management

04:58 Data Silos and Information Waste

05:23 Structured vs. Unstructured Data

06:04 Collaboration and Data Retrieval Issues

08:17 DevRev's Approach to AI and Data Integration

09:23 Building a Business-Centric Knowledge Graph

10:00 Conversational AI and Automation

12:57 Agentic Interactions and Skills Programming

20:05 Multi-Agent Systems and Future Vision

21:25 Challenges in Multi-Agent Communication

25:10 Data Cleanliness and Governance

28:14 Trust and Reliability in AI Systems

36:58 Conclusion and Future Outlook

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devrev.ai

DevRev University

LinkedIn Manoj Agarwal

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Is the agentic AI bubble about to burst? Kelly Vaughn, Director of Engineering at Spot AI, questions whether the agent craze is overpromising potential and leading startups down a path of unsustainable expectations.

Never one to shy away from a hot take, Kelly joins host Conor Bronsdon for a pragmatic look at AI, discussing the differences between building AI-enabled and traditional software, why replacing humans with AI teams will backfire (looking at you customer service), and the proliferation of AI tools.

Kelly also shares insights on constructing AI teams, navigating data governance, and building user trust while avoiding common startup pitfalls.

Chapters:

00:00 Introduction and Guest Welcome

01:13 Is Agentic AI a Bubble?

02:40 Startup Challenges and Market Noise

11:02 Building AI Products vs. Traditional Software

17:31 Ethical Implications and Governance

19:48 Constructing AI-Enabled Teams

22:07 AI Tooling and Productivity

26:00 Questioning Productivity Claims

32:28 Conclusion and Final Thoughts

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Kelly’s Newsletter The Modern Leader

LinkedIn Kelly Vaughn

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Imagine cutting your legacy code modernization timeline from years to months. It’s no longer science fiction and this week’s guest is here to tell us how. 

Rachelle Palmer, Director of Product Management at MongoDB, joins hosts Conor Bronsdon and Atindriyo Sanyal, for a discussion on the groundbreaking ways AI is modernizing legacy applications. 

At MongoDB, Rachelle's forward-deployed AI engineering team is tackling the challenge of transforming complex, outdated codebases, freeing developers from technical debt. She details how LLMs are automating tasks like improving documentation, test generation, and even business logic conversion, dramatically reducing modernization timelines from years to months. What once demanded teams of dozens can now be achieved with a small, highly efficient team.

Chapters:

00:00 Introduction and Host Welcome

00:58 Challenges in Modernizing Legacy Applications

02:52 Real-World Examples of Code Modernization

04:00 The Role of LLMs in Code Modernization

08:01 Measuring Success in AI-Powered Modernization

12:28 The Future of AI in Engineering

16:17 Evaluating Modernization Success

21:12 Returning to Your Startup Roots

29:07 Forward Deployed AI Engineers

35:36 Importance of Academic Research in AI

42:10 Conclusion and Farewell

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⁠Rachelle PalmerMongoDBApplication Modernization Factory

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This week, we're sharing a special episode courtesy of 'Dev Interrupted.' Our co-host, Galileo CEO Vikram Chatterji, recently joined theDev Interrupted team for an engaging discussion on AI strategy. We were so impressed by the conversation that we wanted to share it with our audience, and they were kind enough to let us. We hope you enjoy it!

From Dev Interrupted:

"Vikram Chatterji joins Dev Interrupted’s Andrew Zigler to discuss how engineering leaders can future-proof their AI strategy and navigate an emerging dilemma: the pressure to adopt AI to stay competitive, while justifying AI spending and avoiding risky investments.

To accomplish this, Vikram emphasizes the importance of establishing clear evaluation frameworks, prioritizing AI use cases based on business needs and understanding your company's unique cultural context when deploying AI."

Chapters:

00:00 Introduction and Special Announcement

01:14 Welcome to Dev Interrupted

01:42 Challenges in AI Adoption

03:16 Balancing Business Needs and AI

06:15 Crawl, Walk, Run Approach

10:52 Building Trust and Prototyping

13:07 AI Agents as Smart Routers

13:50 Galileo's Role in AI Development

16:25 Evaluating AI Systems

25:36 Skills for Engineering Leaders

27:35 Conclusion

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Podcast

Substack

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Andrew

Ben

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Google’s strength in AI has often seemed to get lost in the midst of OpenAI announcements or DeepSeek fervor - yet Gemini 2.0 is more than good for many tasks; it’s the model to beat - and we have the research to back it up. 

This week, Logan Kilpatrick, senior product manager at Google DeepMind, joins us to discuss Gemini’s creation story, its emergence as the premiere model in the AI race, and why the launch of Gemini 2.0 is great news for developers.

During the conversation Conor and Logan explore the exciting world of multimodal AI, Gemini's strengths in agentic use cases, and its unique approach to function calling, compositional function calling, and the seamless integration of tools like search and code execution.

They also chat about Logan’s vision for a future where AI interacts with the world more naturally, offering a view of the potential of vision-first AI agents, and why Google's hardware advantage is enabling Gemini's impressive performance and long context capabilities. 

Follow along with the discussion using Galileo’s AI Agent Leaderboard:https://huggingface.co/spaces/galileo-ai/agent-leaderboard

Chapters:00:00 DeepMind's Role in Gemini's Development

03:49 Gemini 2.0 Updates and Developer Highlights

06:08 Agentic Use Cases and Function Calling

11:29 Multimodal Capabilities

16:15 Putting AI in Production

21:06 Gemini's Differentiation and Hardware

31:22 Future Vision for Gemini and G Suite Integration

35:23 Gemini for Developers

39:02 Conclusion and Farewell

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Twitter:@OfficialLoganK

LinkedIn:https://www.linkedin.com/in/logankilpatrick/

Show Notes

Try Gemini for yourself:gemini.google.com

Gemini for Developers:aistudio.google.com

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