Durable execution has been solving reliability problems for decades. Now it's the missing primitive for agentic AI systems.

We're joined by Jeremy Edberg, C-Suite Advisor* at DBOS, Inc., to explore how durable execution works, why it matters for building production-ready AI agents, and what lessons from Netflix-scale reliability engineering apply directly to the age of agentic coding.



*Since recording, Jeremy has become C-Suite Advisor to DBOS, Inc.

KEY TAKEAWAYS

00:00 Introduction 

01:30 DBOS was born from research into replacing the operating system with a database. 

04:00 A career spent making things reliable — from Reddit to Netflix — built Jeremy's obsession with automation. 

09:00 If you do something twice, automate it; make manual steps look like automation. 

12:30 Durable execution lets systems resume from exactly where they last succeeded. 

14:00 Replaying saved LLM outputs removes non-determinism from agentic debugging. 

16:00 In agentic coding, the bottleneck isn't writing code — it's deployment, testing, and operations. 

20:00 DBOS adds roughly 1% overhead to Postgres, making durable execution practical at scale.

28:00 Agentic AI created a new class of workloads between instant API calls and long bat

RESOURCES MENTIONED

DBOS, Inc

https://www.dbos.dev

Transact library

https://www.dbos.dev/dbos-transact

CockroachDB

https://www.cockroachlabs.com

Dosu

https://dosu.dev/

Thanks for listening to “Hello Agent!: The podcast at the intersection of data & agents.” If you loved this episode, let us know with a 5-star review! Remember to subscribe so you don’t miss an episode. To learn more about Redpanda, visit redpanda.com

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Dominik Tornow, Founder and CEO at Resonate HQ, discusses simplifying distributed systems and why durable execution is becoming critical in the age of AI agents.

We explore how agents introduce new challenges for distributed systems, why long-running autonomous workflows require interruption-tolerant execution, and how durable promises provide a simpler mental model for building reliable multi-agent systems.

Key Takeaways:

00:00 Introduction.

02:20 A career start during the rise of AWS and cloud computing shaped Dominik’s focus on distributed systems.

04:10 Separating the concept of durable execution from specific implementations led to the idea of interruption-tolerant systems.

05:15 Failures as interruptions — voluntary or involuntary — lead to a broader, more practical model for resilient execution.

12:40 Simplifying complex concepts and why engineers often struggle to align their mental models when designing distributed systems.

14:10 Dominik defines an agent as a combination of a model, tools, and a system prompt, while distinguishing between an agent definition and a running agent instance.

23:30 Many effective agents are simply tool-calling loops, despite the complexity of the surrounding ecosystem.

26:20 Running agents in enterprise environments introduces distributed systems challenges, especially when workflows involve long-running or expensive operations.

33:15 Unlike traditional microservices, retrying agent operations can be extremely expensive when LLM calls or data pipelines are involved.

40:20 A durable promise is a simple mechanism for coordination and recovery in distributed agent workflows.

Resources Mentioned:

Dominik Tornow

https://www.linkedin.com/in/dtornow/

Resonate HQ | LinkedIn

http://www.linkedin.com/company/resonatehqio/

Resonate HQ | Website

https://www.resonatehq.io/

"Think Distributed Systems" by Dominik Tornow

https://www.manning.com/books/think-distributed-systems

Temporal (durable execution platform)

https://temporal.io

Thanks for listening to “Hello Agent!: The podcast at the intersection of data & agents.” If you loved this episode, let us know with a 5-star review! Remember to subscribe so you don’t miss an episode. To learn more about Redpanda, visit redpanda.com

#RealTimeData #DataStreaming #Redpanda

Building reliable agents begins with a strong data foundation. As AI systems move from experiments to production, teams need clearer ways to connect data, models, and real-world decision-making. In this inaugural episode of “Hello, Agent!” we talk with Nicolas Dupont, Founder and CEO at Cyborg Inc., about how agents are changing the way teams think about automation, streaming data, and system design. Nicolas shares practical lessons from building Cyborg, what breaks when agents scale, and how engineers can design systems that stay flexible as complexity grows.

Key Takeaways:

00:00 Introduction.

02:00 Nicolas’ focus on solving technical problems for the real world.

05:43 Programming in C builds a deep appreciation for low-level systems.

09:39 Securing proprietary data matters more than models in enterprise AI.

15:32 Vector data can expose sensitive information to attackers.

20:59 Auth systems are often weaker than expected, even for sensitive data.

25:03 Agentic AI removes the perimeter, making zero trust essential.

30:06 Poisoned data is hard to trace across agent systems.

35:07 Deleting keys makes sensitive data unreadable immediately.

40:48 Modern cryptography is no longer a bottleneck, making strong encryption practical.

45:06 Zero trust isn’t optional — the future of agentic AI depends on it.

Resources Mentioned:

Nicolas Dupont

https://www.linkedin.com/in/cyborgnico/

Cyborg Inc. | LinkedIn

https://www.linkedin.com/company/cyborginc/

Cyborg Inc. | Website

https://www.cyborg.co/

Cyborg Blog: Vector Embeddings Are Not One-Way Hashes 

https://www.cyborg.co/blog/vector-embeddings-are-not-one-way-hashes

Thanks for listening to “Hello Agent!: The podcast at the intersection of data & agents.” If you loved this episode, let us know with a 5-star review! Remember to subscribe so you don’t miss an episode. To learn more about Redpanda, visit redpanda.com

#RealTimeData #DataStreaming #Redpanda