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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 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.

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

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.

🚀 What if your stock portfolio could analyze itself?

I’ve published an experimental project that automates investment portfolio analysis by connecting three powerful technologies: Zerodha KITE MCP, CrewAI, and Large Language Models.

The open-source repository (https://lnkd.in/d_BtqyCy) demonstrates how to build an autonomous system for financial insights.

How It Works:
🤖 Autonomous Agents (CrewAI): A team of AI agents manages the entire workflow, from secure login and data fetching to the final analysis.
🛠️ Seamless Integration (Zerodha MCP): The agents use Zerodha’s Model Control Protocol directly to interact with trading and account functions—no complex API integration needed.
🧠 AI-Powered Analysis (LLMs): Once your holdings are retrieved, a language model performs a deep-dive analysis, highlighting concentration risks, surfacing top/bottom performers, and offering actionable suggestions.

This isn’t just about retrieving data; it’s about creating an “intelligent agent” that provides automated, bespoke portfolio advice. The template is simple to clone and extend for much deeper quantitative and qualitative analysis.

This is a powerful use case for agentic AI in finance, and I’m just scratching the surface.

🔗 Check out the repository and see it in action: https://lnkd.in/d_BtqyCy

I’d love to hear your thoughts. What other financial tasks could be automated with an agentic workflow like this?

In the era of AI, engineering isn’t about stepping back—it’s about stepping up to new responsibility.

To remain relevant and drive real impact, the skills that matter most aren’t about writing every line of code, but about mastering the art of orchestration—knowing what to delegate, what to own, and how to guide intelligent systems with purpose.

As Gen AI, co-pilots, and agentic systems become part of daily work, success comes from knowing what to delegate and what to own.

🔹 Define the What:
Set clear objectives, boundaries, and success criteria. Your role is to articulate goals, design workflows, and ensure intent is understood by both AI and people.
Example: When deploying a co-pilot, you determine the specific coding challenge it should address, select which parts of the codebase are in scope, and establish what a successful solution looks like—such as passing all tests and following team conventions.

🔹 Guide the How:
Shape not just the process, but the AI’s thinking and orchestration—decide when prompts should be open-ended to foster creativity and when they should be precise for control. Guide how different tools, agents, and humans interact. Steer AI in unfamiliar territory, especially with new frameworks or domains the model hasn’t learned yet. Craft prompts, review outputs, and intervene decisively where only human expertise can fill the gap.
Example: In an agentic workflow for document automation, you design how an AI agent extracts data, when to escalate to a human for review, and how to route exceptions. If your co-pilot encounters a new library, you provide detailed guidance and supplement its output to match the organization’s needs.

🔹 Own the Why:
Remain accountable for the results. Validate, interpret, and monitor outcomes—always making sure that every decision supports business value, ethics, and user needs.
Example: After deploying a Gen AI-powered support chatbot, you continuously review user interactions, monitor for environmental impact, safety, fairness, and accuracy, and step in when the system produces unintended or biased responses—ensuring the experience aligns with business standards and ethical guidelines.

In this new landscape, engineering means orchestrating intelligent systems with purpose and responsibility. The future belongs to those who define, guide, and own their impact at every stage.

Fear. Anxiety. Uncertainty.
These feelings have become all too common in engineering as artificial intelligence, Gen AI, and agentic workflows reshape everything we know about building and leading in tech. But real transformation always starts in the mind. A new era demands a new mindset—one that turns fear into curiosity, uncertainty into strategy, and every challenge into an opportunity for growth.

But what if these emotions aren’t roadblocks, but invitations?
Invitations to rethink our skills, evolve our roles, and step into new kinds of influence.

That’s why I wrote The New AI Engineering Mindset: Navigating Uncertainty and Opportunity in the Age of Intelligent Machines—a practical roadmap for turning apprehension into agency.

This book goes beyond the hype. It equips engineers and leaders with frameworks, applied examples, and the Human Stack model—helping you navigate rapid change, harness Gen AI and LLMs responsibly, and lead with clarity in uncertain times. It also covers an in-depth case study on transforming the Software Development Lifecycle (SDLC) using Gen AI and Agentic AI—providing actionable insights for every role across the engineering spectrum.

If you’re ready to move from anxiety to action, and to help define the next era of engineering, I invite you to explore this book and join the conversation.
Inside the book, you’ll find:
🔘 Practical playbooks for thriving as an engineer in the age of AI
🔘 Strategies for overcoming anxiety and adapting to rapid technological change
🔘 A candid look at job disruption, automation, and how to remain indispensable
🔘 The Human Stack model for future-ready roles and leadership
🔘 In-depth frameworks for ethical AI, prompt engineering, and responsible deployment
🔘 A holistic case study on transforming SDLC with Gen AI and Agentic AI
🔘 Tips for building community, fostering collaboration, and supporting each other—because we’re all in this together
Let’s transform uncertainty into opportunity—together.

➡️ Explore “The New AI Engineering Mindset” here: https://amzn.to/3ZQyLwy

What if the future of work isn’t about humans versus AI—but about humans moving up the stack? The newest edition of Technology Bytes explores the eight-layer Human Stack, redefining how people interface with, augment, and elevate intelligent systems.

Discover how roles like Context Engineers, AI System Composers, Behavioral Calibrators, Responsibility Anchors, Trust & Certification Leads, Legal/IP Guardians, Sensemakers, and Visionaries create new domains of value—grounded in human judgment, creativity, and stewardship.

Climbing the Human Stack: where technology ends, true human value begins.

👉 Read the full newsletter and explore the various roles.

Certifying Agentic AI: Rethinking Assurance for Evolving Intelligence

As intelligent systems advance, so must the way we measure their trustworthiness. Traditional certification approaches—built for static code and predictable workflows—no longer fit a world where AI agents reason, adapt, collaborate, and act on context.

Certification for Agentic AI isn’t just a box to check. It’s a new discipline. Reliability can’t be frozen in a single moment or test. Instead, it must be continuously evaluated:
🔹 How does an agent reason and delegate?
🔹 Can it self-correct?
🔹 When should it refrain from acting?
🔹 Does it operate within clearly defined boundaries?
🔹 How does it handle conflicting goals or ambiguous instructions?
🔹 Can it explain its decisions in a way that makes sense to humans?
🔹 Is it capable of recognizing its own limitations—and escalating when necessary?
🔹 How does it maintain trust and transparency when collaborating with other agents or external systems?
🔹 What guardrails exist to prevent emergent or unintended behaviors?
🔹 How is ethical and operational alignment maintained as the agent evolves over time?

Certifying these systems means asking new questions, embracing continuous validation, and evolving our standards alongside the agents themselves.

For a deeper dive into this crucial shift, tune in to my latest episode of the Agentic AI podcast: “Certifying Agentic AI: Rethinking Assurance for Evolving Intelligence.

Link - https://lnkd.in/duXft9sq

🧠 Can Co-Pilots Truly Learn New APIs on the Fly? AI coding assistants—or “Co-Pilots”—have become powerful accelerators for development. But here’s a real-world challenge many developers are now facing: 👉 Ask them to write code using a newly released API or framework, and you often get:

• Outdated references
• Incorrect method signatures
• Fabricated classes that don’t exist Yes—hallucinations in code.

Why does this happen?
Because most Co-Pilots are trained on past data. If the framework is new—or if internal documentation isn’t publicly indexed—these tools fall back on best guesses based on similar patterns. That’s where things go wrong.

🔍 So how do we fix this?

• Augment AI with live documentation lookups
• Integrate retrieval-based agents that pull from up-to-date API specs
• Enable dynamic feedback loops so the model learns what worked, and what failed

We’re entering a phase where the future of developer productivity hinges not just on Co-Pilot capabilities, but on how context-aware and adaptable they are to change.

The next-gen coding assistant shouldn’t just autocomplete—it should continuously learn, adapt, and validate.