Posts

LangChain recently published a helpful step-by-step guide on building AI agents.
🔗 How to Build an Agent –https://lnkd.in/dKKjw6Ju

It covers key phases:

1. Defining realistic tasks
2. Documenting a standard operating procedure
3. Building an MVP with prompt engineering
4. Connect & Orchestrate
5. Test & Iterate
6. Deploy, Scale, and Refine

While the structure is solid, one important dimension that’s often overlooked in agent design is: efficiency at scale.
This is where Lean Agentic AI becomes critical—focusing on managing cost, carbon, and complexity from the very beginning.

Let’s take a few examples from the blog and view them through a lean lens:

🔍 Task Definition
➡️ If the goal is to extract structured data from invoices, a lightweight OCR + regex or deterministic parser may outperform a full LLM agent in both speed and emissions.
Lean principle: Use agents only when dynamic reasoning is truly required—avoid using LLMs for tasks better handled by existing rule-based or heuristic methods

📋 Operating Procedures
➡️ For a customer support agent, identify which inquiries require LLM reasoning (e.g., nuanced refund requests) and which can be resolved using static knowledge bases or templates.
Lean principle: Separate deterministic steps from open-ended reasoning early to reduce unnecessary model calls.

🤖 Prompt MVP
➡️ For a lead qualification agent, use a smaller model to classify lead intent before escalating to a larger model for personalized messaging.
Lean principle: Choose the best-fit model for each subtask. Optimize prompt structure and token length to reduce waste.

🔗 Tool & Data Integration
➡️ If your agent fetches the same documentation repeatedly, cache results or embed references instead of hitting APIs each time.
Lean principle: Reduce external tool calls through caching, and design retry logic with strict limits and fallbacks to avoid silent loops.

🧪 Testing & Iteration
➡️ A multi-step agent performing web search, summarization, and response generation can silently grow in cost.
Lean principle: Measure more than output accuracy—track retry count, token usage, latency, and API calls to uncover hidden inefficiencies.

🚀 Deployment
➡️ In a production agent, passing the entire conversation history or full documents into the model for every turn increases token usage and latency—often with diminishing returns.
Lean principle: Use summarization, context distillation, or selective memory to trim inputs. Only pass what’s essential for the model to reason, respond, or act..

Lean Agentic AI is a design philosophy that brings sustainability, efficiency, and control to agent development—by treating cost, carbon, and complexity as first-class concerns.
For more details, visit 👉 https://leanagenticai.com/

#AgenticAI #LeanAI #LangChain #SustainableAI #LLMOps #FinOpsAI #AIEngineering #ModelEfficiency #ToolCaching #CarbonAwareAI LangChain

Google Gemini now lets you generate short, dynamic videos from a single static image, using Veo 3 technology. 🎨 What you can do with it:

• Bring travel photos to life with ambient sound and motion 🌊
• Animate illustrations or designs with matching audio 🎧
• Add motion and narration to social media posts or presentations ✨

📌 How it works:

1. Open Gemini (web or mobile app)
2. Select “Video” from the prompt bar
3. Upload an image
4. Enter your prompt
5. Receive an AI-generated 8-second video

ℹ️ Note: Google Gemini will automatically add a visible watermark to indicate the video was AI-generated, along with an invisible SynthID digital watermark for traceability.

📍 Currently available for Gemini Advanced (Pro) subscribers in select regions.

The video attached here was generated using two static images. There’s a message in it — hope you all get it. 🙂

#Gemini Google Cloud #Veo3 #PhotoToVideo #AI #GenerativeAI #ContentCreation #FutureOfWork #Efficiency #CreativeTech #SynthID

When software begins to think, plan, and act, how do humans stay ahead?

Today, we mark 50 episodes of Agentic AI: The Future of Intelligent Systems, reflecting deeply on our relationship with software that no longer just computes but genuinely thinks.

From autonomy and orchestration to sustainability and alignment, each conversation brings us closer to answering one critical question:

How do we build intelligent systems that amplify human intention, rather than override it?
🎧 Episode 50 flips the challenge:
How do we out-think Agentic AI?
📍 Spotify → https://lnkd.in/dpE3BqKu
📍 Apple Podcasts → https://lnkd.in/dpfiyWDK

Thank you for listening and thinking alongside us.

Around four years ago, the Green Software Foundation Standards Working Group started as a small group of us meeting every week. Through small, consistent actions, regular check-ins, GitHub updates, and collective thinking grounded in inclusivity and consensus, we shaped what would become the world’s first ISO specification for measuring the carbon emissions of software: the Software Carbon Intensity (SCI) standard.

What began as a modest effort steadily grew as more organizations came on board to contribute. Participation expanded, ideas multiplied, and today the Green Software Foundation has evolved into a thriving community of over 60 member organizations, all united by the goal of making software part of the climate solution.

Building on this foundation, we are now extending the specification to Artificial Intelligence through SCI for AI. We recently hosted the first SCI for AI workshop, bringing together over 20 contributors from across industries, all driven by the shared goal of creating consistent, transparent, and actionable metrics to measure the environmental impact of AI systems. Here is the link to the workshop report, which captures the key discussions, outcomes, and the path ahead: https://lnkd.in/di_8ebY5

To explain to my daughter what I do on these weekly calls, I tell her it’s like coming up with the formula for calories on food labels, which she related to instantly. Once something becomes visible, people become more aware and can make better choices.

A big thank you to all the members who have been participating, contributing their time and expertise every week, and to the organizations supporting them to help take this important work forward.

I am fortunate to chair the Standards Working Group and help steer the SCI for AI initiative. Together, we are making the invisible visible by exposing emissions that are otherwise hidden and unaccounted for in the digital systems we use every day, and by providing ways to reduce them. Making emissions measurable is the first step toward managing their impact and driving reduction.

Let’s make Sustainable AI the new norm.

In the Agentic era, choosing how to think is as important as what to think.
Agentic AI systems don’t just act—they reason.
But not all reasoning is created equal—and getting it wrong can waste both cost and carbon.

Here’s a simple framework for reasoning optimization in Agentic AI:
🔹 Stage 1: Close-Ended Reasoning
✅ Deterministic answers (yes/no, select one)
✅ Lowest cost
✅ Lowest emissions
✅ Ideal when the workflow is clear, the outcome set is limited, but AI is still useful for interpreting unstructured inputs.

🔹 Stage 2: Pre-Compiled Thought
✅ Curated, finite responses from precomputed knowledge
✅ Moderate cost
✅ Low emissions
✅ Use when the space of possible answers is finite and repetitive—and full creative generation is unnecessary.

🔹 Stage 3: Open-Ended Reasoning
✅ Generative, exploratory, creative thinking
❌ Highest cost
❌ Highest emissions
✅ Reserve for tasks that demand creativity, ideation, or complex synthesis.

👉 The path to lean, responsible AI is not just about selecting the right model—
It’s about selecting the right type of reasoning at the right time.
Smarter reasoning choices drive:
✔ Lower compute
✔ Lower cost
✔ Lower carbon
✔ Better outcomes

Most Agentic AI conversations are missing a key dimension: cost, carbon, and complexity.

While the spotlight is often on autonomy, orchestration, and innovation, the reality is that Agentic AI systems—if not designed intentionally—carry hidden risks that quietly erode value:
❗ Vague goals that trigger unnecessary actions, retries, and compute waste
❗ Over-planning and decision loops that burn resources without meaningful benefit
❗ Overuse of large models when smaller models would suffice
❗ Redundant tool calls and uncontrolled memory growth
❗ Silent system inefficiencies that drive up cloud costs and emissions without notice

As most organizations are experimenting with or just getting started on Agentic AI, this is the right time to embed efficiency, sustainability, and cost-awareness at the foundation—not as an afterthought.

That’s why I wrote Lean and Green Agentic AI—a white paper with a practical framework for building AI that is not only intelligent but also efficient, scalable, and economically viable.

The paper introduces:
✅ The six-stage Agentic AI lifecycle
✅ Lean principles to minimize cost, carbon, and complexity
✅ Practical techniques for energy-efficient models, inference, and carbon-aware execution
✅ A standardized approach to measurement using the Green Software Foundation Software Carbon Intensity (SCI) framework and its AI extension
📄 Access the full white paper here:
👉 https://lnkd.in/dU_zHHXg

The agentic future is coming fast. Let’s ensure it’s smarter, greener, and built to scale responsibly.

At the Green Software Foundation, we’re taking the globally adopted ISO Software Carbon Intensity (SCI) standard to the next frontier: SCI for AI.

As Chair of the GSF Standards Working Group, I’m excited to help steer this important initiative—working alongside the collective intelligence of our members to build open, interoperable standards that not only measure impact but inspire real action.

Our working group is developing a methodology to measure carbon emissions across the entire AI lifecycle—from the training of large models to inference and the growing use of Agentic AI. The goal: to equip developers, data scientists, and AI leaders with consistent, actionable metrics that enable smarter, greener AI decisions.

In the latest episode of GSF’s Environment Variables – Backstage, I join Chris Skipper to share insights on:

• The foundations of the SCI ISO specification
• The tools and technologies supporting carbon measurement
• How we are evolving the SCI for AI specification to meet the growing environmental impact of AI

We are also looking at shaping global benchmarks for sustainable software through initiatives like:

• The Real-Time Energy and Carbon Standard for cloud providers
• The SCI Guide for practical implementation
• The TOSS (Transforming Organisations for Sustainable Software) Framework

🎧 Listen to the full episode at :
https://lnkd.in/dchWQAza

Let’s make sustainable software and AI the new normal.

The AI revolution began in the cloud — but its most personal chapter is unfolding on your phone.

This edition explores the rapid emergence of Mobile AI Agents — compact, on-device systems that understand language, orchestrate tools, and act in real time. Powered by innovations like Gemma 3n, on-device RAG, and function calling, these agents represent a new class of intelligent, private, and multimodal assistants.

What you’ll discover:
🚀 From Cloud to Edge — The architectural shift reshaping AI experiences
🧠 Meet the Mobile AI Stack — Tools and models optimized for edge-native deployment
🔧 New Developer Mindset — Build for intent, not just interaction

The next wave of AI isn’t just in the cloud — it’s in your pocket.

Mobile AI is entering a new era — fast, private, and offline-ready, powered by compact yet capable LLMs. This shift isn’t just about smaller models. It’s about a tool-driven mobile ecosystem where language becomes the interface.

Imagine this:
🔹 Prompt in, action out — models that read your screen, understand your context, and call functions, all locally.
🔹 Multimodal inputs — text, image, audio, or video — all processed in real time, without leaving your device.
🔹 RAG on-device — search across local files, chats, or documents with no server needed.
🔹 Function calling as an API layer — where AI orchestrates toolchains natively across mobile platforms.

From apps that understand voice and visuals to workflows powered entirely by language, this is a new AI-native design paradigm — one that values privacy, speed, and control.

With models like Gemma 3n, the building blocks are already here. The ecosystem is evolving — it’s not just about apps anymore, but intelligent agents and tool-chaining systems running on edge.

Get ready for the Mobile AI stack — where language is the OS.

AI cost estimates can be off by 500–1,000% as per Gartner.Recent FinOps Foundation data shows 63% of organizations now actively manage AI spend—double last year—showing cost control is now mission critical.

Agentic AI enables transformative automation, but costs can spiral quickly—especially when agents autonomously call external tools, trigger APIs, and retry failed requests. To unlock innovation without losing control, organizations need a robust FinOps framework designed for Agentic AI:
FinOps Framework for Agentic AI

1. Cost Visibility & Tagging:
   Tag every agent, tool call, and workflow. Monitor costs per agent, tool/API, and retry.

2. Real-Time Dashboards:
   Visualize costs for each workflow step—agent, model, tool/API, and retry. Segment by business unit and integration.

3. Tools Integration Management:
   Catalog all tools/APIs. Limit and monitor invocations to prevent runaway “tool chaining.” Analyze usage and spend.

4. Retry Control:
   Set retry policies for model/tool calls. Use exponential backoff, max thresholds, and circuit breakers. Audit and optimize retry patterns.

5. Context Size Management:
   Trim input/output tokens and context windows. Enforce max context sizes and monitor related costs.

6. Dynamic Resource Allocation:
   Route simple queries to lightweight tools/models. Auto-scale within budget boundaries.

7. Automated Guardrails:
   Set spend thresholds and triggers. Pause or reroute costly workflows before breaching budgets.

8. Cost Attribution:
   Allocate costs to the right business or product unit. Use showback/chargeback for accountability.

9. Cross-Functional Collaboration:
   Make tool/retry cost control a shared KPI for engineering, finance, and business.

10. Continuous Optimization:
    Audit for inefficient tool use, context bloat, and high-retry agents. Balance cost, performance, and sustainability.

Agentic AI’s value compounds with scale—but only with FinOps discipline over every model, tool call, retry, and workflow. Proactive management is essential for sustainable innovation.

Bringing Order to Content Chaos: How Gemini CLI Elevates Your Command Line Productivity

Creating and curating digital content often leads to an overwhelming number of files—drafts, revisions, duplicate versions, and scattered resources. Anyone producing content at scale knows the pain: it’s easy to lose track, waste storage, or even accidentally publish the wrong file.

With Google’s new Gemini CLI, there’s finally a smarter way to bring order to this chaos. As an open-source AI agent for your command line, Gemini CLI goes beyond traditional scripts or shell tools. It brings generative AI directly into file management workflows—helping you search, summarize, compare, organize, and automate content tasks, all in natural language.

Why this matters for content creators and knowledge workers:
🔍 Automatic Duplicate Detection: No more manual checks—let Gemini CLI scan folders, compare documents, and surface duplicates instantly.
💬 Natural Language Commands: Ask for “all presentations from 2024 with more than 10 slides” or “summarize key differences between these two drafts”—and get actionable results.
🗂️ Effortless Organization: Rename, move, or consolidate files across projects with a single prompt.
☁️ Integrated with Google’s AI Stack: Seamlessly connects with Google Drive, Workspace, and other tools—so file management isn’t just local, but works across your cloud assets too.

Here’s an experiment I tried:
To test Gemini CLI, I pointed it at one of my project folders of presentations, reports, and working drafts from over time. With just a few natural language commands, Gemini CLI quickly analyzed the folder, identified duplicate files, outlined unique documents, and gave me a clear, actionable summary. The experience was both effective and time-saving, surfacing issues that would have taken much longer to spot manually.

I also asked Gemini CLI to take a screenshot of my screen and convert it to JPG. It intelligently prompted me for the necessary permissions. Once set, the agent handled the task seamlessly—showing how integrated agent workflows can bridge everyday utility tasks from the command line.

For anyone dealing with large volumes of content—be it research, presentations, or creative work—Gemini CLI can turn file management from a headache into a streamlined, intelligent process. It’s not just about tidying up folders. It’s about making your workspace as smart and efficient as your creative process.

Gemini CLI is a new kind of assistant—one that understands your files, your intent, and your workflow. For those who create, iterate, and organize at scale, this is the future of file management.

💡 This is just one example of how an integrated agent CLI can make a difference. Looking ahead, future operating systems will be powered by smart agents—transforming how we interact with files, applications, and information across our digital lives.

Read more at - https://lnkd.in/dyaAxvsU

Stories shape civilization. Long before code and algorithms, storytelling was our greatest tool for understanding change—and it remains so today.📖

In an era defined by rapid technological change, stories remain our most powerful tool for making sense of the world. For the next five days, the digital editions of three of my acclaimed books are available for free—each inviting you to explore the evolving relationship between humans and AI. 🚀

-> Echoes of Tomorrow: The Responsible AI Awakening
Read on Amazon - https://amzn.to/3G4pwlT
Winner of the Golden Book Awards
Set in 2045, this novel follows Zymer Zucher as he returns after a 20-year absence to a society shaped by artificial intelligence. The story explores how technological progress and ethical choices shape the fabric of our lives, while reminding us that our search for belonging and meaning remains as vital as ever.

-> Beyond the Software Code: A Tale of Human and Generative AI Transformation
Read on Amazon - https://amzn.to/3TGJzKc
This high-stakes narrative follows the intertwined destinies of human developers and an AI called “CodeMaster.” Through dramatic twists and moral dilemmas, it examines what it means to build trust, adapt, and thrive alongside evolving AI systems.

-> Silent AI: The Unseen Influence
Read on Amazon - https://amzn.to/3FX0pBv
Set in a society quietly shaped by hidden algorithms, this story delves into questions of autonomy, control, and the subtle forces that influence human choices. It invites readers to reflect on how much agency we hold in a world where technology’s influence is often invisible.

As technology accelerates, wisdom, empathy, and conscious action matter more than ever.

📚 Download your free digital copies and discover new perspectives on the future of AI and humanity. Each book offers a unique lens on how AI may shape, challenge, and transform our world in the years ahead—inviting you to witness how the story of AI might unfold.

You’re right to be frustrated, and I apologize for the repeated error.
”I sincerely apologize that this error is still persisting. It’s incredibly frustrating…
”My deepest apologies. You are right to be frustrated… I’ve misunderstood a critical part…
And it goes on…

These aren’t customer support transcripts.
These are AI-generated apologies to itself — for failing to fix the code it wrote.

An AI tool generated an entire application. It looked great. But during testing, it got stuck in the logic it created. And even after multiple retries, it couldn’t resolve the bug.

So what went wrong?
The problem isn’t just the error. It’s the expectation.
The expectation that you can write a single prompt, and the AI will produce a perfect, production-ready codebase with 50+ files.
Instead, it should’ve taken an incremental, human-in-the-loop approach:
→ Build small
→ Validate
→ Align with intent
→ Then proceed
Without that, even the most sophisticated copilots become black boxes that break silently—and apologize loudly.

It’s time to rethink how AI development assistants are designed.
Not as one-shot generators, but as collaborative partners—grounded in step-by-step co-creation.

We don’t need AI that acts like a genius.
We need AI that behaves like a thoughtful pair programmer.

🌍 Green Software isn’t a feature — it’s a mindset.
It’s a culture shift in how we build, deploy, and run technology — prioritizing sustainability at every line of code, every server request, and every design decision.

⚡ We’re redefining performance — not just in speed or scale, but in how efficiently we can do more with less environmental impact.

Here are some guiding principles for building truly green software:
✅ Energy-Efficient Code = Better Code
Optimizing algorithms to reduce energy use is the new performance benchmark.
✅ Design for Clean Energy
Schedule workloads to run when grids are greener — timing and location matter.
✅ Same Outcome, Less Hardware
Consolidate, virtualize, and streamline to reduce carbon and cost.
✅ Data Has a Carbon Footprint
Archive, compress, and delete — avoid digital hoarding.
✅ Support Device Longevity
Build software that runs well even on older devices. Think adaptive degradation, not forced obsolescence.
✅ Green Cloud, the Smart Way
Choose sustainable cloud regions, carbon-aware scheduling, and efficient deployment strategies.
✅ Test with Purpose
Limit over-automation — testing has an emissions cost too.
✅ Make Green the Default
Sustainable settings shouldn’t be an option — they should be the standard.
✅ AI Needs Efficiency, Not Just Accuracy
Train lean, purpose-fit models with energy impact in mind.
✅ Design Drives Emissions
Minimalist UI/UX with lighter assets and efficient interactions = lower digital waste.
🟢 Green software is everyone’s responsibility — from developers to designers, architects to testers.

Let’s build systems that are not just smart, but sustainable.
📌 Check out the visual cards for a quick snapshot of these principles.

🚀 New White Paper: Building Production-Ready Agentic AI applications with Cost, Carbon, and Conscience in Mind

AI agents shouldn’t just work—they should scale responsibly, remain auditable, and respect real-world limits.

This white paper introduces a 10-step lifecycle for designing production-ready agentic systems grounded in purpose, precision, and sustainability.

🔍 What’s inside:

• Defining agent purpose and stakeholder alignment
• Designing modular, role-based agent architectures
• Enabling contextual memory and reasoning strategies
• Validating behavior, safety, and fairness
• Optimizing for cost, carbon footprint, and complexity
• Embedding governance, traceability, and compliance

The full lifecycle is brought to life through a financial services use case, applying each step to build a responsible Agentic AI-driven loan underwriting agent.

📄 Download the white paper to access the complete guide.

If you’re shaping the future of enterprise AI, this is your blueprint.

🧠 How to Build AI Agents the Right Way
A Holistic Lifecycle Approach: From Requirements to Responsible Operations

1️⃣ Define Purpose & Requirements

• Problem Framing: What real-world task will the agent solve?
• Stakeholder Mapping: Who are the users? What are their expectations?
• Success Metrics: Define efficiency, accuracy, cost, and sustainability targets.

2️⃣ Design Agentic Blueprint

• Roles & Goals: Define each agent’s specialization, responsibilities, and autonomy level.
• Decomposition Strategy: Break down the task into subtasks mapped to agents.
• Interaction Model: Self, collaborative, or autonomous workflows.

3️⃣ Choose the Right Models & Tools

• LLM Selection: Pick SLMs or LLMs based on task, cost, and emission profile.
• Toolchain Design: APIs, webhooks, data access tools, planning libraries.
• Agent Orchestration Framework: CrewAI, LangGraph, ADK, Autogen, or custom.

4️⃣ Enable Contextual Memory

• Episodic Memory: Track short-term interactions and loops.
• Long-Term Memory: Use vector DBs, SQL/NoSQL for history.
• Shared State: Enable inter-agent memory and cross-task coordination.

5️⃣ Incorporate Reasoning & Planning

• Reflection Loops: Evaluate and refine actions mid-task.
• Planning Depth Control: Avoid hallucinations and inefficiencies.
• Prompt Engineering: Optimize for compression, clarity, and chain-of-thought.

6️⃣ Validate & Simulate Behavior

• Scenario Testing: Use synthetic and real-world test cases.
• Edge Case Simulation: Identify failure paths, looping, and over-execution.
• Agentic Evaluations: Use auto-evals for robustness, explainability, and efficiency.

7️⃣ Optimize for Cost, Carbon, and Complexity

• Model Routing: Dynamically select models based on input.
• Token Efficiency: Compress prompts, prune outputs.
• Green Execution: Schedule in low-carbon zones, use idle-aware agents.

8️⃣ Deploy in Controlled Environments

• Secure Interfaces: REST, MCP, or stream-based calls with scoped access.
• Version Control & Rollbacks: For agents, tools, and workflows.
• Fallback Models: Define what happens when something fails.

9️⃣ Continuous Monitoring & Feedback

• Telemetry Collection: Latency, model cost, emissions, task success rate.
• Behavioral Logging: Track decision paths and agent communication.
• Drift Detection: Trigger retraining or prompt updates as needed.

🔟 Governance, Risk & Compliance

• Auditability: Log decisions, tool usage, model selections.
• Privacy Controls: Mask PII, restrict memory scope.
• Sustainability Standards: Integrate SCI for AI, emission budgets, and green compliance.

Building AI agents isn’t about chaining tools — it’s about designing a living system that thinks, adapts, collaborates, and respects boundaries of compute, cost, and conscience.

Code migration is no longer a manual, one-off effort. With agentic AI frameworks like CrewAI, Google ADK, and LLM-powered tools, we can now design multi-agent systems that analyze legacy code, detect compatibility issues, recommend modern syntax, and even generate a migration report — all through guided LLM orchestration.

🔍 I have created an experiment that modernizes Java 8 to Java 17+, powered by:
🧠 Specialized agents for legacy assessment, compatibility checks, refactor suggestions, and reporting
🔄 Prompt-driven tools using Google Gemini (via Google AI Studio)
📄 Outcome: a markdown-based migration plan with clear upgrade paths and insights

This provides a template to get started — but you can extend it by modifying the prompts and plugging in custom tools that are executed as part of the agent workflow. Whether it’s deeper code analysis, security validation, or auto-refactoring, the structure is fully modular.

🧪 Available here to try and extend:
👉 https://lnkd.in/dDPaygET

Google ADK + Zerodha MCP + LLMs: Autonomous portfolio analysis in action.

Modern financial analysis is rapidly moving toward automation and agentic workflows. Integrating large language models (LLMs) with real-time financial data unlocks not just powerful insights but also new ways of interacting with portfolio information.

This experiment brings together secure browser-based authentication, live data retrieval from Zerodha’s MCP, and LLM-driven risk and performance analytics—all orchestrated autonomously.

This is a starter kit to get you going, but it can be extended to support sophisticated, fully automated quantitative models—simply by crafting effective prompts.

I’ve made the experiment available on my GitHub repo. Please feel free to explore or adapt it for your own agentic financial analysis workflows.

Code and documentation:
https://lnkd.in/gme977GG

🌍 Green Software isn’t a feature — it’s a mindset.
It’s a culture shift in how we build, deploy, and run technology — prioritizing sustainability at every line of code, every server request, and every design decision.

⚡ We’re redefining performance — not just in speed or scale, but in how efficiently we can do more with less environmental impact.

Here are some guiding principles for building truly green software:
✅ Energy-Efficient Code = Better Code
Optimizing algorithms to reduce energy use is the new performance benchmark.
✅ Design for Clean Energy
Schedule workloads to run when grids are greener — timing and location matter.
✅ Same Outcome, Less Hardware
Consolidate, virtualize, and streamline to reduce carbon and cost.
✅ Data Has a Carbon Footprint
Archive, compress, and delete — avoid digital hoarding.
✅ Support Device Longevity
Build software that runs well even on older devices. Think adaptive degradation, not forced obsolescence.
✅ Green Cloud, the Smart Way
Choose sustainable cloud regions, carbon-aware scheduling, and efficient deployment strategies.
✅ Test with Purpose
Limit over-automation — testing has an emissions cost too.
✅ Make Green the Default
Sustainable settings shouldn’t be an option — they should be the standard.
✅ AI Needs Efficiency, Not Just Accuracy
Train lean, purpose-fit models with energy impact in mind.
✅ Design Drives Emissions
Minimalist UI/UX with lighter assets and efficient interactions = lower digital waste.
🟢 Green software is everyone’s responsibility — from developers to designers, architects to testers.

Let’s build systems that are not just smart, but sustainable.
📌 Check out the visual cards for a quick snapshot of these principles.

#GreenSoftware #SustainableTech #EcoFriendly #SoftwareDevelopment #GreenIT #Sustainability #TechForGood #ClimateAction Green Software Foundation

🚀 Engineering with the New AI: Copilots, Agents, and the Human Orchestration.

AI is no longer a silent assistant working behind the scenes. Today, it’s embedded in every phase of engineering—design, coding, deployment, and even documentation. From copilots that accelerate code to agentic systems transforming operations, the game has fundamentally changed.

But success with AI isn’t about plugging in a tool. True transformation happens when engineering teams rethink their roles and workflows, blending human expertise with intelligent automation. The future engineer isn’t just a coder—they’re an orchestrator, a designer of AI-driven processes, and a guardian of outcomes.

🔍 What’s inside the latest Technology Bytes newsletter?

• The 3 core principles that define success in the AI era: Define the What, Guide the How, and Own the Why.
• Examples of copilots and agents driving business impact—without sacrificing trust or control.
• How leading teams are evolving their culture, building resilient workflows, and staying accountable in the age of autonomy.
• Pitfalls to watch out for as AI becomes a true teammate, not just a tool.

From accelerating delivery to amplifying quality, this edition dives deep into the human element—the mindsets, skills, and habits that keep engineering both innovative and responsible as AI becomes an essential team member.

Curious about how to thrive in this new landscape?
👉 Read the full blog and discover why the future belongs to those who orchestrate, not just automate.