Tech Unboxed

That moment when an AI demo looks flawless after two days, then the business asks the obvious question: why is it still not in production three months later? We dig into the real world gap between AI hype and enterprise delivery, especially inside highly regulated organisations like banks where governance, security, and architecture are not optional. 

We walk through a clear decision framework for enterprise AI strategy: buy, build, or boost. Buying gets you speed through off the shelf tools, building gives you custom differentiation, and boosting helps you modernise by extending what you already own through smart integration and an added layer of capability. We share why “boost” is showing up so often right now, how AI changes the economics and complexity of existing software estates, and why ROI is still a tough question when model costs and BAU support are moving targets. 

We also tackle the deeper strategic issue: if everyone has access to the same AI models, where does competitive advantage come from? Our take is that the edge lives in your data, your direction, and how you embed AI into your processes, not in the model alone. We close with practical guidance on avoiding failed proofs of concept, managing expectations, and keeping commitments small in a market that changes weekly. If this helps, subscribe, share it with a colleague wrestling with AI delivery, and leave a review with the biggest blocker you are seeing right now.

AI is getting more capable, but it is also getting more expensive and that changes everything. We sit down with Jason Perry, a cloud and platform engineer at BBD Software, to talk about agentic AI that actually survives contact with real enterprise workflows: budgets, governance, security, compliance, and the uncomfortable question of responsibility when autonomous agents act on their own. 

We dig into why “strategic deployment” beats chasing the largest model, and how rising token costs are pushing organisations to prove ROI in concrete terms. Jason breaks down what makes AI platform engineering different from simply using off-the-shelf productivity tools, then walks through the biggest blockers teams hit today. We cover cost control guardrails, observability, the EU AI Act and other evolving regulations, and why agent identity and access management is often the most technically complex piece of scaling agentic AI safely. 

We also explore practical ways to reduce waste while improving quality: minimising tokens per correct answer, narrowing context, limiting tools, and using techniques like semantic routing and model response caching through an AI gateway. Finally, we look ahead to the shift toward small language models and how that can reshape cloud versus on-prem decisions around data sovereignty and governance. 

If you’re building an AI strategy, an AI platform, or autonomous agents you can trust, subscribe, share this with a teammate, and leave a review. What is the one workflow you would automate with agentic AI first?

If you’ve felt the pressure to “go agentic” or bolt RAG onto everything, this conversation is a deep breath and a better plan. We dig into the real decision points behind modern AI: when a clean prompt solves the problem, when retrieval is worth the effort, and when an agentic system adds cost without adding value. Along the way, we call out how vibe coding accelerates learning but can sabotage maintainability when teams don’t understand the code they ship.

We get practical about data. More isn’t better—better is better. You’ll hear how RAG actually raises the bar for data hygiene, why outdated or messy documents produce confident wrong answers, and how to build retrieval steps that respect source structure and change cadence. From noisy transcripts to multilingual contexts, we map the preprocessing and governance moves that prevent hallucinations and keep answers grounded.

Then we unpack agentic AI as a network of specialists: models and tools with clear roles, routed by a coordinator that chooses the right path, including non-LLM components for math or structured queries. It’s powerful, but not a default. We weigh costs, reliability, and the risk of overengineering when classic ML, search, or a database would do. The through-line is human judgment: engineers stay in the driver’s seat, setting constraints, validating reasoning, and designing systems that can be supported over time.

If you care about building AI that lasts—clean prompts over cargo-cult pipelines, data quality over dashboards, agents where they fit—this one’s for you. Subscribe, share with a teammate who needs a sanity check, and leave a review with your biggest AI misconception so we can tackle it next.

Lucky Nkosi, head of of BBD's ATC team shares insights on AI's evolution from closed research to public accessibility, exploring how we've moved past the peak of the AI hype cycle into the trough of disillusionment.

• OpenAI's release of ChatGPT marked a pivotal moment in AI adoption, reaching millions of users while validating their models
• Microsoft's billion-dollar investment in OpenAI was strategic, with the money returning to Microsoft through compute costs on Azure
• We're now past the peak of the AI hype cycle, with studies showing 93% of enterprise AI projects failing completely
• Heading into the "trough of disillusionment" means many AI investments will prove fruitless, requiring more responsible approaches
• The "time filter" framework helps identify valuable AI applications by asking: Do I need to do this task? Can I automate it? Can I delegate it?
• AI technologies are non-deterministic by nature, making them challenging to integrate with deterministic systems like financial services
• Hardware remains the ultimate frontier for AI impact, as "what truly matters is what you can impact in the physical world"
• BBD helps companies navigate AI by filtering through the noise and conducting rapid R&D to identify practical solutions

Platform engineering is transforming how organizations manage infrastructure by creating standardized "golden paths" that enable developers to self-service their infrastructure needs through a single API.

• Platform engineering emerged as organizations outgrew traditional DevOps approaches
• Kubernetes serves as a universal and extensible cloud-native API, not just a container orchestrator
• Crossplane acts as middleware between developers and cloud providers, enforcing security and governance
• Using the restaurant analogy: developers order from a menu (API) while platform engineers handle the kitchen (infrastructure)
• Compositions in Crossplane are like recipes that combine infrastructure ingredients
• Standardization is crucial for successful platform engineering implementations
• The API-first approach is essential for building scalable internal developer platforms
• Larger organizations with hundreds of engineers benefit most from platform engineering investments

Check out our website to learn more about BBD Cool and how our platform engineering services can help your organization create efficient, standardized infrastructure that empowers your developers.

00:25 Introduction

01:38 What is Platform Engineering? 

04:40 What does Kubernetes involve?

06:00Where does Crossplane fit in? 

08:00 Is there too much flexibility in Crossplane? 

10:10 What are compositions in Crossplane? 

13:00 Top pros and cons of using Crossplane

17:00 Final tips for aspiring Platform Engineers

20:03 Closing thoughts