How to Build AI Systems You Can Trust
Large language models are incredibly powerful at understanding language, but that doesn't mean they should be making business-critical decisions on their own. In this episode of Code & Cognition, we explore what it actually takes to build AI systems that are safe enough for production. The conversation focuses on the engineering practices that make AI reliable in real-world environments. We discuss why observability is essential from day one, how deterministic control layers help prevent unpredictable behavior, and why every AI decision should be measurable, explainable, and reversible. We also cover the importance of regression testing as models evolve and why structured outputs should always be validated before triggering downstream actions. If you're building AI-powered products, customer-facing workflows, or applications in regulated industries, this episode offers practical insights into designing systems that are predictable, auditable, and built to earn trust over time. Chapters 00:00 – Introduction: Building Safe AI Systems in Production 01:06 – Designing for Safety: Guardrails & Secure Deployment 02:00 – Human-in-the-Loop for Healthcare & Compliance Workflows 03:31 – HIPAA, BAAs, and Compliance Infrastructure 04:39 – Separation of Environments & Auditability 06:02 – Evaluating LLM Updates with a Production Test Suite 07:07 – Building a Test Harness with Real-World Conversations (200+ Cases) 09:00 – Optimizing Test Performance & Cost 11:56 – Where LLMs Should NOT Be Used (Critical Boundaries) 14:30 – Closing Thoughts: Control, Constraints, and Reliable AI Systems Olio Apps can help you build production-ready AI applications: https://www.olioapps.com/contact-us Subscribe to our monthly newsletter: https://www.olioapps.com/newsletter For the algorithm: #EnterpriseAI #AIEngineering #Observability #LLM #ArtificialIntelligence #DeterministicAI #SoftwareArchitecture #WorkflowAutomation #AIInfrastructure #MachineLearning #CustomerExperience #HealthcareTechnology #TechPodcast #CodeAndCognition #OlioApps

Architecture of Deterministic Chatbots

The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents

Making Deterministic Chatbots | Workflows for Predictable Chatbots

Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026

Guardrails Are Just Suggestions: Securing AI Agents with Okta's Kevin Akermanis

The AI Skills Nobody is Teaching (And Everyone Needs) | AI Expert Ethan Mollick

Model Context Protocol (MCP) Explained for Beginners: AI Flight Booking Demo!

AI Agents Full Course 2026: Master Agentic AI (2 Hours)

How To Become A Top Tier Architect (Google & AWS Veteran)

Building Agentic Systems for Patient Communication

How AI agents & Claude skills work (Clearly Explained)

How Kent Beck shapes the software engineering industry

Harnesses in AI: A Deep Dive — Tejas Kumar, IBM

Stop Using AI Wrong — Agentic AI vs RAG Explained

AI Development, Streaming, and Scaling with AWS Lambda

The Multi-Agent Architecture That Actually Ships — Luke Alvoeiro, Factory

"Learn AI” Is Bad Advice. Learn This Instead

Don't learn AI Agents without Learning these Fundamentals

You Can Learn AI Agent Harness & Loop Engineering In 19 Min | LLM Ops, Eval, Tracing, RAG

