Did you ever walk into a conference session thinking you were ready for the week, only to realise the announcements were coming so fast that you almost needed an agent of your own to keep up? That was the mood across Las Vegas, and it was the backdrop for my conversation with Madhu Parthasarathy, the general manager for Agent Core at AWS.

He has spent the week at the centre of AWS's wave of agentic AI news, working on the ideas that are already moving from keynotes and demos into the hands of real enterprise teams. Sitting down with him offered a rare moment of clarity among the noise, and his calm take on what actually matters helped bring the bigger picture into focus.

Madhu talked through the thinking behind Agent Core and why he believes 2026 will be the year enterprises finally begin shifting from prototypes to production scale agents. He walked me through the two areas customers keep coming back to, trust and performance, and why the new policy framework and agent evaluations could remove long standing barriers to deployment.

His examples were grounded in real behaviour he is seeing inside large companies, whether that is internal support workloads, developer productivity, meeting preparation, or customer facing flows designed to reduce the friction between intent and outcome.

We also explored the deeper shift introduced by Nova Forge, including the idea of blending enterprise data with model checkpoints to create domain specific agents that can work with greater accuracy and context. Madhu explained why there will never be a one size fits all model and how choice remains central to AWS's approach to agentic AI.

My guest also reflected on how infrastructure changes, such as Trainium three ultra servers and expanded Nova model families, are shaping the pace at which companies can experiment, evaluate, and adopt emerging capabilities.

Trust surfaced again and again in our conversation. Madhu was clear that non-deterministic systems also introduce concerns, which is why action boundaries and guardrails are becoming as important as model quality. He described the excitement he is seeing from customers who now feel they have workable ways to give agents responsibility without handing over the keys entirely.

As he put it, this is the moment where confidence begins to grow because the guardrails finally meet the expectations of enterprise leaders.

We closed with the topic many people have been whispering about all week, modernization. Madhu reflected on AWS Transform, the push to help organisations move away from legacy architectures far faster than before, and the impact that agentic systems will have as they support full stack migrations across Windows environments and custom languages.

Madhu cuts through the noise with a grounded view of reliable autonomy, multi agent orchestration, policy driven safety, and the shift toward agents as true collaborators.

The question now is where you see the biggest opportunity. How might these agent-based systems change your workflows, and what would it take for you to trust them with the tasks you never seem to have time for? I would love to hear your thoughts.

Have you ever wondered how an idea that begins with two friends in a pub ends up shaping conversations about health all over the world? That was on my mind as I met Graham Link & Timothy Gnaneswaran from Movember on the show floor at AWS re:Invent.

Their story has grown far beyond the mustache that everyone recognises. What started with a simple gesture of support has become a movement that now reaches millions, raises vast sums through a global fundraising platform, and backs projects focused on prostate cancer, testicular cancer, mental health, and suicide prevention. Hearing them describe how that original spark grew into something this wide and long lasting gave the conversation a real sense of depth.

Recording in the middle of re:Invent added its own flavour. AI news filled the halls, yet Timothy and Graham were there speaking with engineers and builders about something deeply human. Their booth stopped people in their tracks, offered barbershop shaves, and created space for personal stories.

They talked openly about how Movember built its own platform to handle sixty to eighty million dollars in four weeks, how it must stay resilient every minute, and how AWS has supported them for more than a decade. They also shared how technology shapes the work behind the scenes, whether it is clinical quality registries, digital conversations tools, or new research paths that explore how AI might support healthier behaviours.

What stayed with me most was the honesty about the tensions they face. Men are still reluctant to talk about their health. Loneliness is rising. Social platforms create new openings and new barriers at the same time. They see how AI can help someone begin a difficult conversation, yet they are clear about the risks when people rely on tools that were never designed for mental health support.

They also talked about the patterns they see across different regions, the sobering statistics in the major markets where they operate, and how younger audiences now gather in gaming communities rather than traditional spaces.

Movember knows it needs technology to reach scale, but it never wants to lose the human connection at the heart of its mission. What part of their story stands out most for you, and where do you think technology can genuinely help shape the next chapter of men's health?

Did you know a single Formula 1 car produces 1.1 million data points every second from hundreds of sensors? That number alone sets the tone for this conversation with Ruth Buscombe, an F1 strategist, analyst, and F1TV presenter whose work sits at the meeting point of engineering precision and real time storytelling.

We met at AWS re:Invent in Las Vegas, and her insights into how much pressure, judgment, and creativity are wrapped inside each decision brought the sport to life in a fresh way for anyone who has ever stared at a dashboard of metrics and wondered what really matters.

This discussion goes far deeper than split times and tyre choices. Ruth explains how AWS and F1 are rethinking race strategy through real time insights and cloud compute, from TrackPulse and root-cause analysis all the way to predictive graphics that let commentary teams spot a race-defining moment before it happens.

She also reflects on the sport's changing culture, the growth of new fan communities, and the shift from old telemetry to modern systems that process millions of data points every second. Her stories from the paddock at Ferrari, Alfa Romeo, and F1TV help frame just how intense the job can be when 12,000ths of a second separate pole from second place.

There are moments in this conversation that remind us that F1 strategy is as much about human pattern recognition as it is about machine intelligence, and that the strongest engineers find ways to absorb pressure without losing their instinct.

What stood out most was how clearly Ruth links F1 to decision making in every industry. Whether she is talking about marginal gains, pattern detection, or the discipline needed to separate noise from signal, her examples make perfect sense to both race fans and tech leaders.

She shares how AWS tools allow broadcasters and engineers to interpret scenarios instantly, why the sport needed to move past manual diagnosis, and how new tools even help verify whether a driver's mistake came from a small steering slide or a split-second shift error.

Her passion is infectious and her explanations cut straight to the heart of what makes the blend of live racing and cloud computing work so well. As you listen, think about how your own team makes choices under pressure and ask yourself one last question. If you were in the garage making a call with the whole world watching, which signals would you trust and how fast could you act?

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