Build Wiz AI Show

Join us as we tackle the "last mile" of AI Agents series, exploring the rigorous operational discipline required to transform fragile prototypes into reliable, production-grade agents. We break down the essential infrastructure of trust—from evaluation gates to automated pipelines—that ensures your system is ready for the real world. This foundation paves the way for our series finale where we will move beyond single-agent operations to look at the future of autonomous collaboration within multi-agent ecosystems.

In this episode of our AI Agent series, we synthesize our previous discussions on evaluation frameworks and observability into a cohesive operational playbook known as the "Agent Quality Flywheel",. Join us as we explore how to transform raw telemetry into a continuous feedback loop that drives relentless improvement, ensuring your autonomous agents are not just capable, but truly trustworthy,. This episode bridges the critical gap between theory and production, providing the final principles needed to build enterprise-grade reliance in an agentic world,.

Moving beyond the temporary "workbench" of individual sessions, episode 3 of our AI Agents series unlocks the power of Memory—the mechanism that allows AI agents to persist knowledge and personalize interactions over time. We will explore how agents utilize an intelligent extraction and consolidation pipeline to curate a long-term "filing cabinet" of user insights, effectively turning them from generic assistants into experts on the specific users they serve.

In this episode in series AI Agents, we discuss the transformation of foundation models from static prediction engines into "Agentic AI" capable of using tools as their "eyes and hands" to interact with the world. We highlight the complexity of connecting these agents to diverse enterprise systems, setting the stage for Episode 1 to explain the "N x M" integration problem and the Model Context Protocol's role in solving it.

Join us for the premiere of our series on AI Agents, exploring the paradigm shift from passive predictive models to autonomous systems capable of reasoning and action. In this episode, we deconstruct the core anatomy of an agent—its "Brain" (Model), "Hands" (Tools), and "Nervous System" (Orchestration)—to explain the fundamental "Think, Act, Observe" loop that drives their behavior. Discover how this architecture enables developers to move beyond traditional coding to become "directors" of intelligent, goal-oriented applications.

DeepSeek-AI introduces DeepSeek-R1, a reasoning model developed through reinforcement learning (RL) and distillation techniques. The research explores how large language models can develop reasoning skills, even without supervised fine-tuning, highlighting the self-evolution observed in DeepSeek-R1-Zero during RL training. DeepSeek-R1 addresses limitations of DeepSeek-R1-Zero, like readability, by incorporating cold-start data and multi-stage training. Results demonstrate DeepSeek-R1 achieving performance comparable to OpenAI models on reasoning tasks, and distillation proves effective in empowering smaller models with enhanced reasoning capabilities. The study also shares unsuccessful attempts with Process Reward Models (PRM) and Monte Carlo Tree Search (MCTS), providing valuable insights into the challenges of improving reasoning in LLMs. The open-sourcing of the models aims to support further research in this area.

Google's "AI Business Trends 2025" report analyzes key strategic trends expected to reshape businesses. It identifies five major trends including the rise of multimodal AI, the evolution of AI agents, the importance of assistive search, the need for seamless AI-powered customer experiences, and the increasing role of AI in security. The report suggests that companies need to understand how AI has impacted current market dynamics in order to innovate, compete and continue to evolve. Early adopters of AI are more likely to dominate the market and grow faster than traditional competitors. This analysis is based on data insights from studies, surveys of global decision makers, and Google Trends research, using tools like NotebookLM to identify these pivotal shifts.

This podcast presents a comprehensive survey of post-training techniques for Large Language Models (LLMs), focusing on methodologies that refine these models beyond their initial pre-training. The key post-training strategies explored include fine-tuning, reinforcement learning (RL), and test-time scaling, which are critical for improving reasoning, accuracy, and alignment with user intentions. It examines various RL techniques such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) in LLMs. The survey also investigates benchmarks and evaluation methods for assessing LLM performance across different domains, discussing challenges such as catastrophic forgetting and reward hacking. The document concludes by outlining future research directions, emphasizing hybrid approaches that combine multiple optimization strategies for enhanced LLM capabilities and efficient deployment. The aim is to guide the optimization of LLMs for real-world applications by consolidating recent research and addressing remaining challenges.

This episode explores Agentic Context Engineering (ACE), a breakthrough framework that transforms LLM contexts into evolving playbooks that accumulate and refine strategies over time,. We discuss how ACE prevents context collapse and brevity bias through a modular workflow of generation, reflection, and curation, ensuring detailed domain knowledge is preserved rather than compressed away,,. Join us to learn how this approach enables smaller models to rival top-tier agents while significantly reducing adaptation latency and operational costs,,.Source: https://arxiv.org/abs/2510.04618

This episode explores DSPy, a declarative framework that enables developers to build modular software by treating Large Language Models as first-class citizens within a proper Python program. We discuss how core primitives like signatures and modules allow for the decomposition of logic into composable systems that remain robust despite model or paradigm shifts. Finally, we dive into the power of DSPy optimizers, which iteratively refine prompts and metrics to improve performance, often rivaling or exceeding traditional fine-tuning methods.

Drawing on the sources, this episode explores the Claude Agent SDK, an opinionated framework built on Claude Code that enables the creation of autonomous agents capable of deciding their own trajectories. We break down the agent loop—gathering context, taking action, and verifying work—while discussing why bash and Unix primitives provide the most flexible foundation for both coding and non-coding tasks. Finally, we examine how sub-agents and sandboxing empower these agents, providing the tools and security necessary to independently search for information and verify their own results.Building an agent with this SDK is like giving a worker a fully-equipped computer rather than just a stack of papers; it provides the digital environment required for them to look up data, create their own tools, and double-check their work in real-time.Source: https://www.youtube.com/watch?v=TqC1qOfiVcQ

This episode explores the cybersecurity landscape of 2026, where autonomous AI agents and a 1,500% surge in deepfakes have significantly amplified organizational risk. We discuss the mounting financial impact of shadow AI and the urgent need for quantum-safe cryptography to protect against impending "Q-Day" threats. Finally, we examine the transition to passkeys as a primary defense against automated, AI-driven phishing attacks.Source: https://www.youtube.com/watch?v=2jU-mLMV8Vw

This episode explores Manifold-Constrained Hyper-Connections (mHC), a framework designed to solve the training instability and memory overhead issues found in existing Hyper-Connection architectures. We discuss how mHC uses the Sinkhorn-Knopp algorithm to project residual connections onto a doubly stochastic manifold, effectively restoring the "identity mapping" property essential for stable signal propagation. Finally, we examine the infrastructure-level optimizations—such as kernel fusion and selective recomputing—that allow mHC to achieve superior scalability and performance in large-scale foundation models.

This episode explores the five critical shifts driven by AI agents that are set to redefine professional roles and business workflows by 2026. We dive into the transition toward intent-based computing, where every employee evolves into a strategic orchestrator managing specialized AI agents to achieve peak productivity. Discover how these trends—from concierge-style customer service to proactive security operations—are fundamentally lifting the ceiling for human achievement.Source: Google report on AI agent trends 2026

In this episode, we explore the shift from basic AI assistants to sophisticated agentic coding, moving beyond the "Dumb Zone" where oversized context windows often lead to degraded reasoning and "nitrogen narcosis" in LLMs. We break down Anthropic’s Claude Code tool and the "Research-Plan-Implement" (RPI) framework, demonstrating how specialized "ant swarms" of agents can manage complex brownfield codebases while avoiding the accumulation of technical "slop". Finally, we discuss why the future of software development lies in disciplined context engineering and stateful domain memory rather than simply relying on larger models.

In this episode, drawing on insights from the sources, METR researcher Joel Becker explores the widening gap between AI’s exponential progress on benchmarks and its actual impact on real-world productivity. We examine a surprising study where expert developers were slowed down by 19% when using AI, challenging the assumption that benchmark success translates directly into immediate economic gains. The discussion investigates the "puzzle" of why low AI reliability and the complexity of high-context environments continue to hinder performance in the field compared to synthetic tests.Source: https://www.youtube.com/watch?v=RhfqQKe22ZA&list=TLGGeQVQrQpc6NgyODEyMjAyNQ

This episode explores a unified framework for adapting agentic AI systems, detailing how foundation models are specialized to plan, reason, and master external tools for complex tasks. We analyze the four adaptation paradigms—A1, A2, T1, and T2—revealing how modular tool optimization can be up to 70 times more data-efficient than traditional monolithic agent retraining. Discover the shift toward symbiotic ecosystems, where stable reasoning cores work in harmony with agile, evolving tools to drive breakthroughs in software development and scientific research.Source: https://www.arxiv.org/pdf/2512.16301

In this episode, we explore Agent-R1, a modular framework designed to transform Large Language Models from static text generators into autonomous agents capable of active environmental interaction. We dive into how extending the Markov Decision Process (MDP) framework enables these agents to master multi-turn dialogues, utilize external tools, and benefit from dense process rewards. Finally, we discuss how end-to-end reinforcement learning is setting new performance benchmarks in complex tasks like multi-hop reasoning by refining how models learn from their own actions.

In this episode, Andrew Ng and Lawrence Moroni discuss why now is the "golden age" for building a career in AI, highlighting how the complexity of tasks AI can perform is doubling as rapidly as every seven months,,. The conversation explores the shifting landscape of "vibe coding" and the evolving ratio between engineers and product managers, emphasizing that the new bottleneck in software development is deciding what to build rather than the coding itself,,. Finally, the experts offer practical advice on navigating a competitive job market by focusing on business value, managing technical debt, and preparing for the rise of "small AI" and self-hosted models,,,.Source: https://www.youtube.com/watch?v=AuZoDsNmG_s

This episode explores the critical July 2025 maturation point, where the initial generative AI hype gives way to the practical challenges of securing ROI and closing the "Data Readiness Gap". We break down the rise of Agentic AI and the strategic architectures essential for moving beyond disjointed pilot programs to translate initiatives into measurable financial value. Finally, we examine how leaders can navigate a divided global regulatory landscape to empower their workforce and unlock true "superagency".