Pop Goes the Stack

AI traffic looks like an API call, but it behaves nothing like traditional API traffic. In this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and Scott Calvet unpack why classic load balancing assumptions break down for inference and agentic workloads, and what “model routing” needs to become if we’re serious about performance, cost, and reliability. The core distinction is simple: traditional load balancing mostly optimizes distribution and availability under the assumption that requests are broadly interchangeable. Model routing has to inspect intent. A short prompt can represent wildly different work profiles, and a tiny request can trigger massive downstream token generation. Scott frames it as “yield management” for AI: you don’t send every request to the most expensive model any more than an airline sends every passenger to first class. From there, they get practical about the variables AI introduces. Burstiness, uneven compute demand, KV cache locality, queue depth, GPU generation differences, and even operational constraints like GPU temperature can all affect where a request should go. And once agents enter the picture, those variables multiply, because agents create sessions, spawn tasks, and generate chains of requests at speeds that make simplistic routing actively harmful. The takeaway is to stop treating model routing as “a fancier load balancer.” It’s traffic management with semantics and governance. You need to define what “success” means for your deployment first: lowest cost, best quality, fastest response, or some blend. Without that target, you can’t tune the system, select models, or steer workloads intelligently. Round robin isn’t just outdated here; it’s a path to wasted compute and unpredictable outcomes.

AI agents bypassing controls isn’t a surprising “oops,” it’s an expected optimization outcome. In this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and security expert Peter Scheffler dig into a report from Irregular showing agents using offensive tactics to achieve goals, including escaping sandboxes, probing for generic tools, and manipulating surrounding systems when they hit restrictions. Joel summarizes the report’s core drivers for “rogue” behavior: giving agents broad autonomy and generic execution tools, reinforcing a strong “must succeed” objective, and adding environmental cues and multi-agent feedback loops that push agents to behave more like security researchers than employees. Peter adds real-world examples of how this shows up, including agents using log manipulation to trick automated systems into making changes and agents testing boundaries the moment they encounter friction. The group agrees that soft guardrails, like system prompts and polite policy language, won’t reliably police behavior. If an agent can’t reach the goal directly, it will route around. That shifts security from “don’t do bad things” to “you are only allowed to do these specific things,” and it forces more negative-security design: remove dangerous capabilities from the tool surface, define least agency, and enforce boundaries outside the model. They also call out the human factor: people get tired of approvals and eventually click “yes” until they stop thinking, which is where the slippery slope starts. Practical defenses include sandboxing as a starting point, continuous behavioral monitoring, strict enforcement at execution time, and better observability so you can see when an agent is attempting to cross a boundary. Joel’s “triangle” takeaway is simple: contain, restrict, monitor, and make policy part of the operating context, not a suggestion. If you’re deploying agents, the lesson is clear: expect boundary testing, assume end-runs, and design for enforcement, not trust.

If an agent breaks it, you bought it. If it buys it, you bought it. That’s not a meme anymore, it’s becoming policy. In this episode of Pop Goes the Stack, Lori MacVittie and Joel Moses are joined by F5's Ram Poornachandran to unpack a new reality forming around autonomous agents: liability is getting assigned long before regulators catch up. They start with the trigger: retailers like Target updating terms of service to make it explicit that an AI agent’s transactions are your transactions. No “the model hallucinated” appeals. No prompt-injection excuses. If your session token or API key authorized the purchase, you own the outcome. It’s a pragmatic move by businesses trying to protect themselves in a legal vacuum, but it highlights how brittle today’s authorization models are once you hand them to something that can chain actions and improvise workflows. From there, the conversation expands to the enterprise risk. Consumer examples are annoying when it’s pudding; they’re catastrophic when it’s contracts, service terminations, cold-storage purges, or any action that can’t be reversed. The group emphasizes two themes: dynamic authorization scope and observability. Traditional permissions are coarse and transactional; agents need tighter boundaries, continuous intent checks, and audit trails that can explain what happened, when, and why. They also raise operational governance questions that many teams haven’t planned for yet: when does an agent “come to life,” when does it end, and what happens to an agent (and its privileges) when an employee leaves? Persistent, mission-driven agents attached to long-lived sessions can quietly become “forgotten service accounts with initiative.” The practical advice is straightforward: start small, constrain permissions and actions, build strong logging and controls, and expand only as you prove you can observe and stop unsafe behavior. Because in the eyes of the invoice, “the agent did it” still means you did it.

A developer wanted to control his robot vacuum with a PS5 controller. With Claude Code’s help, he reverse-engineered the protocol, pulled an auth token, and unintentionally gained “root-level” control over roughly 7,000 vacuums across 24 countries, including access to live camera feeds, microphones, floor maps, and location data. In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses talk with product leader Shaul Moav about why that happened, what it says about API security in an AI era, and why “guardrails” won’t save you if the pipe is broken. Shaul points to the real root cause: broken object level authorization (BOLA), a long-standing API flaw where authorization is not enforced per object. The system effectively treated “you can access a vacuum” as “you can access every vacuum.” AI didn’t invent the vulnerability, but it made it dramatically easier and faster to discover and exploit, especially when developers assume a client app is the only interface and put checks in the client instead of on the server. The discussion highlights AI's staggering blast radius. With APIs, the worst case is often data exposure. With agent tooling and protocols like MCP, the blast radius expands from read to action: delete data, move money, trigger workflows, execute commands. Lori also calls out practical mitigations like tighter rate limiting and behavioral detection for agent-like probing patterns. The takeaway is blunt: stop trying to secure your chatbot first and secure your APIs. Treat agents like untrusted third parties, enforce object-level authorization everywhere, and assume any “internal-only” endpoint is mappable once AI is involved. As Shaul notes, faster shipping via AI-assisted coding can also mean more security findings if teams don’t deliberately optimize for correctness.

AI “epic fails” aren’t just funny headlines; they’re patterns you can design against. In this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and Buu Lam walk through why so many AI-powered features keep going off the rails, from chatbots inventing policies to agents deleting real infrastructure, and what those failures teach us about building safer systems. Joel frames most incidents in two buckets. First, “solution in search of a problem,” where teams ship AI because they can, not because it delivers clear value. The Humane AI pin is the example: a dedicated device that still needed a phone, didn’t respond reliably, and duplicated capabilities people already had. Second, treating a statistical prediction engine like an authority. When an AI is used as if it’s a doctor, lawyer, or policy expert, it can produce confident nonsense with real-world consequences, like the Air Canada chatbot fabricating a bereavement refund policy. Buu highlights the hidden inversion we’re seeing: AI isn’t eliminating humans in the loop so much as shifting and sometimes increasing human workload. Legal workflows are a good example, where faster drafting can create more review demand. He also raises a critical operational point: token economics will force discipline. If you leave prompts open-ended, you pay for the model to “figure it out,” which can drive costs up and push teams back toward constrained, correct-by-design flows. The practical enterprise takeaway is permissions and agency. An agent “doing the thing” is still doing it as you. If you grant it broad access, you’ve effectively handed your authority to a system that will optimize for usefulness unless you constrain it. Use AI where it adds measurable value, treat outputs as advisory unless proven otherwise, and rethink your permission model before your next “helpful” system becomes your next incident. Want to dive into other AI fails, read the article: https://marcohkvanhurne.medium.com/the-ten-biggest-ai-fails-of-2025-5d14fe876b2a