Agents of Intelligence

Engineering That Works: Inside GitHub’s System Success Playbook

In this episode of Code at Scale, we unpack the GitHub Engineering System Success Playbook (ESSP)—a practical, metrics-driven framework for building high-performing engineering organizations. GitHub’s ESSP reframes engineering success around the dynamic interplay of quality, velocity, and developer happiness, emphasizing that sustainable improvement comes not from isolated metrics but from system-level thinking.
We explore GitHub’s three-step improvement process—identify, evaluate, implement—and dig into the 12 core metrics across four zones (including Copilot satisfaction and AI leverage). We also highlight why leading vs. lagging indicators matter, how to avoid toxic gamification, and how to turn common engineering antipatterns into learning opportunities. Whether you're scaling a dev team or transforming engineering culture, this episode gives you the blueprint to do it with intention, impact, and empathy.

The AI Marketer: How Generative Models Are Rewriting Enterprise Strategy

In this episode, we unpack how generative AI is transforming the foundations of enterprise marketing. Drawing from the white paper Generative AI in Marketing: A New Era for Enterprise Marketing Strategies, we explore the rise of large language models (LLMs), diffusion models, and multimodal tools that are now driving content creation, hyper-personalization, lead scoring, dynamic pricing, and more.
From Coca-Cola’s AI-generated campaigns to JPMorgan Chase’s automated ad copy, the episode showcases real-world use cases while examining the deeper shifts in how marketing teams operate. We also confront the critical risks—data privacy, brand integrity, model bias, hallucinations—and offer strategic advice for leaders aiming to implement generative AI responsibly and at scale. If your brand is serious about leveraging AI to boost creativity, performance, and customer engagement, this is the conversation you need to hear.

Agents at Work: Unlocking Autonomy with the Model Context Protocol

In this episode, we explore the next frontier of enterprise AI: intelligent agents empowered by the Model Context Protocol (MCP). Based on a strategic briefing from Boston Consulting Group, we trace the evolution of AI agents from simple chatbots to autonomous systems capable of planning, tool use, memory, and complex collaboration.
We dive deep into MCP, the open-source standard that's fast becoming the connective tissue of enterprise AI—enabling agents to securely access tools, query databases, and coordinate actions across environments. From real-world examples in coding and compliance to emerging security challenges and orchestration strategies, this episode lays out how companies can build secure, scalable agent systems. Whether you're deploying your first AI agent or managing an ecosystem of them, this episode maps the architecture, risks, and best practices you need to know.

RAG Meets Reasoning: Architectures for Intelligent Retrieval and AI Agents

In this episode, we decode three of the most compelling architectures in the modern AI stack: Retrieval-Augmented Generation (RAG), AI Agent-Based Systems, and the cutting-edge Agentic RAG. Based on the in-depth technical briefing Retrieval, Agents, and Agentic RAG, we break down how each system works, what problems they solve, and where they shine—or struggle.
We explore how RAG grounds LLM responses with real-world data, how AI agents bring autonomy, memory, and planning into play, and how Agentic RAG fuses the two to tackle highly complex, multi-step tasks. From simple document Q&A to dynamic, multi-agent marketing strategies, this episode maps out the design tradeoffs, implementation challenges, and best practices for deploying each of these architectures. Whether you're building smart assistants, knowledge workers, or campaign bots, this is your blueprint for intelligent, scalable AI systems.

Code, Meet Copilot: How LLMs Are Reshaping Full-Stack Development

In this episode, we explore how Large Language Models (LLMs) like GPT-4 and GitHub Copilot are revolutionizing full-stack web development—from speeding up boilerplate generation and test writing to simplifying infrastructure-as-code and DevOps workflows. Based on the white paper Enhancing Full-Stack Web Development with LLMs, we break down the tools, use cases, architectural patterns, and best practices that define modern AI-assisted development.
We cover real-world applications, including LLM-driven documentation, code refactoring, test generation, and cloud config writing. We also dive into the risks—like hallucinated code, security gaps, and over-reliance—and how to mitigate them with a human-in-the-loop approach. Whether you're a solo developer or leading a team, this episode offers a comprehensive look at the evolving toolkit for building smarter and faster with AI.
 

From Model to Market: The MLOps Playbook for Scalable AI

In this episode, we dive into the nuts and bolts of MLOps—the crucial discipline that bridges the gap between machine learning development and real-world deployment. Drawing insights from Introducing MLOps by Mark Treveil and the Dataiku team, we explore what it really takes to operationalize machine learning in enterprise environments.
From building reproducible models and setting up robust CI/CD pipelines to managing data drift and enforcing responsible AI practices, we walk through the entire lifecycle of a model in production. You'll learn about the diverse roles that make MLOps successful, how to align governance with risk, and why monitoring and feedback loops are essential to long-term model health. With practical case studies in credit risk and marketing, this episode delivers a comprehensive roadmap for deploying ML systems that scale—safely, ethically, and efficiently.

The State of AI 2025: Power, Progress, and the Price of Intelligence

In this special episode, we unpack the major insights from the Artificial Intelligence Index Report 2025, the definitive annual report tracking AI’s global trajectory. From breakthrough advances in training efficiency and multilingual model capabilities to serious concerns about carbon emissions, bias, and ethical risks in medicine, this report gives us a sweeping view of where AI is—and where it’s heading.
We’ll dive into how AI is reshaping science, education, and the economy, discuss the exponential rise in AI patents, and explore geopolitical trends in research, talent migration, and public policy. Whether it’s massive compute powering GPT-4o, the booming generative AI investment scene, or the growing calls for responsible AI governance, this episode brings you the numbers, narratives, and nuance behind today’s AI evolution.
Expect data-backed insights, expert commentary, and a big-picture look at what it means to live in the AI age.

Prompt Perfect: Crafting Conversations with Large Language Models

In this episode, we unravel the art and science of prompt engineering—the subtle, powerful craft behind guiding large language models (LLMs) to produce meaningful, accurate, and contextually aware outputs. Drawing from the detailed guide by Lee Boonstra and her team at Google, we explore the foundational concepts of prompting, from zero-shot and few-shot techniques to advanced strategies like Chain of Thought (CoT), ReAct, and Tree of Thoughts.
We also dive into real-world applications like code generation, debugging, and translation, and explore how multimodal inputs and model configurations (temperature, top-K, top-P) affect output quality. Wrapping up with a deep dive into best practices—such as prompt documentation, structured output formats like JSON, and collaborative experimentation—you’ll leave this episode equipped to write prompts that actually work. Whether you’re an LLM pro or just starting out, this one’s packed with tips, examples, and aha moments.

Brains and Bridges: Decoding Agent2Agent and Model Context Protocols

In this episode of Agents of Intelligence, we dive deep into two groundbreaking protocols shaping the future of multi-agent Large Language Model (LLM) orchestration: the Agent2Agent (A2A) Protocol and the Model Context Protocol (MCP). A2A acts as the social glue between autonomous AI agents, allowing them to communicate, delegate tasks, and negotiate how best to serve the user—almost like microservices that can think. On the other side, MCP is the information highway, standardizing how these agents access and interact with external data and tools—making sure they’re never working in isolation.
We’ll unpack the core design philosophies, key features, real-world use cases, and the powerful synergy between A2A and MCP when combined. Whether it’s onboarding a new employee or compiling a complex research report, these protocols are making it possible for intelligent agents to collaborate and operate with unprecedented depth and flexibility.
Tune in to learn how the future of AI is being built—not just with smarter models, but with smarter ways for those models to talk, think, and act together.

Beyond Benchmarks: How Long Can AI Work?

In this episode, we unpack a groundbreaking new way of measuring AI capability—not by test scores, but by time. Drawing from the recent METR paper "Measuring AI Ability to Complete Long Tasks," we explore the concept of the 50% task-completion time horizon—a novel metric that asks: How long could a human work on a task before today's AI can match them with 50% success?
We’ll explore how this time-based approach offers a more intuitive and unified scale for tracking AI progress across domains like software engineering and machine learning research. The findings are eye-opening: the time horizon has been doubling roughly every seven months, suggesting we could see "one-month AI"—systems capable of reliably completing tasks that take humans 160+ hours—by 2029.
We also delve into how reliability gaps, planning failures, and context sensitivity reveal AI’s current limits, even as capabilities continue to grow exponentially. Plus, what does this mean for the future of work, safety risks, and our understanding of AGI? If you're tired of benchmark buzzwords and want to get real about how far AI has come—and how far it might go—this one's for you.