Tech Transformed

Think of a robotic prototype that functioned well in March. Its robotic arm sorts through parcels flawlessly on the lab bench. It runs on a power-demanding compute module with a fan attached to the side. Finally, the production specifications are announced: battery power, a sealed case, a budget of 10 watts at most, and no connection to the cloud. Porting the application to hardware that fits those limits is the part of the roadmap nobody put on the slide.
This is where many physical AI projects stall between demo and shipment.
In the recent episode of the Tech Transformed podcast, host John Santaferraro, CEO and Head Research Analyst at Ferraro Consulting, sat down with Krishna Rangasayee, Founder and CEO of SiMa.ai, and Manuel López Roldán, GenAI Product Manager at SiMa.ai.
They break down everything you need to know about physical AI, why it’s taking machine intelligence out of the data centre and into the device itself, and explain the changes that SiMa.ai has made to its software so that AI agents and human engineers can build complex AI applications for that hardware in days and hours, making imagination the limit.
"You could run very high accuracy models entirely on device. You don't need the cloud at all,” the CEO of SiMa.ai states, setting the agenda for this episode.
Rangasayee says that we’re living through what is likely to be the largest upheaval of experiences. “AI is completely overhauling everything we know. We've gone through the social media revolution; now we're going through the cloud AI revolution.”
He believes that everything will be AI-enabled in some form or another, from the infrastructure enabling it in manufacturing, or in robotics, or in automotive, to everything we experience in our home, in our work, in medicine.
Takeaways
Physical AI transfers reasoning from the cloud to the device itself.
Rule-based control is replaced by systems aware of context & memory.
Majority of physical AI customer applications require a sub-10 watt power envelope.
Smaller & more optimised models operate on-chip with high accuracy.
SiMa.ai carried out a complete rebuild of its industry-first agentic Palette Software Suite for use by agents.
Palette lets developers explain and build systems in plain English.
Current builds now take days or even hours, not "six to nine months".
Humans retain the ideas & the decisions while the agents execute.
Current use cases involve inspection, warehouse robots, drones & cars.

Chapters
00:00 Introduction to the podcast and guests
00:28 The move beyond screen-based AI to physical AI
00:58 Defining physical AI and its significance
01:26 Why physical AI will be larger than cloud AI
02:12 The large upheaval AI is causing in various sectors
03:23 The shift from rule-based to reasoning machines
04:22 How models are getting smaller and more efficient
05:54 Enabling new capabilities through AI in physical devices
06:12 Current innovations in drones, robots, and autonomous vehicles
07:35 The role of agentic software and agent engineers
08:59 Democratisation and ease of developing physical AI systems
10:03 Unlocking new business models with physical AI
11:56 The future of AI development and agentic engineering
12:50 The impact of physical AI on industries like manufacturing and healthcare
21:38 How to lead and join the physical AI revolution
23:16 Closing remarks and next steps for listeners
Read the full article: https://em360tech.com/podcasts/what-physical-ai-outgrow-cloud-ai
Watch episode on YouTube: https://youtu.be/GE4sTiBk95A
SiMa.ai: https://sima.ai
EM360Tech: https://em360tech.com
Watch more from the Tech Transformed Podcast: https://www.youtube.com/playlist?list=PLXoSlBGE35NqhHbvDxnrEKw2bVoNBQ8U1
​ ⁨@SiMaTechnologiesInc⁩ ⁨@enterprisemanagement360⁩
#PhysicalAI #AgenticAI #Robotics #TechTransformed #EM360Tech physical AI, SiMa.ai, agentic AI, AI agents, on-device AI, cloud AI, robotics, autonomous vehicles, drones, industrial automation, AI chips, technology, enterprise technology, Tech Transformed, EM360Tech

In the previous episode of the Tech Transformed podcast, EM360Tech's Shubhangi Dua sat down with David Hsu, founder and CEO of Retool, to tackle an uncomfortable truth for CIOs: vibe coding is already happening inside your organisation, whether you've sanctioned it or not.
From customer data turning up in AI-built apps on the public internet, to duplicate tools showing conflicting numbers, to AI bills climbing with no clear view of the return, David made the case that the answer isn't to ban AI-built software but to put guardrails around it. One central platform, with controlled access to trusted data, gives IT visibility, auditability, and control, because "with governance comes freedom." He finished with a bold prediction: by 2030, most of the work in your company, and the CIO's role, will become closer to a chief people officer for AI.
In this Big Data London special edition, host John Santaferraro picks up where that conversation left off with Ryan Wong, Head of Engineering at Retool. As AI moves from generating apps and content to agents that take real action, what does governing them actually take?
Takeaways
The harness is the layer around the model that a business controls.
AI harnesses context, identity, policy, action, and observability.
Governance, not model quality, stops agents from emailing or touching sensitive data.
Employees connecting Claude or ChatGPT to company data is hard to govern, so centralise access like an API gateway.
Enforce data policy at the resource, not app by app, so employees can vibe code freely.
Start agents with tight limits, then widen them. Log every action with the identity behind it.
Vibe coding made building cheap. Deployment, access control, and observability are now the hard part.
Retool says its platform has long handled those layers.
Retool’s Assist tool struggled because it used a proprietary language. Building closer to what LLMs know works better.
Models will change; the harness will last for years.

Chapters
00:00 Introduction to AI harness and its significance
01:08 Defining the components of an AI harness: context, identity, policy, action, observability
05:12 The role of centralization in AI harness architecture
06:44 Governance challenges in multi-agent AI environments
17:48 Implementing a five-step governance process
22:20 Why focusing on the harness is key for AI progress
27:17 The importance of governance as AI agents gain authority
28:24 Final thoughts on controlling autonomous AI agents
Watch the previous episode with David Hsu, CEO of Retool: https://youtu.be/rvdl4Fy2taw?si=3nMzO...
Learn more about Retool: https://retool.com
More from the Tech Transformed podcast: https://youtube.com/playlist?list=PLX...
#AIHarness #AIGovernance #AgenticAI #EnterpriseAI #Retool #TechTransformed #BigDataLDN #RetoolAI #VibeCoding #AIAgents #AIContext #AutonomousAIAgents #AIROI ‪
@Bigdataldn‬ ‪@Retool‬ ‪@enterprisemanagement360‬

Plenty of organisations have an AI agent pilot that worked well in a demo. Far fewer have hundreds or thousands of agents running reliably in the real world, and that gap is where the hard work sits. AI agents are moving beyond experimentation, but taking them from promising pilots into production at scale is proving to be much more difficult. At the same time, AI is reshaping technical teams, changing traditional career paths and forcing organisations to rethink how technology creates value.
In this episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, explores what that transition looks like in practice. She is joined by Italo Vietro, VP of Engineering at Parloa, and Joshua Burks, Global Managed Services Head at Thoughtworks. They cover platform foundations, workforce change and agent management at scale. Their shared message is that powerful models are only part of the answer.
From Task Execution to Orchestration
Burks opens with a clear observation he has seen, and that is AI is shifting technical teams from task execution towards architectural orchestration. For decades, he says, engineering and operations teams have spent around 75 per cent of their time and capacity clearing tickets, sifting through alerts and writing boilerplate syntax. Agentic AI and automated coding tools are making that kind of syntax generation and routine triage a commodity. So the bottleneck has moved downstream. Burks describes a new kind of team built from T-shaped pods of engineers who manage autonomous agents, validate complex system behaviour and focus on continuous modernisation. The aim is to stop merely keeping the lights on.
The Rise of the Product-Minded Architect
Vietro sees the same pattern at Parloa and has a name for the people at the centre of it, which, as he calls them, product-minded architects. Instead of fixating on the solution itself, they spend more time in the problem space.
On the positive side, he thinks this is good news for engineering. Engineers generally want to solve problems, and understanding the problem has become more important than ever. The practical challenge is how to architect many agents together so they deliver the outcomes you expect. For Vietro, this also means ownership of something. People need to feel they can deliver the solutions they want, with the right outcomes, rather than simply carrying out tasks.
What Value Arbitrage Really Means
Stathopoulos asks Burks to unpack a phrase he used early on, and his answer is one of the most useful parts of the conversation.
For roughly 40 years, he says, managed services has been a race to the bottom on a pricing model built entirely on labour arbitrage. Vendors bill for headcount, hours, utilisation and ticket throughput. The flaw is that the vendor profits from the complexity, manual effort and lingering technical debt in a client’s systems.
Technology arbitrage flips that. AI agents reduce the headcount needed to run operations or development. Burks believes the industry is now heading towards value arbitrage, where platform automation, predictable observability and agentic workflows take on the toil.
The question then changes from how many people or agents are on the account to what the organisation gets back. That means less technical debt, smaller ticket and development backlogs, and systems that are continuously modernised and ready for whatever demands come next, whether internal or external. Burks describes them as systems that never age.
Making The Jump From Pilot to Production
Stathopoulos points out the big difference between an agent working in a controlled playground and deploying hundreds or thousands of them in the real world. Vietro, whose company deploys agents at enormous scale, walks through what the transition involves. Let’s take a look at how he breaks it down:
Start with value, not just cost
Many organisations begin with the savings: fewer people, lower costs. Vietro encourages them to look at value instead. What better service will customers receive, and what problem is the agent solving?
Know how many agents you have
His second priority is observability, and he offers a simple test. Can you say how many agents are running in your organisation right now? If you can, you probably have a reasonable setup. If you can’t, there’s room for improvement. He adds that the question is far from trivial to answer.
Stathopoulos says she suspects many companies don’t realise how many agents are running in the background and can’t count them. Vietro agrees that this matters well beyond performance. Observability also supports security, because you need to know what agents are doing, spot when something goes wrong and steer them back on course.
Test until you trust
The third priority is evaluations and simulations. Parloa runs millions of them across its agents to check that they do their tasks with a given level of confidence. Vietro’s benchmark is demanding. You test until you can give customers the same quality of service an agent provides as they would get from a human.
Once that confidence exists, he says, deploying agents across enterprises becomes easier, as does replacing different work streams within an organisation.
The platform foundations agents need
If pilots work in a playground, production needs infrastructure. Burks says the current moment closely echoes the early days of cloud. Back then, accounts were spun up all over the place, there were no guardrails, and everything went everywhere. Today he sees the same thing in what he calls the “agentic brawl”. His view is that enterprises need a centralised control plane. Thoughtworks has one called AgentWorks. He frames the foundations in three parts:
Observability and telemetry. Agents are only as good as the real-time context they receive. Unified observability across logs, metrics and traces lets AI triage agents correlate incidents and carry out root cause analysis, including for other agents across the enterprise.
Governed skills. Rather than letting every team write its own isolated prompts, organisations should capture expert architectural knowledge in reusable, version-controlled skills applied across the business.
Governance, safety and security. A robust control plane manages model execution costs, token budgets and access controls. It also acts as an LLM-as-a-judge within evaluation loops. Burks says you can’t scale agents unless security and compliance teams have full auditability over every agentic action. Without it, budgets spiral, and security loopholes appear, just as they did in the early cloud years.

Lessons Cloud Already Taught Us
Stathopoulos says all of this gives her a feeling of déjà vu, and both guests agree the cloud era holds real lessons. Burks was in the middle of those migrations. His biggest warning is the lift-and-shift trap. Enterprises moved monolithic, brittle applications straight to the cloud without refactoring them, and ended up with the same technical debt at a different cost point.
The AI version of that mistake is easy to make. If you plug generative AI into fragmented legacy architecture, chaotic data, messy ticketing queues and poor observability, you simply produce bad outcomes faster. His advice is to modernise the underlying operational engine as you adopt the technology.
Vietro says the same thing in different words: work on your fundamentals. AI will magnify whatever it’s given. Put it into an unstructured environment, and it amplifies the bad. Build solid foundations, and it amplifies the good, helping teams deliver better software.
He also stretches the comparison. Cloud was big, but he believes today’s AI shift is closer to the arrival of the web in the 2000s, and may be even bigger. His advice for navigating it is to stay sceptical and curious in equal measure, as the industry did back then.
What getting it right looks like
After a lot of talk about what can go wrong, the conversation ends on the positive side. For Vietro, successful scaling starts with model routing, which Parloa calls its model mix. Each incoming task goes to the model with the best track record for it, based on earlier simulations and evaluations. Success is a good conversation, whether by voice or chat, handled reliably and at lower cost.
He gives a vivid example. A German-language agent serving Germany would use standard Hochdeutsch. In Switzerland, the same task would land better if the agent spoke Swiss German, with the right accent and customer warmth. Stathopoulos recognises the same effect from living in Spain, where accents and...

Envision this: A ServiceNow ticket arrives at a big bank, and 50 customers can't log into their accounts. All the dashboards show a red indicator. About 60 to 100 people end up in a war-room meeting and remain there for about two hours. They discover the problem was a slow SQL query in a database ten hops down the stack, a system neither the login team nor their monitoring system had originally identified.
That is the situation which Anish Agarwal, CEO of Traversal, alludes to when explaining why incident response is becoming more difficult and why he believes the solution must be designed differently from the observability tools that most enterprises currently operate.
In the recent episode of the Tech Transformed podcast, Dan Twing, President and COO of Enterprise Management Associates (EMA), sat down with Agarwal, also a professor at Columbia University. They discuss how code laid out by artificial intelligence (AI) is affecting production incidents.
They also talk about what is needed to trace symptoms back to their causes within a portfolio of 5,000 applications, and how businesses decide when to switch from using AI suggestions to adopting autonomous remediation.
In terms of reliability, Agarwal says that the enterprise has to question whether the task was not just initiated but completed. However, it's also possible for a job to finish, yet the answer might not be the one you want.
Key Takeaways
"Did it run?" no longer proves an AI-built system worked.
Coding agents are trained on compile-and-run, so they can sideline the real goal.
Same configuration, three runs, three different results.
Engineers now write specs; agents write code, so code comprehension drops.
Testing improves, but failures still happen at the seams between systems.
Agents need to search far more data than dashboards were built for.
Traversal's production world model maps entities, connections and live telemetry.
Customers are using it to validate CMDBs, which was unplanned.
Causal analysis separates upstream and downstream anomalies from spurious ones.
Multiple paths converging on one cause builds confidence in a root cause.
L4 cut a war room from 60–100 people to five to 10.
Reported MTTR fell from two or three hours to 20–30 minutes.
Coding agents now call Traversal's MCP to write production-aware code.
Decide up front when to build in-house versus buy.

Chapters
00:00 Introduction to Hybrid Cloud Challenges
02:53 The Role of AI in Production Systems
06:34 Complexity of Troubleshooting in AI Systems
12:03 Causal Analysis in Production Environments
18:03 Agentic Systems and Human Collaboration
22:56 Framework for Agentic Readiness in Reliability
27:03 Future of Self-Driving Production
29:55 Practical Steps for Tech Leaders

Visit traversal.com.
agentic SRE, self-driving production, site reliability engineering, AI SRE, AI incident response, root cause analysis, causal AI, MTTR, mean time to resolution, production world model, CMDB, CMDB validation, ServiceNow, observability, AIOps, incident management, Traversal, Anish Agarwal, Dan Twing, Tech Transformed, EM360Tech, Enterprise Management 360, AI-generated code, coding agents, DevOps, enterprise IT

“Vibe coding is already present in your company,” according to David Hsu, CEO of Retool and governance is the only way to secure it.
Let's put this into context. Think of a Fortune 500 company’s CIO who has recently come across a vibe-coded application spotted on the public internet. This app was developed by an employee of the Fortune 500 company in question. The developer obtained a data dump from Salesforce pertaining to customers, then fed this data to an external AI tool not part of the company’s set of AI tools. The employee prompted the tool to host the resulting application on a public URL.
Since it was an external AI tool, a security review couldn’t be carried out, nor could the IT department view it. Without any internal visibility into a tool created by the employee, the actual customer data was live on the open web. It’s a scary situation, a breach of trust, so imagine the CIO’s wrath upon seeing it.
This is why, in the recent episode of the Tech Transformed podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down for a conversation with David Hsu, CEO and Founder of Retool. They talk about the repercussions of vibe coding in addition to examining why most enterprises have no conception of how much of it is already taking place within their own enterprise.
The discussion then turns from the immediate issue of governance concerning uncontrolled AI app-building to Hsu's prediction that software engineering as a profession could vanish within two to three years.
“All the CIOs that I speak to are genuinely frightened about vibe coding, and even if you don't want to admit it, vibe coding is already present in your company,” he tells Dua. This is partly why Hsu founded Retool.
Retool secures vibe coding for enterprises. Hsu explains that Retool can be connected to a company’s Snowflake database, for instance or their Databricks data lake. “Users should be able to have access to particular tables only,” he says. Access controls are required at the group level.
“If you're in this particular Okta group, you can only access this table, and it's read-only. Whereas if you're in this other Okta group, you can actually write back to this other Salesforce over here." So one Okta group could be granted read-only access to a particular table, while another group might be given write access to a connected Salesforce instance. All applications that are built on top of that connection automatically inherit the same restrictions.
Once rules of such a nature are put in place, people could then be set free, explaining why he views governance as something that enables freedom rather than something that restricts.
Takeaways
Vibe coding is already happening inside most enterprises, sanctioned or not
A Fortune 500 CIO found a vibe-coded app exposed on the public internet
One customer's AI coding spend hit 30x their original budget estimate
Two employees independently built duplicate apps that returned different numbers
Hsu forecasts fully automated coding arriving by late 2027 or early 2028
By 2030, Hsu estimates 90% of company work will be done by AI employees
The CIO's future role: a "chief people officer" governing AI agents, not humans

Chapters
00:00 The Rise of No-Code and Low-Code Development
04:52 Governance in the Age of Rapid Development
12:40 Centralisation and Control in Software Development
20:04 The Future of Software Engineering and AI
25:13 The Role of Humans in an AI-Driven World
vibe coding, AI governance, enterprise AI, CIO, AI agents, software engineering, Retool, David Hsu, enterprise software, AI security