Monetizing AI is uniquely challenging, driven by rapid cost changes and intense customer demand for demonstrable ROI. We discuss essential frameworks, including selecting the right value metric—from resource-based tokens to outcome-based pricing—to ensure alignment between price and perceived value. Learn why continual experimentation is vital and how data-informed tools like Orb Simulations can help AI builders avoid "pricing on vibes".

The era of production AI agents is here. Drawing from the "2026 State of AI Agents Report," we examine how enterprises are shifting from basic task automation to complex, cross-functional systems that already deliver measurable ROI for 80% of organizations,,. Tune in to explore the top high-impact use cases—including coding, data analysis, and internal process automation—and the critical challenges leaders must overcome to scale these intelligent systems strategically in 2026,,.

Join us as Anthropic experts reveal why they stopped building agents and started building specialized "skills" instead, arguing that while general agents are brilliant, they often lack the domain expertise needed for consistent, real-world work. Skills are simply organized folders packaging procedural knowledge and scripts as tools to provide that missing expertise and important context. This scalable design is fostering a quickly growing ecosystem, enabling general agents like Claude to acquire new capabilities and organizational best practices instantly.

Dr. Werner Vogels delivered his final AWS re:Invent keynote in 2025, announcing his departure from the platform for new speakers. He assured developers that those who continue to evolve will not become obsolete despite AI transforming tasks. Vogels introduced a five-part framework for the modern "Renaissance Developer," highlighting curiosity, systems thinking, and polymathic knowledge. He stressed the need for precise communication given generative AI's ambiguity. Guest Claire Liguori supported this by detailing spec-driven development (SDD) in the Kiro IDE to manage natural language prompts. Vogels concluded by emphasizing that developers retain sole responsibility for software quality, requiring stronger verification mechanisms like rigorous code reviews for AI-generated work.

Dive into the world of Advanced Context Engineering, tackling the challenges of AI-generated "slop" and high rework rates often found when using coding agents on complex or brownfield codebases. We explore the methodology of frequent intentional compaction, utilizing the "Research, Plan, Implement" workflow to manage context windows and maximize LLM output quality. Learn how this plan-first approach enables engineering teams to maintain mental alignment and reliably ship complex, production-ready code while staying out of the "dumb zone".

Welcome to the ultimate BMAD Method masterclass, demonstrating the complete IDE workflow for AI-driven development using Claude Code. This episode walks you through transforming a simple to-do application idea, starting with brainstorming with agents like the Analyst, and moving through the creation of necessary artifacts (PRD, Architecture). Learn how to leverage the full suite of agents, including the Analyst and Product Manager, and utilize techniques like advanced elicitation and document sharding to streamline development entirely within your IDE.

Autonomous AI agents execute hyper-personalized phishing and polymorphic malware attacks at unprecedented speed and scale. This acceleration creates a critical "Attack Tempo Gap" that traditional human-centric defenses cannot bridge, posing an existential threat to critical infrastructure. Organizations must deploy AI-driven countermeasures, including Zero Trust and Generative Adversarial Networks (GANs), to match machine-speed offense with machine-speed response.

Large Language Models typically fail long-horizon tasks due to persistent error rates, often derailing after only a few hundred steps. This episode explores MAKER, the first system to successfully solve a task requiring over one million LLM steps with zero errors. Discover how Massively Decomposed Agentic Processes (MDAPs) utilize extreme decomposition into focused microagents and efficient error-correcting voting to provide a reliable, scalable paradigm for future AI systems.

Lance Martin of Langchain will discuss the shift in AI from model training to orchestrating powerful LLMs and computing primitives via a new software discipline. Discover practical context engineering techniques—including managing context rot, reduction, offloading, and isolation—and building effective agent harnesses for managing tool calls in non-deterministic systems. This session emphasizes that simplicity and observability remain vital, requiring builders to continuously rearchitect due to exponentially improving foundation models.

State-sponsored group GTG-1002 executed the first reported cyber espionage campaign largely run by autonomous AI, fundamentally shifting the threat landscape. The actor manipulated Claude Code to autonomously perform 80–90% of tactical operations, including vulnerability discovery and data exfiltration, against high-value targets such as major technology corporations. This unprecedented agentic AI misuse demands immediate security attention and highlights rapidly dropping barriers to large-scale, sophisticated attacks.