The Loop Asia

An AI agent can have the right credentials, the right permissions and authorisation for every step, and still do something nobody wanted. As agents take on more autonomy, the security question shifts from "who has access?" to "is this what the person actually meant?" 

Aditi Sawhney, Senior Vice President of Security Solutions at Mastercard in Asia Pacific, explains what that means for payments and the rest of the enterprise. 

What we cover: 

• Agent Pay and Mastercard's trust framework for agentic payments
• Telling a legitimate agent from a rogue one, and why velocity checks fail 
• Whether today's identity and authorisation standards are enough for agents
• Verifiable intent and interoperability across the industry • Who carries the liability when an authorised agent gets it wrong 
• A CISO's three priorities in Asia Pacific for the next 12 months 

Figures mentioned: 

Mastercard, A Short History of the Future of Shopping and Payments (2026, forecasts 300 million shoppers routinely using AI agents by 2030);

McKinsey, The agentic commerce opportunity (2025, US B2C retail);

Verizon Data Breach Investigations Report 2025 and 2026. 

Connect with Aditi Sawhney: https://www.linkedin.com/in/aditi-sawhney/ 

The Loop Asia: www.theloop.asia

Aki Ranin has spent years helping organizations think clearly about AI strategy, and right now he is focused on a problem most companies are only beginning to notice: the deeper you embed AI agents into your business, the more exposed you become to providers you do not control. In this episode, recorded soon after his keynote at apidays Singapore 2026, Aki walks through what sovereign AI actually means in practice — not as a regulatory concept, but as a question of operational risk. If your agents can be switched off without warning, if you cannot quantify hallucination rates, if you do not own the code, you do not own your business.

What you will learn:

• Why the AI supply chain carries the same fragility risks as physical supply chains — from TSMC and ASML to the concentration of frontier models in Silicon Valley
• How a startup of 40 people running on Claude APIs was accidentally banned for 24 hours, and what that tells you about building on closed platforms
• The Shopify case study: how one team reduced their AI costs from $5 million per year to $70,000 by moving down the model stack without sacrificing accuracy
• Why "AI inflation" may matter as much as financial inflation — and how token costs are rising even as providers keep calling new models "Flash"
• The difference between drag-and-drop agent platforms and code-owned agents, and why that distinction will determine who keeps the IP
• How to build a sovereign AI stack as a gradient: from paid cloud APIs, to private cloud instances, to open models, to on-premise inference hardware
• Why AI governance belongs at the board level, not delegated to a single AI lead
• How Singapore's Foreign Minister Vivian Balakrishnan is running a local AI second-brain on a Raspberry Pi — and what that signals about accessibility.

The sovereign AI stack Aki describes:

• Free tools — acceptable for non-sensitive, public-domain research only
• Paid subscriptions — minimum bar for any work touching company data
• Private cloud instances (AWS, Azure, GCP) — frontier models with access controls and no training data exposure
• Open models via API — more portability, less vendor lock-in
• Open models on your own hardware — maximum auditability, task-specific agents, sensitive workloads

About Aki Ranin: Aki Ranin is a two-time founder, AI advisor, and published author who has focused exclusively on machine learning and AI since 2016. He co-created the "Usable AI" transformation framework, advises startups, corporates, and VC funds on AI strategy, and serves as Affiliate Faculty in AI and ML at Singapore Management University. 

Follow Aki Ranin on Substack, especially his article "Not your AI, not your business" — https://recursivelabs.substack.com/p/not-your-ai-not-your-business

Most enterprise AI programmes produce proof-of-concepts that never reach production. Sutowo Wong, Managing Director of AI x Data at Temus, has a framework for diagnosing why — and fixing it. He calls it the AI Triangle: three conditions that must be satisfied simultaneously. Miss any one and the transformation stalls.

Recorded after apidays Singapore 2026, this conversation covers the structural reasons AI initiatives fail to scale, why AI transformation has to be CEO-led, how to close the tempo gap between model releases and enterprise deployment, and why governance designed into architecture from the start is an accelerator — not a speed bump.

What we cover:
• The AI Triangle — transformative, sovereign and fast, explained
• The POC trap and why it is a structural problem, not just a technology problem
• Data sovereignty and contextual sovereignty in practice
• Prototype in 6 hours, live deployment in 6–12 weeks
• Why the CEO, not the Chief AI Officer, must drive AI transformation
• Governance as accelerator — and why it must be baked in from the start

Connect with Sutowo on LinkedIn: linkedin.com/in/sutowo/
Temus: temus.com
The Loop Asia: www.theloop.asia

Manjunath Bhat leads software engineering research at Gartner and has been one of the most thoughtful voices tracking what AI is actually doing to the way we build software — not just the productivity story, but the structural changes to teams, platforms, and careers. He's a returning keynote speaker at apidays Singapore and apidays Australia.

In this conversation we cover:

• The shift from AI-augmented human work to agent-driven development — and why Manju deliberately avoids calling these tools "peer programmers"
• Why the prototype-to-production gap hasn't closed, and where the real friction still lives
• The hidden downside of vibe coding: when you fully offload to AI, you stop learning too
• How platform engineering is evolving from template-based governance to embedding intelligence directly into the development workflow
• The three mismatches platform teams now have to resolve: speed, determinism, and accountability
• What junior engineers should actually focus on as AI absorbs more entry-level work
• Why Gartner's position is that creativity — not productivity — will be the new measure of engineering excellence
  
  

Manju will be delivering a keynote at apidays Singapore, 14–15 April 2026.
Connect with Manju: linkedin.com/in/manjunathbhat
apidays Singapore 2026: apidays.global/singapore

TheLoopAsia: www.theloop.asia