In this episode, we step back and reflect on what really happened in the agentic AI space in 2025.

The year was defined by rapid model releases, evolving frameworks, and a growing ecosystem of tools promising intelligent, autonomous systems. But beneath the momentum, organisations encountered a deeper challenge: building agentic systems on foundations that were still shifting.

This episode explores how continuous model upgrades affected agent behaviour, why tooling emerged to stabilise execution rather than boost intelligence, and how coordination, interoperability, and structure became unavoidable concerns. From agent-to-agent communication and Google Antigravity to Model Context Protocol and the formation of the Agentic AI Foundation under the Linux Foundation, 2025 marked a shift toward standardisation and consolidation.

Most importantly, the episode reinforces a central theme of this podcast: agentic AI systems are tools, not the work itself. Building production-grade agentic AI requires engineering discipline, behavioural testing, lifecycle thinking, and the ability to design for constant change.

As we move into 2026, the key question is no longer which agent framework to adopt, but where organisations should focus when everything is still evolving. That is the question we take up in the next episode.

In this episode, we explore a quiet but profound challenge emerging across the artificial intelligence landscape: model upgrades that no longer behave like software updates. Newer versions of large language models often shift reasoning patterns, change output formats, and break carefully-designed workflows, leaving organisations struggling to maintain consistency, trust, and reproducibility.

Drawing from real-world evidence and industry research, this narrative uncovers why behaviour changes between versions are inevitable, why backward compatibility is nearly impossible, and why engineering discipline — not just better models — will determine who succeeds in the era of agentic systems.

A deep dive into a problem every AI team will face, and one that will shape the future of intelligent systems.

This episode explores what it truly takes to build a production-ready agentic AI system — the architecture beneath it, the behavioural and contextual logic that gives it depth, and the engineering discipline required to move beyond clever demos into systems that evolve with real users. It reflects on where AI coding copilots fall short, why domain understanding matters, and how practical constraints shape every design decision.

Humanity has been trying to understand itself long before machines existed. Astrology, the Chinese and Vedic systems, numerology, cultural archetypes, and behavioural science have all attempted, in different ways, to decode why people think, feel, and act the way they do. If you strip away the labels, they are simply frameworks for interpreting human tendencies. Reimagining these systems — not as prediction tools, but as structured behavioural lenses for the modern world — became one of the foundational ideas explored in this episode.

The discussion includes my journey of building the AI Life Coach system from scratch as a practical example. Do check it out, support it, and share it — the app is available on both Android and iOS at https://ailifecoach.in/.

In this episode of Agentic AI — The Future of Intelligent Systems, we explore the architectural foundations that determine whether Agentic Artificial Intelligence becomes powerful… or unmanageable.

Modern architecture is being rewritten as agents plan, act, reason, trigger tools, and evolve faster than traditional systems can keep up. This episode breaks down the ten core principles every organisation must master—modularity, simplicity, scalability, reusability, maintainability, security, sustainability, economical design, ethical considerations, and responsible architecture.

These principles are no longer optional. They define how autonomous systems behave, how they scale, how they stay safe, and how they remain sustainable.
Because the future of Agentic AI will not be shaped by how fast agents think, but by how well we design the foundations beneath them.

Tune in for a deep, practical, and forward-looking conversation on building systems that endure in an era where artificial intelligence doesn’t just generate output… it acts.

This episode breaks down a quiet shift happening beneath the excitement surrounding artificial intelligence. The technology continues to advance rapidly, but the systems, processes, and expectations around it are not keeping pace. The result is a growing gap between what artificial intelligence can demonstrate in isolation and what organizations can reliably run at scale.

We explore why inflated expectations are beginning to correct, why prototypes fail when they meet real-world environments, why costs and constraints are forcing more grounded choices, and why engineering discipline—not model size—will determine who succeeds. This isn’t collapse. It’s a structural reset, pushing the narrative back toward fundamentals: efficiency, integration, reliability, trust, and sustainable design.

Tune in to understand why the narrative bubble has burst and why the real journey of artificial intelligence begins now.

This week’s episode dives into one of the most critical questions shaping our digital future — what happens when AI stops assisting and starts acting?

As AI agents become capable of executing real actions — from shopping to scheduling, from browsing to managing — we find ourselves at a crossroads between freedom and control. Who truly holds the power when an AI acts on your behalf: you, the assistant, or the platform that hosts it?

The recent debate between Perplexity and Amazon captured this growing tension. Perplexity believes users should be able to let their AI assistants act freely using their data and accounts. Amazon, on the other hand, worries that such autonomy challenges years of trust, governance, and security carefully built within its ecosystem.

Neither side is wrong — they’re simply defining different parts of the same evolution. This isn’t just about two companies; it’s about the world’s shift toward agentic systems that blur the line between automation and authority.

In this episode, I explore why innovation must respect what platforms have built — their infrastructure, governance, and trust models — while still challenging the limits of what’s possible. History has shown us how conflict can become collaboration: when web automation raised similar fears years ago, it led to OAuth — a standard that balanced safety and innovation. AI now needs its own version of that balance.

Because real progress won’t come from breaking rules; it will come from redefining them responsibly.
Innovation should challenge limits, not disregard them. And the future of AI will depend on platforms and pioneers evolving together — with trust at the core.

Tune in to When AI Acts for You: The Thin Line Between Freedom and Control — and join me as we unpack how to keep innovation moving forward without losing the balance that makes it meaningful.

This week’s episode steps away from the usual exploration of agentic workflows to address a growing misconception — that AI is the reason behind layoffs. The truth runs deeper.

AI influences work, but it doesn’t define it. The real inefficiency begins at the top — in strategies built on hype, budgets poured into billion-dollar models, and a fractured SDLC that treats AI as a patch instead of an integrated foundation.

In this thought-provoking narrative, Navveen dissects why the current AI wave is failing to deliver sustainable value. From copilots that can code but not comprehend, to chatbots that respond but don’t reason — this episode explores how the absence of seamless integration, traceability, and engineering discipline has created an illusion of progress.

You’ll hear why AGI is a distraction, why today’s AI breaks when faced with the new, and why engineering — not automation — will define the next chapter of intelligent systems.

Because the future isn’t about replacing people with AI.
It’s about building systems where intelligence, accountability, and adaptability coexist — engineered to work, not just to impress.

In this special episode of Agentic AI: The Future of Intelligent Systems, we explore Stellas, a personal research project that merges ancient astrological wisdom with modern artificial intelligence. Discover how specialized AI agents interpret cosmic patterns to offer instant, personalized life insights — not as prediction, but as perspective.

Whether you believe in astrology or not, this episode invites you to reflect on how humanity’s oldest pattern science meets the newest form of intelligence.
Visit https://stellas.me/promo to experience the AI-augmented cosmos for yourself and download the Android app at - https://play.google.com/store/apps/details?id=me.stellas.app

In this episode, we step back from the hype and take an honest look at why LLMs and coding copilots haven’t lived up to the claims of productivity revolutions and autonomous engineering.

From hallucinated logic and brittle architecture to context loss and duplicated code, we unpack real-world failures surfaced in AI-generated production code — and explain why today’s tools still lack the judgment, systems thinking, and trade-off awareness that real software engineering demands.

We also explore the marketing narratives shaping expectations, and why benchmarks and demos obscure the true cost of using these tools at scale.

This episode is not a rejection of AI — it’s a reframing. Because while these tools can accelerate parts of development, they are not engineers. They are assistants. And treating them as such is where their real value begins.

Following our last episode on the Agent Economy, this conversation goes deeper into what truly drives success in AI — not tools, but engineering.

Agentic AI isn’t a plug-and-play revolution. It’s built through architecture, measurement, resilience, ethics, and sustainability. Tools can spark experimentation, but engineering makes innovation last.

Organizations don’t need to replace people; they need to augment engineering talent — strengthening the foundations of reliability, responsibility, and trust.

Because the next AI revolution won’t be bought through tools.It will be engineered through people.