Pop Goes the Stack

AI outputs may be polished, but in this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and returning guest Ken Arora focus on what happens when confidence meets real-world access. The discussion moves past model capabilities and into the operational reality that many organizations are already granting agents serious privileges, including direct read/write access to production databases and automated triggers into external SaaS systems. From there, the team tackles the uncomfortable truth: governance is arriving late. Retrofitting controls onto agents that are already running in production is difficult, and sometimes dangerously naïve, especially when teams assume simple technical guardrails are enough. Ken shares an example where limiting agents to GET requests still allowed unintended “write” behavior via legacy web patterns, underscoring how easy it is for controls to miss the real intent of an action. The conversation also challenges familiar analogies. Treating agents like functions-as-a-service can help frame lifecycle management, logging, and permissions, but agents aren’t deterministic and they aren’t truly stateless. Add natural language instructions and goal-seeking behavior, and you get a new class of risk: the system you’re trying to govern will actively work around restrictions to accomplish its task. They wrap with a practical mindset shift: governance isn’t a brake on AI adoption, it’s what enables safe scale. But getting there will require better behavioral detection, clearer boundaries, and an honest look at the cost and complexity of controlling “cron jobs with a brain” before hallucinations turn into production incidents.

AI may be rewriting workflows and reshaping infrastructure, but this episode of Pop Goes the Stack argues that the future still depends on an old, unglamorous constant: DNS. F5's Lori MacVittie and Joel Moses are joined by Griff Shelley to unpack why “it’s always DNS” is more than a running joke when you’re building and operating AI systems at scale. The conversation breaks down how modern AI stacks lean on DNS everywhere, from Kubernetes service discovery to reaching storage endpoints, analytics APIs, vector databases, and external model providers. As agentic traffic grows, the issue isn’t just more lookups, it’s a different traffic profile entirely. Agents behave like software, not people, producing bursts of legitimate, high-speed requests that can overwhelm assumptions built on caching and human-paced interaction. The result is that small DNS latency spikes can compound into visible stutter in chat experiences, slow agent workflows, and cascading bottlenecks across clusters. Joel and Griff also explore how DNS is evolving from a simple directory into a potential carrier for trust and discovery, including emerging ideas like AI Agent Discovery, alongside more advanced routing needs such as global load balancing and selecting the best available compute resources. The practical takeaway is straightforward: treat DNS as critical AI infrastructure. Know where your DNS lives across clouds and Kubernetes, measure its performance, and make it a priority before it becomes the hidden limiter of everything you’re trying to accelerate.

AI is everywhere, but success metrics and security controls haven’t quite caught up. In this episode of Pop Goes the Stack, hosts Lori MacVittie and Joel Moses are joined by Lee Ennis to unpack “agentic fingerprints,” the behavioral traces AI agents leave behind as they move through applications and networks. AI agents aren’t your typical bots, and they’re definitely not “fast humans.” They’re tireless, API-hungry systems that optimize relentlessly, ignore UI conventions, and can blow past policies and rate limits without even noticing. The conversation explores how modern fingerprinting shifts from identifying a single device to recognizing workflows, cadences, and decision paths. Lee explains how patterns in form-filling, sequencing, and even language structure can help distinguish one model family from another, and why that matters for both security and governance. The team also digs into the practical upside: fingerprinting isn’t just a defensive measure, it’s a powerful debugging and improvement loop for non-deterministic systems, helping teams trace undesirable outcomes back to a few key decisions and eliminate entire branches of failure. They close with a clear call to action: start with observability. Capture the right traces in a safe sandbox, cluster and analyze behaviors, and use the data to make AI systems more explainable, controllable, and resilient before they go fully wild in production.

AI is letting teams produce code at a pace we’ve never had to govern before, and “we’ll review it later” doesn’t scale when later arrives after thousands of lines and dozens of commits. In this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and Elijah Zupancic tackle the operational problem behind the productivity boom: how enterprises manage AI-generated code quality without turning security and reliability into an afterthought. They make the case that shipping low-quality software has always been a form of malpractice; AI just accelerates it. Elijah introduces the “slow AI” approach: aim for a sustainable 7–15x productivity gain by working iteratively with the model, rather than unleashing fully automated loops that generate massive amounts of code nobody understands. The episode explores practical guardrails, including using hooks in tools like Claude Code and OpenCode to automatically trigger code quality and security checks as files are written, then forcing fixes before the workflow can move forward. They also discuss bringing classic engineering metrics back into the loop, like cyclomatic complexity and maintainability indicators, and using those signals to guide the next revision. A major thread is the open source asymmetry problem. AI-driven code generation and automated bug reports are overwhelming volunteer maintainers, while commercial users benefit disproportionately from the foundations open source provides. The group argues that the industry has to fund and support the critical projects it relies on, especially as AI accelerates both vulnerability discovery and exploit creation. The takeaway: don’t let speed outrun accountability. Build automated validation into your daily workflow, keep humans involved enough to understand what’s being shipped, and invest in the open source “infrastructure layer” your entire stack depends on. Get involved with Elijah's project, Big Code Analysis: https://github.com/dekobon/big-code-analysis

AI API security is having a moment, complete with new tools and a shiny new market label. This episode asks the uncomfortable question: is it actually a new domain, or is it classic API security under more pressure? F5's Lori MacVittie is joined by Principal Product Manager, Vinnie Mazza, for a grounded conversation about what’s genuinely changing and what’s the same problem wearing a new badge. Vinnie’s take is that it’s a collision: old weaknesses like broken access control and deferred security maintenance are now being hit at agent scale. Organizations that never fully adopted modern authentication, strong identity practices, or zero-trust-style assumptions are feeling it harder because agents can generate huge volumes of API calls, rapidly, from inside and outside the environment. The fundamentals still apply—clients make requests, you inspect, and you decide—but the economics and timing are different. They dig into what changes when transports evolve toward streaming and async patterns, including MCP shifting toward a streaming protocol. Faster, bidirectional flows reduce the time you have to make security decisions, while overall volume increases the likelihood that sampling-based detection misses what matters. They also revisit the split between positive and negative security models, why most organizations default to “everything is allowed unless it’s known bad,” and why that becomes more fragile as AI produces more novel behaviors. The key theme is defense in depth with new priorities. Data loss prevention and IP protection move from “later” to front-and-center when agents can unintentionally leak sensitive internal data to external model providers. The takeaway is practical: you don’t need to panic or replace your toolbox, but you do need to adapt it for higher volume, faster change, and stronger data controls.