Spencer Reagan thinks it might be, and he’s not shy about saying so. In this episode, he and Demetrios Brinkmann get real about the messy, over-engineered state of agent systems, why LLMs still struggle in the wild, and how enterprises keep tripping over their own data chaos. They unpack red-teaming, security headaches, and the uncomfortable truth that most “AI platforms” still don’t scale. If you want a sharp, no-fluff take on where agents are actually headed, this one’s worth a listen.

In this session, we’ll explore how developing and deploying AI-driven agents demands a fundamentally new testing paradigm—and how scalable simulations deliver the reliability, safety and human-feel that production-grade agents require. You’ll learn how simulations allow you to: - Mirror messy real-world user behavior (multiple languages, emotional states, background noise) rather than scripting narrow “happy-path” dialogues. - Model full conversation stacks including voice: turn-taking, background noise, accents, and latency – not just text messages. - Embed automated simulation suites into your CI/CD pipeline so that every change to your agent is validated before going live. - Assess multiple dimensions of agent performance—goal completion, brand-compliance, empathy, edge-case handling—and continuously guard against regressions. - Scale from “works in demo” to “works for every customer scenario” and maintain quality as your agent grows in tasks, languages or domains. Whether you’re building chat, voice, or multi-modal agents, you’ll walk away with actionable strategies for incorporating simulations into your workflow—improving reliability, reducing surprises in production, and enabling your agent to behave as thoughtfully and consistently as a human teammate.

Overcome the friction of boilerplate code and infrastructure wrangling by adopting a declarative approach to AI agent development. This article introduces Ackgent, a production-ready template built on Google’s Agent Developer Kit (ADK) and Agent Config, which allows developers to define agent behaviors via structured YAML files while keeping implementation logic in Python. Learn how to leverage a modern stack—including uv, just, and Multi-agent Communication Protocol (MCP)—to rapidly prototype, test, and deploy scalable multi-agent systems on Google Cloud Run.