SE Radio 729: Garth Mollett on AI Supply Chain Security

Birgitta Boeckeler, a Distinguished Engineer and consultant focused on AI-assisted software delivery at Thoughtworks, joins host Priyanka Raghavan for a deep dive into harnesses for AI agents. The episode begins by unpacking the concept of harnesses and harness engineering before exploring the core building blocks — guides and sensors — that help AI agents operate more reliably in engineering environments. Priyanka and Birgitta discuss practical implementations of harnesses in real-world workflows, including the use of guides with .MD files and sensors with tools such as SonarQube and Semgrep, which steer agent behavior. The episode also explores how harnesses integrate with existing CI/CD pipelines and pull-request processes. Birgitta describes how stronger harnesses can improve trust in AI-generated code, while emphasizing that harnesses themselves require continuous maintenance as underlying foundation models evolve. The episode concludes with a thoughtful discussion on accountability between humans and agents, along with future directions for harness engineering and AI-assisted software development.

Garth Mollet, Senior Principal Product Security Engineer and Technical Advisor for Product Security at Red Hat, joins host Robert Blumen for a discussion of AI supply chain security. They start with the basics of supply chain security, including the key components of the AI supply chain, and how it differs from the conventional software supply chain. Garth discusses whether the attacks target model weights or inference, and describes the most common attacks and what's in it for the attacker, whether exfiltration, credentials, sabotage, or resources. The episode also considers SPIFFE, SPIRE, attestation, workload identity, and whether AI has the equivalent of "reproducible builds." Brought to you by IEEE Computer Society and IEEE Software magazine.

Clare Liguori, a Senior Principal Engineer who works on developer tooling and agentic AI at Amazon Web Services, speaks with host Sri Panyam about the Amazon Strands Agents SDK. This episode explores the philosophy, design decisions, and emerging patterns behind building production-grade AI agents. Clare frames any agent as three core components: a model, a set of tools, and a prompt. During this interview, she describes the origin story of Strands, the model-driven approach vs. workflows and custom orchestration, steering hooks, tools and MCP, sub-agents and multi-agents, memory layers, production readiness, testing and evaluation starting with use cases where trajectories can be evaluated deterministically, and anti-patterns for newcomers. She describes what's next for Strands, and offers some closing advice for getting results from working with agents

Jeroen Janssens, Head of Developer Relations at Posit, and Thijs Nieuwdorp, a developer relations engineer at Polars, speak with host Gregory M. Kapfhammer about Polars, a Python package for transforming, analyzing, and visualizing data. After discussing the key features, they explore the implementation and use of the expressions data type provided by Polars. Along with comparing Polars to other data-manipulation packages like Pandas, they also share best practices for performing data analysis in Python with Polars. Jeroen, Thijs, and Gregory also discuss topics such as how to interface Polars with a SQL database.

Scott Kingsley, a VP of Engineering at SmartBear, speaks with host Gregory Kapfhammer about the Swagger ecosystem. They discuss the user interface, editor, and Swagger CodeGen and how these tools support the creation and documentation of OpenAPI-compatible APIs. Scott describes how Swagger fits into frameworks like FastAPI, as well as how Swagger APIs can be exposed through the Model Context Protocol (MCP). The discussion closes with best practices for designing and testing APIs and the role that APIs play in a landscape in which AI agents are building and interacting with APIs.

Danny Yang and Sam Goldman, both Software Engineers at Meta, speak with host Gregory M. Kapfhammer about the Rust-based Pyrefly type checker for Python. After a look at the foundational concepts for annotating and checking types for Python programs, Danny and Sam present a deep dive of the implementation of Pyrefly. While comparing and contrasting against various type checkers, they also describe how Pyrefly implements the language server protocol (LSP) for Python. The episode explores a range of other topics, including how to balance the features, performance, and language integrations of a type checker.

Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.

Dave Airlie, a Distinguished Engineer at Red Hat, speaks with host Gregory M. Kapfhammer about Linux kernel maintenance. After over-viewing the scale and structure of the Linux kernel, they dive deep into the review and validation of kernel patches, drawing on examples from the GPU subsystem. After discussing the features and benefits of the Linux kernel's maintenance model, they also explore kernel maintenance best practices and the supporting tools for these practices. Dave and Gregory also discuss topics such as the integration of Rust code in the Linux kernel and the ways in which AI-driven code review are influencing kernel maintenance.

Dwayne McDaniel, developer advocate at GitGuardian.com, joins host Priyanka Raghavan to talk about the engineering challenges of secrets management. They explore what "secrets" really are in modern systems—far beyond passwords—including API keys, tokens, certificates, and machine identities, and how "secret sprawl" emerges across the SDLC. Drawing on reports from GitGuardian and Verizon, they discuss the growing scale of secret leaks and why credential abuse and phishing remain dominant attack vectors. They examine common leak points—from code repos and logs to CI/CD pipelines, containers, and SaaS integrations—and how cloud, DevOps, and AI tooling are amplifying risks. Priyanka quizzes Dwayne about recent supply chain attacks from pyPi and trivy ecosystems, highlighting recurring root causes like poor access control, long-lived credentials, and weak security hygiene. Finally, they consider detection, response, and modern solutions—short-lived credentials, secret scanning, and identity-based approaches like OWASP NHIR and SPIFFE/SPIRE—ending with practical advice for engineers to reduce blast radius and design for secure secret lifecycle management.

In this episode, Rob Moffat, author of Risk-First Software Development and chief technical architect at the FinTech Open Source Software Foundation (FINOS), speaks with host Brijesh Ammanath about how all of software development is actually risk management. Rob introduces the concept of 'risk-first software development,' which sits in the context of existing methodologies like scrum and kanban. Showcasing multiple real-world project patterns to illustrate how things can go wrong when risk is ignored, he makes the case for why risk should be the primary lens behind every development decision, from architecture to prioritization. Through various examples, he shows how every developer action can be viewed as a risk trade-off and why making that explicit can lead to better outcomes. The conversation takes a deep dive into the risk-first framework and how teams can apply it in their existing processes.