Pratik Daga

Thanks to Anurag Aggarwal and Ravi Kiran Achalla for encouraging the development of this idea from my earlier post. In response to your inquiry about whether trust architecture guardrails could be integrated directly into application code, I took your feedback to heart and have built a solution. I am excited to announce the release of llm-trust-guard on npm. This TypeScript package provides deterministic guard checks for LLM/agent workflows, addressing concerns such as prompt injection, encoding attacks, memory poisoning, tool-chain validation, trust boundary checks, and more. It also includes integrations for Express, LangChain, and OpenAI wrappers. This release continues the direction I’ve been pursuing: - Architecture-level validation: https://lnkd.in/gdCe5nKj - Code-level guardrail enforcement: https://lnkd.in/gPw7xUuw #AIEngineering #LLMOps #AIGovernance #AgenticAI #TrustArchitecture

27

7 Comments

I am still energized from last week's #GCCXHyderabad event. Thank you 3AI and HYSEA for bringing leaders from the industry like the OG of GCCs - Lalit Ahuja along with several others to share their deep insights.   Here were the Key Takeaways from the event that resonated the most with me:   * Adopt a Cofounder Mindset: Leadership in today's GCCs means shifting from mere execution to true ownership and innovation. The target maturity needs to be set from top-down but embraced bottom-up. * Empower Your Teams: Create one unit - call them pods/squads that is capable of creating anything and everything and takes full ownership - let them own the full gamut of tasks and not just code — from requirements to building to QA to DevOps and so on. Help teams see the big picture—explain the "what" and "why," not just the "how." * Progress Over Perfection: Only 15% of AI experiments make it to production. Instead of chasing perfection, help your teams ship more often. * Data Before AI: Your AI is only as good as your data. Invest in robust data foundations to amplify your AI ambitions. * Monitor, Listen & Adapt: Put the right tools in place to track productivity gains from your AI adoption. Regularly engage with teams to uncover what's working, what's not, and help move the needle on real challenges. * Never Stop Upskilling: Bring in early talent, create a top-notch enablement plan, champion continuous up-skilling, and practice internal mobility - which means lower costs, greater loyalty, and deeper tribal knowledge. * Strengthen Academia Partnerships: Bridge the talent gap by collaborating with academia to help them keep pace with the rapid tech innovation. * Human-Centric Leadership: Empathy isn’t optional—make it the foundation of your culture. * Innovation as the Organizational DNA: Make innovation your default mindset. With co-located and cross-functional teams across tech, product, support, professional services, customer success, marketing, legal, rev-ops and sales, the impact is direct and transformational. The goal of a GCC shouldn't be to gain independence from the HQ; the focus needs to be to drive purposeful, aligned innovation with business context and global goals.

60

1 Comment

Enjoyed collaborating with Josephine Joyce on this DZone article: https://lnkd.in/gAhNzXd9 . In Performance-Focused Platform Engineering, we explore why platform teams must evolve beyond enablement and start engineering for measurable performance outcomes. Curious to hear how others are thinking about this. #PlatformEngineering #DevOps #SRE #CloudNative #IDP

8

Most systems fail not because they get answers wrong, but because they answer too early. Last week, Santosh Jaiswal articulated this cleanly in his post on “When answering is NOT the right answer.” It resonated because I’ve been working on a framework where UNKNOWNs are not defects to eliminate, but deliberate design artifacts. First-class citizens. https://lnkd.in/gpGW8A73 In complex systems, architecture once made ignorance explicit. We spent time defining what we knew, what we didn’t, and what we were intentionally deferring. In today’s sprint-driven execution model, that step is usually skipped. We build partial truths, demo something that “works,” then pay for it in rework because the boundaries were never declared. AI workflows behave the same way. Give them an incomplete picture and they will still complete it—plausibly, confidently, and often prematurely. The alternative is structural honesty. Partition the problem into phases. Each phase explicitly declares what it will not solve. UNKNOWNs are scoped, tracked, and deferred by design. If you know what not to fill now, you avoid rework later. This pairs naturally with Venkat Pillay’s “Needs” framing: what is required now versus later, driven by evidence rather than optimism. Combined, you get phased architecture where exclusions are explicit and progression is data-backed. AI doesn’t invent answers; it preserves your explicit structure. This is where human cognition becomes more relevant, not less. AI operates inside a defined problem space. Human curiosity defines the space itself. It decides which UNKNOWNs are worth preserving, which assumptions are premature, and which questions should not yet be answered. That requires intent and judgment. Models do not do that. At CloudFrame, this is how we use AI. Translation, analysis, migration—AI excels. For boundary-setting, sequencing, and confronting unknown unknowns, automation stops. Architecture resumes. In an age of powerful automation, the scarce skill is not producing answers; It is knowing when not to.

21

1 Comment

Had a great time attending the Product Thinking and the Future of EPD(Engineering, product and Design) as an Engineering Leader event hosted by ELC today! A big thank you to ELC and Commure for organizing this thoughtful and engaging session—and to the amazing panel, including Dhruv Parthasarathy, Nissim Lehyani, James Tyack,Patrick Gallagher, for sharing their candid insights on how the traditional boundaries between Engineering, Product, and Design are rapidly evolving in the age of AI. Here are a few takeaways that really stuck with me: 🔹 Companies are shifting away from static PRDs and increasingly using prototypes as a discovery tool—validating ideas with customers early and often. 🔹 Hiring is changing: it’s less about checking boxes and more about finding problem solvers and clear thinkers who can adapt and lead. 🔹 When it comes to LLMs, everyone has their own “AHA moment.” Until then, keep exploring. You’ve essentially got the smartest person in your pocket—start using it to solve real problems. Excited to bring these insights back to my team and continue experimenting at the intersection of product, engineering, and AI! #EngineeringLeadership #AI #ProductThinking #ELC #LLM #TechLeadership #Prototyping #EPD

9

2 Comments

Spring Boot 4 adopts JSpecify for null-safe APIs. I wrote a short post on using JSpecify alongside Optional to reduce null-related issues in Java applications. https://lnkd.in/gz7SsfyJ

10

Salim FEKU not able to digest NoSQL databases are converging to PostgreSQL. Setting: A chai ki dukaan (tea stall) in Hyderabad. Salim Feku is holding forth, gesturing wildly with his chai glass. (Salim Feku sips his chai, then puts the glass down with a flourish, looking around at an imagined audience.) SALIM FEKU: Arre bhai, aaj kal ki duniya mein kya chal ra? Sab bolte, "PostgreSQL, PostgreSQL!" Kya hai re usmein? Apun bolta, woh to puraane zamane ka ghoda hai! (He leans in conspirantly, lowering his voice slightly but still loud enough for everyone to hear.) SALIM FEKU: Suno tum meri baat, agar tumko asli data rakhna hai, jo kabhi nikalta nai, jo tezz bhaagta, toh tumko NoSQL hona bhai! Yeh PostgreSQL, woh to apna aadmi, kaisa... sara din join lagate baithta. Ek table se dusra table, dusre se teesra... Arre baba, jab tak woh join lagake data nikalta, apun chai pi ke doosra dhanda kar leta! (He chuckles, shaking his head.) SALIM FEKU: Apun ke paas kya hai? MongoDB! Arre kya cheez hai woh! Tum feko usmein kuch bhi! Document feko, JSON feko, apun ka data, seedha andar. Koi tension nai. Woh PostgreSQL, usko pehle batao 'column' kya hai, 'row' kya hai, 'schema' kya hai. Arre baba, itna sochne mein hi system slow ho jata! (He snaps his fingers.) SALIM FEKU: Aur suno, jab apun ka traffic aata, haan? Croreon log aate, kya karte? Woh PostgreSQL to ek machine pe baitha rehta. Apun ka Cassandra, DynamoDB... yeh log kya karte? "Arre bhai, thoda data tu le le, thoda data main le leta," bolke baant lete. Shaana log! Sab milke kaam karte, isliye apna system kabhi rukta nai, bhai! Woh PostgreSQL ko to pasina aa jata! (He gestures expansively, like he's spreading data everywhere.) SALIM FEKU: Woh bolte, "Hum consistent hai!" Arre haan, theek hai. Lekin consistency ke chakkar mein kya, apna customer wait karte baithta! Apun ka NoSQL? Woh bolta, "Thoda late aaya to kya hua? Aayega to sahi! Customer khush hona, jaldi result aana!" Zara sa "eventually consistent" hua to kya hua? Duniya to chalte rehti na! (He lowers his voice again, for the grand finale.) SALIM FEKU: Toh bhai, agar tumko naya, tezz, bina tension ka system banana hai... toh woh puraane dimaag ke relational ko choro! Apun ka NoSQL, woh hai asli king! Baaki sab to... chillar hain! (Salim Feku takes another proud sip of his chai, looking very pleased with his pronouncement.Just then, his eyes fall on someone approaching the chai stall. His confident smirk vanishes, replaced by a look of surprise, then forced cordiality.) SALIM FEKU: (Suddenly a soft, almost meek tone) Arre! Kaun? Ismail Bhai... PostgreSQL wale!?! (He rushes forward, abandoning his lecture, ) SALIM FEKU: Aao Miya! Kaisa hai tum? Tumhari hi raah dekh raha tha main! Kaisa, sab khairiyat? Woh, woh main to... ...main to bas logon ko bata raha tha ki purani cheezo ki reliability kitni zaroori hai! Haan! Tumhari hi baat kar raha tha, bhai! Maine suna..tum bhi distributed ho gaye!!!

8

Is there a point to these posts on Hawa Mahal and “ancient climate solutions”? I’m struggling to see it. If the point is “go back to our roots”, that idea has been repeated endlessly — and ignored just as consistently. At some stage, it’s worth asking: Is the idea incomplete, or is it simply impractical at scale? Also, there’s nothing uniquely Indian about this. People across geographies have always adapted to their environments: - Desert regions developed passive cooling - Arctic communities built for extreme cold - Tropical regions designed for heavy rainfall Humans solve for time and place. But times have changed. We now have: - Urban density at an unprecedented scale - Resource constraints very different from the past - Expectations of comfort that didn’t exist earlier - And importantly, structures like Hawa Mahal were built for a tiny, privileged section of society — not something that can be generalized for millions. If the goal is to drive change today, the conversation needs to move from “look at what royalty built centuries ago” to “what scalable, modern solutions can work for today’s realities.” Otherwise, it remains an interesting history lesson — not a practical direction.

3

1 Comment

At One2N, we’ve been running OpenTelemetry Collectors on Kubernetes in production for a while now. Along the way, we ran into a few non-obvious challenges, especially around processor behavior, rate limiting, data volume, and stability at scale. In this post, Sanket Rajgiri and Spandan Ghosh share what went wrong, what we learned, and the practical changes that helped us run OTel collectors more reliably in Kubernetes. 👉 https://lnkd.in/dV8NH6gS

16

2 Comments

Reality check: Building enterprise trust goes beyond mere features; it hinges on demonstrable engineering excellence: - Emphasizing **Release velocity** over roadmap promises leads to enhanced Predictability. - Balancing **Incident response** with innovation ensures heightened Reliability. - Elevating **Automation maturity** beyond AI hype fosters improved Scalability. At OpsHub, Inc., quantifying these capabilities, has revolutionized customer interactions, resulting in increased satisfaction and retention. The key takeaway? Engineering rigor extends beyond internal operations; it forms the cornerstone of your most compelling customer narrative. Currently crafting an in-depth article on this topic with all my experiences. For instance imagine the level of automation we have that we are releasing weekly. Your input is invaluable: ""Which engineering metrics do you highlight to cultivate customer trust?"" #EngineeringExcellence #CustomerSuccess #B2B #Eng #Leadership #integration #opshubengineering

50