How can AI remember without losing clarity or consistency?
In this episode, we explore Hindsight, a structured memory architecture that enables AI agents to retain facts, track experiences, reflect over time, and maintain a stable persona. We break down how it outperforms traditional retrieval methods and why structured, evidence-aware memory is key to building truly long-term AI agents.

How are AI agents actually being used in the real world?

In this episode of Intelligent Insights, we break down insights from Measuring Agents in Production (MAP) — a large study of how teams are deploying AI agents across industries like finance, healthcare, and science.

Instead of complex autonomy, most teams succeed with simple, controlled systems focused on productivity and reliability. We explore why human oversight still matters, why many teams build their own agents in-house, and what practical patterns are emerging in production today.

If you’re building or thinking about AI agents, this episode focuses on what works — not hype.

Is artificial intelligence the next dot-com crash waiting to happen - or are we entering a true new era of innovation? In this episode of Intelligent Insights, Praveen dives into the economics behind the AI boom: billion-dollar burn rates, sky-high valuations, and the circular flow of money powering tech giants and AI startups alike.

We compare today’s AI gold rush to the dot-com era, uncover what’s driving the frenzy, and ask the big question - are we in a bubble, or just at the beginning of something bigger?

Artificial Intelligence is transforming the workplace—but what does that mean for careers? In this episode of Intelligent Insights, we explore how AI is reshaping the traditional career ladder.

We’ll cover:

• Why entry-level opportunities are rapidly shrinking since 2023.

• Whether AI is pushing companies toward a “flatter” structure.

• The risk of job displacement versus the rise of new roles.

• Lessons from past technology shifts.

• How professionals can adapt and thrive by embracing new tools and training.

If you’re a student, early-career professional, or a leader wondering how AI will impact your team’s growth, this conversation will help you see what the future of work could really look like.

Artificial Intelligence is no longer just about bigger models and clever prompts. The real shift is happening in context engineering—the art and science of shaping how AI systems understand, interpret, and apply knowledge. In this episode, we dive into why context engineering is emerging as the backbone of next-gen AI, how it differs from prompt engineering, and what it means for developers, businesses, and the future of intelligent systems.

If you want to understand where AI is headed next—and how to stay ahead of the curve—this episode is for you.

In this episode, we explore the new rules of engineering leadership in a world shaped by AI and distributed teams. From fostering aligned autonomy to navigating the messy but exciting adoption of AI tools, we break down what it really means to lead high-performing tech teams today.

You'll hear insights on:

• Why diverse backgrounds are an engineering asset

• How to create autonomy without chaos

• What AI means for productivity, onboarding, and workflow

• Why metrics should start conversations, not end them

• How to design user experiences that guide good AI outcomes

Whether you're a tech leader, engineer, or product builder, this episode will help you rethink how teams can thrive in a world where both code and context are constantly evolving.

In this episode of Intelligent Insights, we explore the Model Context Protocol (MCP) - a groundbreaking standard that's redefining how AI models interact with tools, APIs, and external systems. Inspired by the Language Server Protocol (LSP), MCP has rapidly gained traction among major AI platforms like OpenAI, Anthropic, and Cloudflare. But with great power comes great responsibility: the protocol also introduces new vectors for security risks and governance challenges.

We dive deep into the architecture of MCP, its server lifecycle, real-world use cases, and the emerging community ecosystem supporting it. You’ll also hear about the most pressing security threats across creation, operation, and update phases - from name spoofing and sandbox escapes to configuration drift.

Whether you're a developer, researcher, or AI enthusiast, this episode offers valuable insights into the future of agentic workflows and the infrastructure behind intelligent autonomy.

AI agents have exploded in popularity—but most of them are stuck inside walled gardens. In this episode, we dive into the Agent-to-Agent (A2A) Protocol, the new open standard that lets agents discover each other, share tasks, and stream results in real time.

What you’ll hear:

• Why LLMs alone aren’t enough—and how an “agent layer” adds memory, tools, and goals

• The magic of the Agent Card (/.well-known/agent.json) for instant discovery

• How A2A tasks, messages, and artifacts keep multi-agent workflows organized

• Streaming vs. non-streaming modes—and when to choose each

• Where A2A fits alongside the Model Context Protocol and modern Agent Dev Kits

Whether you’re an AI engineer, product leader, or just curious about the next wave of interoperable agents, this conversation will leave you ready to break your bots out of their silos.

Listen now and join the push for truly collaborative AI.