As Agentic AI transitions from labs to live environments, it brings not just promise—but unprecedented complexity. These aren’t just programs; they’re systems that plan, decide, and act autonomously.
This shift calls for a new design imperative: security by architecture, not afterthought.
Our latest newsletter unpacks the real risks and outlines practical design strategies for securing next-gen autonomous systems.
📖 Read the blog to explore how to build trustworthy, resilient Agentic AI.
Agentic AI is shifting from proof-of-concept to strategic capability. But for it to be enterprise-ready, it must evolve—fast. It’s no longer enough for agents to plan, reason, and act.
Enterprises demand systems that are secure, efficient, observable, and accountable. Here’s what enterprise-ready Agentic AI really means: 🔒 Security by Design
✅ Agentic AI must now be lean, secure, explainable, and scalable.
It’s not about building more agents. It’s about creating the right ones—that last, that scale, and that earn trust.
🔍 For a deeper dive into designing cost-, carbon-, and complexity-efficient agentic systems, do visit https://leanagenticai.com
🤖 As Agentic AI scales, four problems surface fast:
We need to design autonomy that’s efficient, not excessive.
That’s where Lean Agentic AI comes in — a mindset and methodology to build agentic systems that optimize cost, carbon, and complexity. It means choosing the right-sized models, designing workflows with intention, reducing unnecessary memory/state, and adding smart guardrails to avoid runaway invocations.
To help developers, architects, and researchers get started, I’ve created a public repo explaining the core concepts of what “lean” really means in the context of agentic AI—covering principles, pitfalls, and design patterns.
🔗 GitHub Repo – Lean Agentic AI
https://lnkd.in/dp8KZVku
The repo includes byte-sized learnings, practical strategies, mental models, and architectural insights.
For a deeper dive into building cost, carbon, and complexity-efficient systems— 📘 Refer to my book: Lean Agentic AI – Cost, Carbon, and Control
(Linked in the repo and available worldwide via Amazon)
Let’s build agentic systems that are lean by design, responsible by intent.
📄 MCP vs A2A – Protocol Comparison for Agentic AI
As agent-based systems scale, two key protocols are shaping how AI agents communicate and operate:
🔹 Anthropic’s Model Context Protocol (MCP) – Ideal for enhancing a single agent with dynamic tool access and contextual grounding.
🔸 Google’s Agent-to-Agent (A2A) Protocol – Designed for agent collaboration, delegation, and decentralized orchestration.
This one-page PDF provides a crisp comparison across 15 core features — from architecture to security to real-world applicability.
📥 Download the visual PDF below.
#mcp #a2a Anthropic Google
Lean Agentic AI Principles – Minimize Cost • Carbon • Complexity
Build AI that’s lean by design, not bloated by default.
Agentic AI unlocks powerful capabilities—but without the right principles, it can become expensive, inefficient, and hard to scale.
This visual guide introduces foundational strategies to design efficient, effective, carbon-aware, and purpose-driven agentic systems.
From smart memory use to carbon-aware orchestration, each pattern focuses on doing more with less—without compromising intelligence or impact.
📄 Explore the guide and design with cost, carbon, and control in mind.
Stay tuned for more insights on Lean Agentic AI.
📌 Every major tech wave starts the same way—rapid innovation, competing tools, and a race for adoption— until one thing consistently reshapes the landscape: standardization.
What we’re seeing with Agentic AI today isn’t new—it’s déjà vu. Let’s rewind:
🔍 Web Era (Search Engines & SEO):
Early web discovery featured search engines like AltaVista, Yahoo!, and Lycos.
Then came Google, redefining relevance, introducing keyword bidding, and creating a structured SEO economy that transformed online visibility.
🔗 API Era (SOAP, REST):
Software integrations were once built on varied protocols—CORBA, XML-RPC, SOAP—each with different implementation models.
REST simplified the landscape with stateless design and broad adoption, becoming the foundation of the modern API economy.
📱 Mobile Era (Platforms to App Stores):
Mobile apps initially ran on diverse platforms—Palm OS, Symbian, BlackBerry OS, and Windows Mobile—each with its own development tools and delivery models.
Then came Apple’s iOS and Google’s Android, offering unified environments.
With the App Store and Google Play, mobile development was transformed—standardizing discovery, monetization, and updates.
🤖 Agentic AI Era (Frameworks to Protocols):
Today, we see a growing number of agent frameworks—LangChain, AutoGen, CrewAI, LangGraph—each shaping its own vision of agentic workflows, memory, and orchestration.
At the same time, protocols like Anthropic’s MCP and Google’s A2A are emerging to standardize how agents interact and collaborate, much like REST did for APIs and app stores did for mobile.
The next evolution is the rise of agent marketplaces—the AI equivalent of the App Store or Play Store—where agents and tools are composed dynamically to complete tasks and deliver outcomes, standardizing how we discover, deploy, and monetize autonomous capabilities.
If you’re feeling overwhelmed by the pace—new models every week, frameworks launching daily, everyone racing to define the future—relax.
The model race, the framework race, the agent race—it will all settle. It always does.Standardization brings clarity. And with it, scale. This is the natural rhythm of tech evolution.
And if AI follows the same arc of consolidation we’ve seen in every prior wave…
What do you think it will become— the operating system, the interface, or the infrastructure of tech?
🌍 Code runs everywhere. So does its environmental impact.
From AI to UI, from cloud choices to code structure — every decision we make in software design affects energy, carbon, and sustainability.
This isn’t about adding another checklist. It’s about shifting the mindset.
Attached is a visual deck of Green Software Principles — practical ideas that apply across roles, tools, and technologies.
Let’s build systems that are not just smart — but sustainable.
#GreenSoftware #SustainableTech #ClimateAction #AI #Cloud #SoftwareEngineering #DigitalSustainability Green Software Foundation
🎯 Google cloud launches Agent Development Kit (ADK) to Simplify Multi-Agent Development.
The ADK provides an open-source framework for building multi-agent AI applications with control, modularity, and integration in mind.
ADK supports both deterministic pipelines and LLM-driven workflows, allowing developers to define structured and adaptive agent behaviors. 🔘 Multi-Agent Composition – Build modular applications by chaining or nesting agents 🔘 Model Control Protocol (MCP) – Connect tools, models, and data sources securely 🔘 Apigee Integration – Use existing business APIs directly within agent workflows 🔘 Tool Ecosystem – Extend agents with built-in tools, custom logic, and external libraries 🔘 Evaluation Framework – Debug and test with built-in evaluation tools 🔘 Flexible Deployment – Deploy agents locally, on Kubernetes, or using Vertex AI 🔘 Connected Intelligence –
🚀 Getting Started with ADK
Install the SDK, explore documentation, and start with examples:
🔗 Documentation & Tutorials - https://lnkd.in/dFW7XYvC
ADK provides a practical path for building scalable agent-based systems with built-in connectivity and control.
#GoogleCloudNext #AIDev #NewTech #ADK #GoogleCloud Google Cloud
🎥 The video attached is generated using Veo 2, announced at Google Cloud Next 2025 — showcasing just a glimpse of what’s now possible with generative video.
Here’s the prompt that brought it to life:
A cinematic video of a large-scale tech conference, Google Cloud Next 2025, in a grand convention center. Show diverse attendees arriving, registering, networking, and walking past booths with vibrant digital signage. Transition to a packed keynote hall with dramatic lighting. Cut to breakout sessions with developers coding on laptops, enterprise leaders in panel discussions, and immersive demos of generative AI tools and cloud infrastructure. Show an AI like human robot taking a breakout session. Create a wide-angle view of the venue’s exterior, lit up at night, with banners that read ‘Google Cloud Next 2025 – The Future is Now.’ Realistic animation, dynamic camera angles, energetic music, and natural crowd motion throughout.
🪄 With Veo 2, it’s more than just video generation — it’s about:
Paired with Lyria for text-to-music, the result is a cohesive, AI-augmented creation experience — where your ideas shape both visuals and sound.
📌 Business applications? From quick concept videos to consistent, scalable brand storytelling.
Remember: AI doesn’t know what to create until you prompt it.
It’s not automation — it’s augmentation.
We’re entering the age of AI-native content pipelines — flexible, fast, and fully prompt-driven.
#Veo2 #Lyria #GoogleCloudNext2025 #GenerativeVideo #TextToMusic #VertexAI Google Cloud #DeepMind #GoogleCloud #AIHypercomputer
Lean Agentic AI Principles – Minimize Cost • Carbon • Complexity
Build AI that’s lean by design, not bloated by default.
Agentic AI unlocks powerful capabilities—but without the right principles, it can become expensive, inefficient, and hard to scale.
This visual guide introduces foundational strategies to design efficient, effective, carbon-aware, and purpose-driven agentic systems.
From smart memory use to carbon-aware orchestration, each pattern focuses on doing more with less—without compromising intelligence or impact.
📄 Explore the guide and design with cost, carbon, and control in mind.
Stay tuned for more insights on Lean Agentic AI.
Excited to share the release of my latest book – Lean Agentic AI: Minimizing Cost, Carbon, and Complexity
📘 https://leanagenticai.com/
I’ve written several tech books over the last two decades—but this one is different. It’s not just another AI book. It’s a mindset shift. The first to focus entirely on designing agentic systems that are lean, efficient, cost-effective, carbon-aware and responsible from the ground up.
When I built my first agent, the focus was on orchestration—chaining steps, calling models, getting results. But as things scaled, it became clear: the system was overthinking, overspending, and overconsuming—on autopilot.
The industry keeps chasing bigger models, longer context windows, and leaderboard scores. But here’s the truth:
💬 “Intelligence without discipline leads to waste.
This book introduces new and practical concepts—like Purpose-Bound Reasoning, Model Minimalism, Cost-centric design, Memory Cost Index, Elastic Intelligence, and Lean Workflow Design—all aimed at making autonomy efficient, not excessive.
🔍 What You’ll Find Inside
💰 A clear view of the cost and carbon impact of agentic systems
Understand how seemingly simple workflows quickly become energy-intensive, driving up operational costs,
🧠 The root causes of waste in AI agents
Explore how prompt design flaws, uncontrolled loops, poor model choices, and lack of feedback loops lead to inefficiency and cost escalation.
⚙️ Design principles for building lean agentic systems
Apply fewer agents, smaller models, dynamic routing, tighter prompts, and caching strategies—without compromising output.
📊 Evaluation of leading frameworks
Learn how CrewAI, LangGraph, and AutoGen differ in control, resource awareness, and energy impact.
🌍 Deployment strategies for efficient operations
Use green regions, scale responsibly, and build carbon-aware infrastructure choices into agent design.
📉 Examples and patterns that work
Real-world strategies to reduce token usage, cap API calls, and maintain balance between autonomy and control.
🔮 And more …
Design for cost efficiency, Green agents, carbon dashboards, and systemic sustainability monitoring inside agentic pipelines.
If you’re building or scaling agentic systems, this book was written with you in mind.
Let’s reimagine intelligence—leaner, smarter, and truly sustainable.
🧠 Most agentic AI systems today are overthinking, overspending, and overconsuming—on autopilot.
This edition uncovers why smarter agents don’t mean bigger models or longer memory. In fact, that mindset may be the trap.
Lean is not less. Lean is what makes autonomy sustainable.
📥 Click to read the latest newsletter:
“Rethinking Agentic AI: Why Lean Is the New Smart
🖼️ Ghibli-Style Generations - What if your AI-generated art came with an energy label?
Social media is overflowing with stunning Ghibli-style AI creations—soothing scenes, magical creatures, timeless vibes. But here’s the hidden layer most users never see:
⚡ Behind every dreamy prompt is real compute.
🌍 Behind every scenic output is a carbon cost.
🧠 Behind every “generate” click, a resource is consumed.
Most users don’t know.
And why would they?
There’s no meter. No alert. No visibility.
🤔 Why don’t Gen AI systems display a basic energy or emission estimate—just like nutrition labels or price tags?
Not to stop creativity.
But to inform it.
Not every generation is heavy. Not every user will care.
But some will, if we give them the choice to know.
🎨 Ghibli-style art celebrates nature.
Let’s honor that by making digital creativity more transparent—and more sustainable.
Because when awareness becomes default, sustainability becomes a choice.
In fact, OpenAI’s CEO, Sam Altman, recently revealed:
“We’ve been growing a lot and are out of GPUs.” (source: yahoo news)
This highlights the immense computational power required for such AI models—leading to significant energy consumption and environmental impact.
Trends will keep changing.
Today it’s Ghibli-style. Tomorrow it’ll be something new.
But building awareness now would shape how responsibly we create—no matter what trend comes next.
#GenerativeAI #GreenAI #ResponsibleCreation #GhibliStyle #SustainableDesign #TransparencyInAI #CarbonFootprint #DigitalAwareness #OpenAI Green Software Foundation
Choosing the right agentic framework isn’t just about features — it’s about how your agents think, plan, and act.
In 2025, the diversity in AI agent frameworks reflects their core philosophies — from minimalist code-first loops to graph-based workflows and decentralized simulations.
How agents think and act varies by framework:
🔁 LangChain – Chain-of-thought prompts
💬 AutoGen – Asynchronous message-driven agents
📋 Semantic Kernel – Planner-skill execution model
👥 CrewAI – Role-based pipelines
📊 LlamaIndex – Workflow graphs with shared context
💻 SmolAgents – Code-first agent loop
📐 PydanticAI – Structured, validated outputs
🌐 AgentVerse – Agents in simulated environments
Cloud-native frameworks are also shaping enterprise AI adoption:
I’m working on a detailed multi-dimensional comparison covering:
🔹 Execution models & coordination
🔹 Tool use & memory integration
🔹 Scalability strategies & deployment readiness
🔹 LLM interoperability & extensibility
🔹 Business impact, ROI potential & real-world adoption patterns
If you’d like an early copy of the draft, drop a comment below 👇
🚀 Project IDX — The Google Cloud-Native IDE That Might Just Replace Your Local Setup
IDX brings the full power of a local dev environment straight to the browser — running on pre-configured VMs on Google Cloud. No setup headaches, no local machine bottlenecks. Just open, code, and go.
I’ve been using IDX recently, and it definitely removes all the usual setup pain — especially when switching between frameworks or onboarding onto new projects.
If you’re looking for:
Some of the features include:
✅ Pre-built templates for frameworks like React, Angular, Flutter, Next.js, Vue, and more
✅ Built-in Android & web emulators for testing apps directly in your workspace
✅ Gemini AI integration for code suggestions, terminal commands, refactoring, and documentation — right from an interactive chat
✅ GitHub integration — instantly import private or public repos
✅ Direct deployment to Firebase Hosting
✅ Full VM-based, cloud-powered workspaces that are customizable and reproducible
It’s still in beta, but the experience is already smooth and production-friendly for many workflows.
Try it out at - https://idx.google.com/
#ProjectIDX #GoogleCloud #CloudIDE #DeveloperTools #AIinDev #WebDev #FlutterDev #GeminiAI #SoftwareDevelopment #DevExperience Google Cloud
From Code to No-Code to Vibe Coding: The Evolution and Its Trap
Programming has always evolved to make complexity manageable.
We started with low-level code, where every instruction had to be precisely crafted. Then came high-level languages, followed by frameworks that abstracted repetitive patterns. Soon, no-code platforms emerged—enabling non-developers to build apps with visual workflows.
Now we’re in the age of vibe coding—where describing what you want in plain language generates usable code. You don’t just write code anymore—you vibe with it.
It’s revolutionary.
But here’s where evolution can drift into illusion.
Vibe coding thrives on patterns, not understanding. It can write, but it doesn’t reason.
It can mimic best practices, but it doesn’t invent them.
It solves what it’s seen—not necessarily what’s best for your problem.
Sometimes, it generates complex logic where simpler solutions exist—adding layers that make debugging and maintenance harder than they need to be.
Over time, we risk swapping thinking with prompting.
When developers over-rely on AI to generate logic, they stop challenging it.
They stop learning why something works, or how to refactor it better.
The code looks clean—but under the hood, it may be inefficient, insecure, or just wrong.
Evolution was meant to empower—not to detach.
Use vibe coding to accelerate.
But never let it replace understanding.
Otherwise, we’re not evolving. We’re looping in reverse—with prettier outputs, and shallower roots.
🔍 What’s often missing in our AI conversations?
🚀 New LLMs are outperforming previous generations — with better accuracy, faster inference, and richer capabilities.
🧠 New AGENT FRAMEWORKS are emerging — enabling planning, memory, and autonomous execution.
📡 Protocols like MCP (Model Context Protocol) are redefining how agents retain and reason with context — adding structure to multi-step decision-making.
We talk about it all:
➡️ Model performance
➡️ Framework innovation
➡️ Multi-agent orchestration
But rarely do we ask:
❗ How are we operating these intelligent systems once they’re live and acting on our behalf?
As AI systems become more autonomous, the real challenge shifts from building them to operating them responsibly.That’s the focus of the latest edition of our newsletter:
🧭 ResponsibleOps: A Strategic Imperative for CIOs and Digital Leaders
It offers a 7-pillar operational framework to embed trust, transparency, efficiency, and oversight into enterprise AI systems—across software, infrastructure, policy, and human interaction.
📥 Explore the blog and uncover what’s missing from most enterprise AI strategies—not just how to build AI, but how to run it responsibly.
👇 Read now and share with teams shaping AI operations and governance.
🚀 Excited to Launch the Green Software Researcher Agent – Now in Beta!
Awareness is the first step toward change—and today marks a meaningful milestone in the journey toward sustainable software.
This has been a personal passion project—building an agent to support the software community in learning how to design and build green software applications.
🔧 What it does:
The agent helps with discovery, analysis, and insight generation, drawing from publicly available data via trusted LLM providers to offer grounded, actionable responses.
🌍 The application provides insights using the Green Software Foundation (GSF) principles:
💡 Examples of what you can ask:
🔍 Whether you’re in development, architecture, UI, AI, or infra—just ask your question.
🧪 This is a beta version, so expect a few rough edges. Your feedback will help shape the future. I’ll also be publishing a blog soon with key learnings from building this agent.
🌱 This is just the beginning of a larger journey to embed sustainability into every phase of software development. Let’s make awareness the default starting point.
👉 Try it now – https://greensoftware.ai/
💬 Share your feedback and forward this to your networks. Let’s build this together.
Co-pilots can be powerful assistants — but I’ve often found myself looking for a simple solution, only to be steered into a more complex route. Interestingly, most co-pilots follow similar patterns — a reflection of learning from the same data.
That’s where experience plays a crucial role. Only when you’ve navigated the challenges yourself can you prompt the co-pilot to choose simpler, smarter paths.
Relying solely on co-pilot output, without your own perspective, is like accepting complexity as the default — when simplicity was always an option.
Green Software: A Strategic Lever for Reducing Cost and Carbon
Green Software is one of the most powerful levers to reduce both cost and carbon emissions—especially in the era of Generative AI.
From optimizing code, improving compute and model efficiency, to using effective prompting and selecting the right model for the task—every design decision counts.
💡 Small changes at scale lead to meaningful impact.
Here’s a snapshot from my recent panel discussion on this topic.
🎥 Watch the full video on my YouTube channel:
🔗 https://lnkd.in/dhk-hwDH