Blogs:

Love to share the blog I curated best of my knowledge for my Claude Certified architect

https://claude-architect.dataopslabs.com/

In Person Events:

AWS Summit Bengaluru 2026

Online Events:

Meetup:

• Supercharging Agents with Built-in Tools: Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

Share

The AI Vulnerability Storm Is Here. Is Your Enterprise Ready?

• Claude Mythos achieved a 72% exploit success rate across every major OS and browser — generating 181 working Firefox exploits autonomously, compared to 2 by the previous model. The patch window is no longer weeks. It’s under 20 hours.

• This isn’t a single event — it’s a trajectory. From XBOW topping HackerOne in June 2025, to the first AI-orchestrated state espionage campaign in November, to 500+ open source zero-days in February 2026. Mythos is the acceleration, not the starting gun.

• 13 risks, 11 priority actions, mapped to NIST CSF 2.0, MITRE ATLAS, and OWASP Agentic 2026 — with time horizons from this week to 12 months. This is the Monday morning plan your CISO needs walking into the boardroom.

• Internal applications are the highest-value targets — legacy ERP, shadow AI agents, citizen-coder pipelines. The post breaks down three breach scenarios with specific mitigations, and what a well-hardened enterprise actually looks like in practice.

• Burnout is an operational risk. The briefing from 50+ CISOs is unusually direct: if you don’t add headcount and mandate AI agent adoption before the Glasswing patch wave hits, your most experienced people will be the first casualty.

Innovate

LinkedIn Post — DocLing + AWS Nova Pro: Nutrition Detection & Validation

🚀 Building an AI-powered Nutrition Label Detection & Validation System — here’s what I learned.

Nutrition labels look simple. But try to extract structured, machine-readable data from thousands of them — across scanned PDFs, product images, and mobile photos — and you’ll quickly discover one of the messiest document problems in food tech.

Here’s how I tackled it using DocLing + Amazon Nova Pro, and why this combination is a game-changer. 🧵

GitHub Link - https://github.com/dataopslabs-aws/nutrition-detection-validation

🧩 The Problem

Nutrition labels come in every shape, size, and format imaginable:

• Scanned PDFs with skewed tables

• Product images with varying fonts, layouts, and lighting

• Manufacturer documents with custom structures that don’t follow any standard template

Traditional OCR tools fail here. They can read text but have no understanding of structure — they can’t tell you that “Saturated Fat 2g” belongs to a nutrition facts table, not a marketing tagline. You need something smarter.

🔬 Enter DocLing — Document Intelligence at Scale

DocLing by IBM Research is an open-source document AI library that goes far beyond OCR. It understands document layout — tables, sections, headers, list items — and converts them into structured, queryable formats.

In this project, DocLing runs as a containerized microservice on AWS Fargate, giving us:

✅ Layout-aware parsing — it recognizes the nutrition facts table as a table, not random lines of text ✅ PDF + image ingestion — handles both scanned PDFs and uploaded label images ✅ Structured output — JSON representations of extracted entities ready for downstream processing ✅ Horizontal scalability — Fargate auto-scales based on demand, so a burst of label uploads doesn’t bottleneck the system

The DocLing Fargate service is the backbone of the document understanding pipeline. Without it, everything downstream would be guessing.

👁️ Amazon Nova Pro — Visual Intelligence for What DocLing Can’t See

Some labels can’t be parsed from PDFs — they live only as product photos. That’s where Amazon Nova Pro steps in.

Nova Pro is Amazon’s multimodal foundation model, built for understanding both text and images with state-of-the-art accuracy. In our architecture, it powers the analyze_image_handler — a service that:

🔍 Identifies nutrition panels within complex product imagery (even partial or angled shots) 📊 Extracts key nutrition entities — calories, macros, allergens, serving sizes — directly from visual content ⚡ Integrates with Strands Agents — the extracted data feeds directly into an agentic compliance pipeline

The combination is powerful: DocLing handles the structured document side, Nova Pro handles the raw image side. Together, they cover virtually every format a nutrition label comes in.

🏗️ The Architecture (simplified)

Three AWS CDK stacks keep this clean:

• DocLing Fargate Stack — containerized document processing

• Similarity Engine Stack — vector embeddings + DynamoDB for semantic search

• Amplify Hosting Stack — React frontend

💡 Key Lessons Learned

1. Don’t fight document structure — understand it. OCR alone is a dead end for semi-structured documents like nutrition labels. DocLing’s layout-awareness is what makes extraction actually reliable.

2. Multimodal AI is no longer optional in real-world pipelines. A huge percentage of product data lives in images. Nova Pro bridges the gap between visual content and structured data extraction.

3. Agents make compliance validation tractable. Using Strands Agents to orchestrate the compliance checking pipeline let me define rules declaratively and let the agent handle the edge cases — instead of writing endless if/else logic.

4. Vector search changes what “validation” means. Rather than hard-coding nutrition thresholds, the similarity engine finds semantically related products and flags anomalies. It’s validation that gets smarter with more data.

If you’re working on document AI, food tech, or building intelligent validation pipelines — I’d love to connect and trade notes. Drop a comment or DM me! 👇

#DocLing #AWSNovaPro #GenerativeAI #DocumentAI #FoodTech #MLEngineering #AWS #BuildingInPublic #AmazonBedrock #AIAgents

Elevate

PODCAST : The AI Vulnerability Storm: An Enterprise CISO Readiness Guide

By: Ayyanar Jeyakrishnan

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Blogs:

Love to share the blog I curated best of my knowledge. Recently I passed GenAI Professional Exam.

AJ - AWS Certified Generative AI Developer - Professional (AIP-C01) Exam Handout

Beyond the Chatbot: 5 Crucial Realities of Securing the Agentic AI Frontier

In Person Events:

AWS Summit Bengaluru 2026

Online Events:

Meetup:

• Supercharging Agents with Built-in Tools: Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

Share

Ranjini G | Vijayalakshmi B | Chandika

started their knowledge sharing Journey on

AWS Certified Machine Learning Engineer Associate (MLA-C01) - Session 3 - Focused on Storage

Innovate

🚀 Project Spotlight: Kegel Exercise App

This week, I’d like to highlight an interesting open-source project built by my friend Vidhya — a simple yet meaningful application focused on improving pelvic health through guided exercises.

💡 What is it about?

The Kegel App is designed to help users practice Kegel exercises, which strengthen pelvic floor muscles. These exercises are widely recommended for improving bladder control, post-pregnancy recovery, and overall pelvic health.

✨ Key Features

• Guided Kegel exercise sessions

• Simple and user-friendly interface

• Timer-based exercise guidance

• Focus on building a consistent daily habit

🌍 Why this project stands out

Health-tech apps often focus on complex tracking or expensive subscriptions. This project keeps things minimal, practical, and accessible, making it easier for anyone to start improving their pelvic health with just a few minutes of daily exercise.

It’s a great example of how simple software can solve real-world health problems.

🔗 Explore the Project

You can check out the code and contribute here:
https://github.com/vidhya03/kegel-app

If you are interested in health-tech, mobile apps, or open-source development, this project is worth exploring.

👏 Kudos to Vidhya for building something that combines technology with everyday health improvement.

Elevate

The Neuro-Symbolic Shift: Achieving Deterministic Logic in Financial Oversight

Approach to Problem - https://www.thecore.in/business/indiaais-regtech-challenge-a-deal-only-a-desperate-start-up-would-sign-859465

By: Ayyanar Jeyakrishnan

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Blogs:

Love to share the blog I curated best of my knowledge.

In Person Events:

https://www.meetup.com/awsugblr/events/313253190/

Online Events:

Meetup:

• Supercharging Agents with Built-in Tools: Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

Share

Handson Session - Supercharging Agents with Built-in Tools: Code Interpreter

Happy to share that our DataOps Labs Community Leaders Session 2

Ranjini G | Vijayalakshmi B | Chandika

started their knowledge sharing Journey on

AWS Certified Machine Learning Engineer Associate (MLA-C01) - Session 2

Innovate

Let us Build —

Cease & Desist Document Processing System

Follow this Guide for reference - https://github.com/jayyanar/agentic-ai-training/tree/main/day5/capstone-project/guides

Successful Solution leveraging any AWS technologies will be provide 100$ Credits

🎯 Project Overview

Business Problem

Enterprises receive Cease & Desist requests from customers who want to stop all direct communication. Currently, human agents must manually read scanned PDF documents to determine if each request is legitimate, which is:

• ⏱️ Time-consuming and slow

• 💰 Expensive (requires human review)

• ❌ Error-prone (human fatigue, inconsistency)

• 📈 Not scalable (volume increases over time)

Your Mission

Build an intelligent multi-agent system that automates the classification and processing of Cease & Desist documents, reducing manual effort while maintaining accuracy and compliance.

Elevate

Podcast Title: From Automation to Agentic AI: Solving the Determinism Paradox in Financial Services

By: Ayyanar Jeyakrishnan

This episode explores the strategic shift from traditional rule-based automation to Agentic AI in the financial sector, where autonomous software agents manage complex, multi-step workflows across GRC, AML, and credit risk. It addresses the industry’s core technical paradox: reconciling the probabilistic, non-deterministic nature of large language models (LLMs)—including risks like hallucinations—with the strict regulatory demands for determinism, auditability, and traceability. Leading institutions are resolving this through hybrid architectures that layer automated reasoning engines and knowledge graphs over foundational AI models, grounding decisions in structured, verifiable data to ensure mathematical certainty. Ultimately, this transformation elevates the workforce from routine task execution to high-value cognitive orchestration, building a compounding institutional memory that becomes a sustainable competitive moat.

• Strategic shift → From rule-based automation to Agentic AI

• Core paradox → Probabilistic AI vs. Deterministic regulation

• Solution → Hybrid architecture + reasoning + knowledge graphs

• Impact → Cognitive orchestration and compounding institutional intelligence

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Blogs:

Love to share the blog I curated best of my knowledge.

In Person Events:

https://aws.amazon.com/events/ai_conclave/ai-conclave-agenda/

Online Events:

Meetup:

• Supercharging Agents with Built-in Tools: Code Interpreter & Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

Share

Happy to share that our DataOps Labs Community Leaders

Ranjini G | Vijayalakshmi B | Chandika

started their knowledge sharing Journey on

AWS Certified Machine Learning Engineer Associate (MLA-C01)

Innovate

Invoice Processing Lab Exercise

Repository: https://github.com/jayyanar/evidence-auditor

An end-to-end AI system demonstrating modern document processing technologies for automated invoice classification and validation.

🎯 Lab Objectives

This lab demonstrates the integration of cutting-edge AI technologies:

• LangGraph: Workflow orchestration and state management

• LangChain: Document parsing and text processing

• OpenAI: Vision-based extraction and embeddings

• Pinecone: Vector storage and retrieval

📋 Business Rules & Validation

Invoice Validation Logic:

• ✅ Valid: All required fields present (vendor name, invoice number, date, amount)

• ❌ Invalid: Missing any required field

Validation Process:

1. Extract structured data from PDF using GPT-4 Vision

2. Validate against business rules

3. Classify as Valid/Invalid with detailed rationale

4. Store results with validation metadata

Elevate

Podcast Title:

Consequence-Aware Reasoning: The Future of Household Robotics

By: Ayyanar Jeyakrishnan

• The Move from Reactive World Models to Deliberative Consequence Models: Current household AI is “short-sighted”, relying on reactive world models that only predict immediate state changes, such as where an object moves. The proposed Consequence-Embedded Reasoning Model (CERM) introduces a dedicated “Consequence Model” that explicitly forecasts long-term outcomes—including physical, social, and safety-related effects—over extended horizons. This architecture treats consequence evaluation as a first-class part of the planning process, allowing the robot to “imagine” and reject risky actions before they are executed.

• A Multi-Dimensional Evaluation Framework for Robot “Meaning”: While traditional models predict what happens next, consequence models capture what it means through four distinct lenses:

  • Physical: Predicting breakage, material stability, or spills.

  • Social: Respecting privacy, cultural norms, and human comfort.

  • Safety: Proactively avoiding injury, contamination, or fire risks.

• Task-Functional: Ensuring current actions do not block future necessary steps in a task chain. This holistic approach allows a robot to realize, for instance, that while stacking a plate on a glass “clears the table,” it violates physical stability norms.

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Must Read for all Builders in IT Space.

Happy to share my notes on AWS re:Invent 2025 announcement.

https://blog.dataopslabs.com/50-new-announcements-on-reinvent-2025

Online Events:

Meetup: Serverless Media Processing with AWS CDK

• Supercharging Agents with Built-in Tools: Code Interpreter & Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

Share

Happy to share my first reinvent as speaker - Truly a proud moment and more happy that I shared with fellow AI Heroes

AWS re:Invent 2025 - Anti-Money Laundering Multi-agent Orchestration with AWS Strands (DEV326)

Innovate

A Comprehensive Learning Guide

This notebook demonstrates a sophisticated Anti-Money Laundering (AML) detection system using 8 specialized agents powered by OpenAI GPT-4 and LangGraph.

🎯 Learning Objectives

By the end of this notebook, you will understand:

• How AI agents can be specialized for different AML tasks

• Why Large Language Models (LLMs) are crucial for financial crime detection

• How to build a sequential agent workflow using LangGraph

• Real-world AML risk scoring and decision-making processes

  Repository: https://github.com/jayyanar/aml-detection-langgraph/blob/main/AML_Detection_System.ipynb

Elevate

Podcast Title:

Neuro-Symbolic AI: The Path to Deterministic Enterprise Automation

By: Ayyanar Jeyakrishnan

Happy to share my learning about NeuroSymbolic AI.

• The Enterprise Requirement for Determinism: Highly regulated sectors like Banking, Financial Services, and Insurance (BFSI) and Healthcare require agentic systems to deliver deterministic, auditable, and logically sound outcomes to automate mundane tasks governed by Standard Operating Procedures (SOPs).

• Conflict with Neural AI: Modern AI agents, based on the Neural/Generative lineage (such as LLMs), rely on stochastic generation and emergent reasoning, which results in unpredictable and sometimes opaque outputs, fundamentally conflicting with the need for verifiable compliance in high-stakes environments.

• Neuro-Symbolic AI as the Bridge: Neuro-Symbolic (NeSy) AI is a hybrid methodology that integrates the pattern recognition and adaptability of neural networks with the explainability, logical inference, and formal reasoning of symbolic AI to create robust cognitive systems.

• Achieving Auditable Execution: Symbolic reasoning provides the core mechanism for enforcing determinism through algorithmic planning and explicit logic, thereby transforming the opaque nature of neural models into transparent, auditable systems.

• Mechanism for Compliance: Symbolic components, often encoded through models like Logic Tensor Networks (LTNs) or specialized languages like Scallop, serve as a white-box safety gate, applying logical constraints directly to the system’s output to guarantee adherence to defined rules.

• Transparency and Traceability: The use of formal reasoning allows NeSy models to generate explicit, per-decision proof artifacts or traceable reasoning routes, ensuring that decisions are clinically or legally explicable and verifiable for audit purposes.

• Safety in Healthcare: In medical contexts, the symbolic layer is essential for encoding medical guidelines or diagnostic pathways, ensuring AI recommendations are clinically explicable and accurate, while neural networks manage the analysis of complex patient data (e.g., imaging).

• Constraint in Finance and Legal: For domains like finance, where neural networks analyze vast, unstructured data, they are often augmented by symbolic-like mechanisms such as Retrieval-Augmented Generation (RAG) to ground stochastic outputs in verified data, thereby mitigating hallucination and ensuring compliance in critical tasks like fraud detection or contract review.

• Real-Time Safety in Robotics: In safety-critical applications like Unmanned Aerial Vehicles (UAVs), hybrid agents use neural networks for real-time perception (e.g., detecting obstacles) while relying on symbolic rules to enforce safety-critical actions (e.g., selecting a safe-landing zone based on geometric and safety constraints), demonstrating reliable real-time decision-making.

• The Strategic Future is Hybrid: The most promising future direction for Agentic AI is the strategic integration of the two architectural lineages (neuro-symbolic integration) to produce high-performance systems that successfully balance the adaptability of neural components with the reliability and verifiability of symbolic components.

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Join us at re:Invent 2025:

Online Events:

Meetup: Serverless Media Processing with AWS CDK

Meetup: Serverless E-commerce Order Processing System (Python)

• Supercharging Agents with Built-in Tools: Code Interpreter & Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

Share

Building Context-Aware AI Agents in Bedrock AgentCore Memory

Innovate

🧠 Market Trends Analysis & Context Engineering for Multi-Agent Workflows

This repository contains comprehensive materials for two advanced AI agent presentations focusing on context engineering, AgentCore Memory, and multi-agent workflow orchestration with production-ready implementations.

• Repository: Market_Trends_Agent_Working.ipynb

  

Elevate

Podcast Title: Autonomous Enterprise and the Agentic Workflow Paradigm
By: Ayyanar Jeyakrishnan

Join me as we explore : A fundamental shift is underway in enterprise operations, moving from rigid, predefined automation to adaptive, goal-driven execution. While Large Language Models (LLMs) possess significant reasoning capabilities, they falter when faced with complex, multi-step tasks, primarily due to challenges in managing state changes and long-term planning. The solution is the Agentic Workflow, a paradigm that embeds procedural knowledge—often structured as Hierarchical Task Networks (HTNs)—to create autonomous digital workers capable of planning, acting, and verifying tasks with high reliability.

This approach dramatically improves LLM performance on complex tasks by explicitly managing dependencies and logical consistency. Agentic workflows can be deployed across a spectrum, from fully autonomous systems that self-evolve, like Evolver, to Human-in-the-Loop (HITL) models for safety-critical applications, such as the ArachNet system used in research. The successful enterprise-scale adoption of this paradigm hinges on a robust infrastructure, conceptually represented by technologies like AWS Strands for dynamic workflow orchestration and Amazon Bedrock AgentCore for a secure, scalable, and fully-featured autonomous runtime. These foundational layers provide the observability, security, and integration capabilities necessary to deploy production-grade, self-optimizing agents that can solve the most complex operational challenges.

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Learn

Join Amazing In Person Sessions

TechXConf , AI & Cloud Conference 2025 - Saturday, November 1, 2025
9:00 AM to 6:00 PM IST

Use the Coupon Code: TXDataOps25 - https://techxconf.com/

Join us at re:Invent 2025:

Online Events:

• Adding Memory: Building Context-Aware AI Agents in Bedrock AgentCore

• Supercharging Agents with Built-in Tools: Code Interpreter & Browser in Action

• Integrating APIs and Services with AgentCore Gateway

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore

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Going Beyond Basics: Custom Agent Development Without the Starter Toolkit - Wednesday October 15 9.00 PM IST (Today)

Innovate

🧠 Financial Advisor Multi-Agent System — Repository Overview

• Repository: jayyanar/financial-advisor-multiagent

• Concept: A complete example of building a multi-agent AI system for financial advisory — from prototype to production.

• Goal: Demonstrates how multiple AI agents collaborate to analyze markets, create strategies, assess risk, and deliver insights responsibly.

• Tech Stack: Strands Agents SDK, Kiro (spec generation), and AWS Bedrock AgentCore for deployment.

• Focus: Turning an AI concept into a deployable, production-ready system using spec-driven workflows and modular architecture.

🚀 What You Can Learn from This Project

• 1️⃣ End-to-End AI Agent Lifecycle:
  Learn how to go from a manual Jupyter prototype → spec generation → system design → production deployment.

• 2️⃣ Multi-Agent Collaboration:
  See how specialized agents (Market Intelligence, Strategy Architect, Risk Assessor, etc.) interact and coordinate through an orchestrator.

• 3️⃣ Spec-Driven Development:
  Explore how tools like Kiro convert plain-language prompts into structured specs, tasks, and design blueprints — reducing dev time.

• 4️⃣ Production Readiness:
  Understand how to integrate CI/CD pipelines, security validations, runtime configs, logging, and monitoring for reliable deployment.

• 5️⃣ Transparent Architecture & Docs:
  The repo includes clear architecture diagrams, testing modules, and documentation that make it easy to follow and extend.

• 6️⃣ Ethical & Domain Awareness:
  Learn how to build AI systems responsibly — especially in regulated fields like finance — with disclaimers and compliance principles.

I shared that in recent Data/AI Day at Bengaluru on October 9th 2025

Elevate

Podcast Title: Orchestrating Intelligence: Context Engineering Strategies for Multi-Agent Workflow using AgentCore Memory
By: Ayyanar Jeyakrishnan

Join me as we explore how context engineering empowers scalable, intelligent multi-agent systems. Discover strategies that enable agents to share memory, align goals, and coordinate tasks through structured context and semantic reasoning. We’ll move beyond prompt engineering to design collaborative, memory-driven workflows that transform industries like finance, healthcare, and logistics.

Disclaimer “AI Generated Podcast” but content curated by me

Thanks for TRESIDUS for sponsoring this blog

Join Amazing In Person Sessions

🔗 Submit Your Talk Here:
https://forms.gle/5Kra7VTketxKQi7K7

Saturday, November 15, 2025 - 10:00 AM to 1:00 PM IST

TechXConf , AI & Cloud Conference 2025 - Saturday, November 1, 2025
9:00 AM to 6:00 PM IST

Use the Coupon Code: TXDataOps25 - https://techxconf.com/

AWS Student Community Day - Chennai 2025 - Sep 20th, 2025 8:00 AM to 5:00 PM

Online Events:

• Going Beyond Basics: Custom Agent Development Without the Starter Toolkit – Mon, Sep 22, 2025 – 9:00 PM IST

• Adding Memory: Building Context-Aware AI Agents in Bedrock AgentCore – – Mon, Oct 6, 2025 – 9:00 PM IST

• Supercharging Agents with Built-in Tools: Code Interpreter & Browser in Action – Mon, Oct 20, 2025 – 9:00 PM IST

• Integrating APIs and Services with AgentCore Gateway – Mon, Nov 3, 2025 – 9:00 PM IST

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore – Mon, Nov 17, 2025 – 9:00 PM IST

Share

Enjoy the 1 of 6 Session on Bedrock AgentCore

Getting Started with AWS Bedrock AgentCore - Runtime

Innovate

Amazon Aurora transformed traditional relational databases by rearchitecting them for the cloud — decoupling compute and storage, and embedding intelligence into storage. This shift enabled unmatched scalability, availability, and performance for mission-critical applications.

Recently Aurora Celebrated 10 Years at AWS Heroes Summit

My Understanding and Top 10 innovation

• 2014 – Aurora announced at AWS re:Invent

• 2015 – Decoupled Compute and Storage
  → Enables independent scaling of compute and storage, improving elasticity and availability.

• 2015 – 6-Way Replicated Storage Across 3 AZs
  → Segmented storage with 6 replicas across 3 AZs ensures durability and fault tolerance.

• 2015+ – Asynchronous Distributed Storage with Background Tasks
  → Offloads heavy storage tasks like backups and validation to background processes.

• 2017 – "Redo Log is the Database" Architecture
  → Moves redo log processing to the storage layer, removing traditional WAL bottlenecks.

• 2017 – Ultra-Fast Crash Recovery
  → Asynchronous redo application enables instant recovery without full database restart.

• 2018 – Aurora Serverless v1 launched (My first reinvent)

• 2022 – Aurora Serverless and In-Place Scaling
  → Serverless v2 allows millisecond-level autoscaling with live migration and no downtime.

• 2022 – Intelligent Load "Unbalancing" and Live Migration
  → Proactively redistributes workloads and migrates hot instances to prevent performance hits.

• 2022 – Token Bucket Rate Regulation
  → Regulates resource growth to handle bursty workloads during scale-ups.

• 2022 – Aurora Capacity Units (ACUs)
  → Abstracts CPU, memory, and I/O into ACUs for simple, usage-based scaling.

• 2024 – Aurora DSQL and predictive resource control announced

• 2024–2025 – Global Serverless & ML-Driven Future
  → All Aurora offerings become serverless by default, with predictive and ML-driven optimizations.

Elevate

Feel free to read through this amazing blog, I read recently. Must read to understand an extensive overview of Agentic AI, detailing its core concept as intelligent personal assistants powered by Large Language Models (LLMs) that can act autonomously on a user's behalf. It explores key scientific and technical challenges in this emerging field, such as the need for agents to communicate in a standardised "embedding space" rather than human language, the complexities of context sharing and privacy in agent-to-agent interactions, and the application of behavioral economics and game theory to model agent negotiations. The text also addresses the long-standing challenge of instilling common sense, including subjective human preferences, into AI systems. Additionally, the source showcases numerous career opportunities for applied scientists in various AI-related domains within Amazon, underscoring the company's commitment to advancing these technologies.

Disclaimer “AI Generated Podcast”

Podcast on this Topic

• Scientific frontiers of agentic AI

Thanks for TRESIDUS for sponsoring this blog

Join Amazing In Person Sessions

🔗 Submit Your Talk Here:
https://forms.gle/5Kra7VTketxKQi7K7

Saturday, November 15, 2025 - 10:00 AM to 1:00 PM IST

Online Events:

• Getting Started with AWS Bedrock AgentCore: Building Your First Intelligent Agent – Mon, Aug 25, 2025 – 9:00 PM IST

• Going Beyond Basics: Custom Agent Development Without the Starter Toolkit – Mon, Sep 8, 2025 – 9:00 PM IST

• Adding Memory: Building Context-Aware AI Agents in Bedrock AgentCore – Mon, Sep 22, 2025 – 9:00 PM IST

• Supercharging Agents with Built-in Tools: Code Interpreter & Browser in Action – Mon, Oct 6, 2025 – 9:00 PM IST

• Integrating APIs and Services with AgentCore Gateway – Mon, Oct 20, 2025 – 9:00 PM IST

• Production-Ready AI: Observability, Scaling, and Best Practices in AgentCore – Mon, Nov 3, 2025 – 9:00 PM IST

Share

Enjoy the amazing session from Veera - AWS Community Builder

Ride-Sharing System Design Apps Like Uber & Lyft - System Design Series - Part 7

Chat App System Design Scalable Architecture - Part 8

Innovate

We want to share https://socials.dataopslabs.com/

Helps to Create, Schedule, and Share Social Content with AI - Feel free to use this tool and share your feedback

Nice thought process and developed by Aadhitya - AWS Community Builder - For the Community

Reach out to info@dataopslabs.com to get Early access to tool

Elevate

Feel free to have a glance at blogs and share your thoughts

1. My Honest Career Roadmap & Health Guide

2. Revolutionising Trade Settlement with Amazon Bedrock AgentCore: Part 1 - The Problem and Agentic AI Solution

3. Revolutionizing Trade Settlement with Amazon Bedrock AgentCore: Part 2 - Technical Deep Dive and Implementation

Thanks for TRESIDUS for sponsoring this blog

Join Amazing In Person Sessions

1) AWS Gen AI Loft Bengaluru

2. AWS Builders Online Series - July 31 - 2025

Online Events:

Ride-Sharing System Design Apps Like Uber & Lyft - System Design Series - Part 7

Chat App System Design Scalable Architecture - Part 8

Share

Enjoy the amazing session from Veera

Build a Scalable URL Shortener Service | System Design Series - Part 5

How to build the Social Media News Feed - System Design Series - Part 6

Innovate

As we mentioned in Previous Newsletter - We build a innovative tool - Focus on Context Engineering - Read this blog for more informaiton.

Thanks to Aadhityaa (Aadhi) SB to put together nice UI and working solution - We demonstrated the same in recent GenAI Loft Community Mixture 2025.

Set your Task

PromptWeaver Understand the Context and Create a MultiAgent Orchestration and Also provide bootstrap AWS Strands Code.

Reach out to info@dataopslabs.com to get Early access to tool

Elevate

Feel free to have a glance at blogs and share your thoughts

• In this blog we show how to build a real‑time AML detection system on AWS using Ambient AI and Strands to enhance financial crime monitoring and compliance efficiency.

• They extend the setup with generative AI models on AWS to reduce false positives and deliver smarter, more precise AML alerts in real time.

Join Amazing In Person Sessions

1) AWS User Group Bengaluru June meet-up editon: Data and Analytics -

Online Events:

• Deploying a Flask Application to AWS Fargate with Amazon Q CLI

Share

Join offline meetups

Build a Scalable URL Shortener Service | System Design Series - Part 5

How to build the Social Media News Feed - System Design Series - Part 6

Innovate

PromptWeaver is a full-stack web application designed to help users break down complex prompts into multiple specialized agents following Anthropic's best practices. The platform addresses a key challenge in prompt engineering: managing complex tasks that require multiple specialized agents rather than a single monolithic prompt.

Core Value Proposition

• Simplifies Multi-Agent Design: Transforms lengthy, complex prompts (≥250 words) into structured, specialized agent prompts

• Follows Anthropic Best Practices: Each generated agent includes role, behavior guidelines, constraints, and examples

• Enables Collaboration: Provides versioning, attribution, and forking capabilities for community improvement

• Visualizes Agent Orchestration: Generates flow diagrams showing how agents should interact and exchange data

Elevate

Feel free to have a glance at blogs and share your thoughts

AWS Strands SDK Masterclass: Introduction to Building AI Agents

AWS Strands SDK Masterclass: Models and Model Providers

Join Amazing In Person Sessions

1) Be Part of AWS Community Day Bengaluru -

https://acd.awsugblr.in/

Workshops were Announced. Pick anyone of your choice - Register

https://acd.awsugblr.in/ to be get benefit out of workshop

Online Events:

• Deploying a Flask Application to AWS Fargate with Amazon Q CLI

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Topic: Building a Full-Stack Budget Tracker Application with Amazon Q CLI

Innovate

What is Amazon Nova-ACT?

Amazon Nova-ACT is an AI-driven web automation tool that lets users describe tasks in natural language. It translates those instructions into precise browser actions and executes them on live websites. Instead of writing code like driver.find_element_by_xpath(...), you can say “log in and show me my recent orders.”

This turns web automation from a developer’s headache into a user-friendly, AI-powered assistant.

How It Works

Nova-ACT runs in your Python environment and leverages a local or headless browser session. It uses the following process:

1. Interpret natural language commands

2. Translate them into actionable steps

3. Execute those steps in a browser

4. Observe results and iterate automatically

5. Complete the task end-to-end

Behind the scenes, it’s using AI to bridge the gap between what you mean and what needs to happen in the browser.

Usecase worked for me : Retrieving Uber Trip Receipts

Let’s say you want to download the receipt for your most recent Uber ride. Doing this manually means logging into Uber, navigating to trip history, scrolling through, and clicking "View Receipt." It’s mundane and repetitive.

import fire  # type: ignore

from nova_act import NovaAct


def main(user_data_dir: str, headless: bool = False) -> None:
    with NovaAct(
        starting_page="https://riders.uber.com/trips",
        user_data_dir=user_data_dir,   # <-- Correct: Use the parameter passed!
        headless=headless,
        clone_user_data_dir=True,
    ) as nova:
        nova.act("I am already logged into my Uber account, proceed directly to trip history")
        nova.act("identify the most recent trip charged in US Dollars")
        nova.act("View the receipt for trip")


if __name__ == "__main__":
    fire.Fire(main)

With Nova-ACT, this process is automated using a few natural language instructions:

This script:

• Uses your existing Chrome profile (so you're already logged in)

• Opens the Uber trip history page

• Locates your most recent trip charged in USD

• Opens the receipt page automatically

Stay tuned on my blog for end-to-end working solution with demo -

https://blog.dataopslabs.com/

Elevate

Feel free to have a glance at blogs and share your thoughts

• Building a Full-Stack Budget Tracker Application with Amazon Q CLI: An Iterative Approach

• Deploying a Flask Application to AWS Fargate with Amazon Q: A Journey of "No-Touch Coding"

Join Amazing In Person Sessions

1) Be Part of AWS Community Day Bengaluru -

https://acd.awsugblr.in/

Online Events:

• Building a Full-Stack Budget Tracker Application with Amazon Q CLI

• Deploying a Flask Application to AWS Fargate with Amazon Q CLI

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The AWS Community Day Bengaluru 2025 Blogathon is an exciting competition designed to encourage AWS enthusiasts, developers, architects, and students to showcase their expertise by writing insightful blog articles. The Blogathon aims to highlight real-world AWS implementations, innovative solutions, and measurable impacts across various domains, including DevOps, FinOps, Big Data, Security, and more.

Topic: Refactor SOAP to REST with Amazon Q Developer Agents | Java 1.8 Modernization Demo

Topic: Deep Dive: Next-Gen Amazon SageMaker -Unified Platform for Data, Analytics,AI

Topic: AWS System Design Series: Introduction

Topic: System Design Key Concepts Explained

Topic: Basic Components in System Design

Topic: AWS Core Services for System Design

Innovate

Navigating AI Protocols: MCP vs. A2A

As artificial intelligence (AI) continues to evolve, the need for standardized communication protocols becomes paramount. Two such protocols, the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) Protocol, have emerged to address different facets of AI interactions. Understanding their distinct functionalities and applications is crucial, especially in complex domains like Compliance AI.​

Challenges in Multi-Agentic AI Without MCP and A2A

Understanding MCP and A2A

Model Context Protocol (MCP)

Developed by Anthropic, MCP is an open standard designed to connect AI models with external data sources, tools, and systems. It enables models to access context beyond their training data, enhancing their capabilities to perform specific tasks by integrating real-time information from various sources. ​

Agent-to-Agent (A2A) Protocol

Introduced by Google, the A2A Protocol facilitates communication and collaboration between multiple autonomous AI agents. It provides a standardized way for agents to discover each other, exchange messages, and work together to accomplish tasks, promoting interoperability across diverse AI systems.

Integrating MCP and A2A in “Compliance Reviewer” Agent

By leveraging MCP, AI models can access and interpret vast amounts of regulatory data, ensuring that compliance checks are thorough and up-to-date. Simultaneously, A2A enables different AI agents, such as those handling legal, financial, and operational compliance, to communicate and coordinate effectively. This integration ensures a holistic approach to compliance, reducing the risk of oversight and enhancing organizational efficiency.

Elevate

Feel free to have a glance at blogs and share your thoughts

• About Prompt to Action

Prompt to Action - Large Action Models-I

Prompt to Action - Large Action Models-II

• Something Old But really valuable

Fine-Tuning Tomcat 8 JDBC Connection Pool

• Vibecoding of this week

Migrating a Complex Java 8 Application to Java 17 Using GitHub Copilot

Join Amazing In Person Sessions

1) Be Part of AWS Community Day Bengaluru -

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Online Events:

• Deep Dive: Next-Gen Amazon SageMaker -Unified Platform for Data, Analytics,AI - Monday, March 24, 2025 - 9:00 PM to 09:45 PM IST

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The AWS Community Day Bengaluru 2025 Blogathon is an exciting competition designed to encourage AWS enthusiasts, developers, architects, and students to showcase their expertise by writing insightful blog articles. The Blogathon aims to highlight real-world AWS implementations, innovative solutions, and measurable impacts across various domains, including DevOps, FinOps, Big Data, Security, and more.

Topic: Hands-On: Build LangGraph Apps & Run LLAMA & DeepSeek R1 Locally with Ollama

Innovate

Hybrid RAG: Combining Knowledge Graphs and Vector Retrieval for Enhanced Information Extraction

This whitepaper introduces HybridRAG, a novel approach to improve information extraction from unstructured text data, particularly in the finance domain. The authors, from BlackRock and NVIDIA, address the limitations of traditional Retrieval-Augmented Generation (RAG) techniques, especially when dealing with domain-specific terminology and complex document formats common in financial documents like earnings call transcripts.

Problem Statement

• Large Language Models (LLMs) struggle with extracting intricate information from unstructured financial text data, even with existing RAG (VectorRAG) techniques.

• Financial documents contain domain-specific language, varied data formats, and unique contextual relationships that general-purpose LLMs find difficult to handle.

• Traditional VectorRAG methods, which rely on paragraph-level chunking, can miss critical contextual information due to the hierarchical structure of financial statements.

Proposed Solution: HybridRAG

The paper proposes HybridRAG, a combination of two RAG approaches:

1. VectorRAG: Traditional RAG that uses vector databases for information retrieval based on semantic similarity.

2. GraphRAG: RAG that leverages Knowledge Graphs (KGs) to represent entities and relationships within the financial documents.

HybridRAG aims to leverage the strengths of both methods to achieve more accurate and contextually relevant answers to questions about financial documents.

Methodology

The paper outlines the following key steps in their HybridRAG approach:

1. VectorRAG Implementation: Standard RAG process involving chunking documents, creating embeddings, storing them in a vector database, and retrieving relevant chunks based on query similarity. They explicitly incorporate document metadata to improve performance.

2. Knowledge Graph Construction: A detailed process for building KGs from unstructured text data, semistructure and structure dataset with relationship involving:

   • Knowledge Extraction: Identifying entities and relationships from text using NLP techniques, including entity recognition, relationship extraction, and coreference resolution. A two-tiered LLM chain is used for content refinement and information extraction

   • Knowledge Improvement: Enhancing the quality of the KG by KG completion and fusion.

3. GraphRAG Implementation: Using the KG to retrieve relevant nodes (entities) and edges (relationships) related to a user query.

Contributions

• HybridRAG Approach: The main contribution is the introduction and evaluation of HybridRAG, which combines VectorRAG and GraphRAG to improve information extraction from financial documents.

• Evaluation on Financial Data: The paper uses a novel ground-truth Q&A dataset extracted from financial call transcripts of companies in the Nifty-50 index (Indian stock market index).

• Demonstrated Improvement: The results show that HybridRAG outperforms both VectorRAG and GraphRAG individually in terms of retrieval accuracy and answer generation.

Significance

The HybridRAG approach has the potential to significantly improve the accuracy and efficiency of information extraction from complex financial documents. This can lead to better-informed investment decisions, risk management, and overall financial analysis. The technique's applicability extends beyond the financial domain to any area dealing with complex, unstructured data.

Elevate

Feel free to have a glance at blogs and share your thoughts

• Amazing blog by Rohith Gowtham G - Student at The National Institute of Engineering | AWS Certified Cloud Practitioner

Building a serverless Clickstream Analytics Pipeline on AWS

• Based on my Understanding on HybridRAG - I created a blog for small usecase

Building a HybridRAG System for Financial Document Analysis: An End-to-End Guide

Join Amazing In Person Sessions

1) AWS Fintech Forum 2025 - Tuesday 25 February, 2025 9:00 – 5:00 PM IST Bengaluru

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• Run LLAMA and Deepseek R1 Model Locally Leveraging Ollama – Hands-On Session - Monday, February 24, 2025 - 9:00 PM to 9:30 PM IST

• Deep Dive: Next-Gen Amazon SageMaker -Unified Platform for Data, Analytics,AI - Monday, March 17, 2025 - 9:00 PM to 09:45 PM IST

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Topic: Hands-On with Deepseek R1 Model on AWS Bedrock

Topic: Revolutionizing Complex Applications with Amazon Bedrock Knowledgebase - GraphRAG

Topic: Revolutionizing Complex Applications with Amazon Bedrock Knowledgebase - Structure Data

Innovate

I was reading more Agentic RAG Graph vs Agentic RAG lately. I will share quick summary for benefit of our beloved newsletter readers.

Agentic RAG: Step-by-Step Flow

Example Use Case: A customer support chatbot answering a query about changing a shipping address.

1. Query Reception:

   • Input: The user asks, "Can I change my shipping address for my order?"

   • Agent Role: A central coordinating agent receives and logs the query.

2. Query Transformation & Understanding:

   • Processing: The agent reformulates the query if needed, clarifying key details (e.g., order status, shipping policies).

   • Purpose: Ensures the query is precise for semantic matching.

3. Initial Vector Retrieval:

   • Action: The agent sends the refined query to a vector database to retrieve semantically similar documents.

   • Data Retrieved: Past customer queries, order status records, shipping policy documents, etc.

4. Iterative Reasoning and Reflection:

   • Process: The agent reviews the retrieved documents and performs self-reflection (possibly re-ranking results or seeking additional context).

   • Outcome: It selects the most relevant pieces of information for response synthesis.

5. Answer Generation:

   • Synthesis: The agent uses the integrated context to generate a response, refining its output through iterative feedback if necessary.

   • Output Example: "Your order is currently in transit. Although changes cannot be applied immediately, you can update your address for future orders or contact our support for urgent modifications."

Agentic Graph RAG: Step-by-Step Flow

Example Use Case: A legal research assistant generating a brief on precedents for contract breach cases in New York.

1. Query Reception:

   • Input: The user asks, "Find precedents related to contract breaches in New York that cite case XYZ."

   • Agent Role: A central agent receives the query and initiates the retrieval process.

2. Query Transformation & Terminology Extraction:

   • Processing: A dedicated terminology agent transforms the query into precise legal terms (e.g., “contract breach,” “jurisdiction: New York,” “citing case XYZ”).

   • Purpose: Ensures consistency in searching structured legal databases.

3. Initial Vector-Based Retrieval:

   • Action: The system performs a vector search to retrieve relevant legal documents or cases that match the transformed query.

   • Data Retrieved: A set of candidate cases, legal opinions, and citations.

4. Graph-Based Traversal:

   • Action: The system uses a knowledge graph that models legal citations, relationships, and dependencies.

   • Process:

     • It identifies nodes (e.g., specific cases or legal principles) from the vector retrieval.

     • It then traverses edges in the graph to fetch linked cases (e.g., cases cited by or citing the initially retrieved cases).

5. Dependency Aggregation & Multi-Hop Reasoning:

   • Process: The agent aggregates the initial documents with their related nodes (precedents, citing relationships, relevant legal principles).

   • Outcome: This creates a rich, multi-hop context that reflects the underlying structure of legal knowledge.

6. Iterative Synthesis & Refinement:

   • Synthesis: The agent iteratively refines its understanding by merging the graph-derived context with the vector-retrieved content.

   • Feedback Loop: It may adjust weights or re-rank relationships to ensure all critical dependencies are captured.

7. Answer Generation:

   • Output: The final response is a detailed legal brief that includes a coherent chain of related cases and citations.

   • Output Example: "Based on the analysis, case XYZ has been cited by several precedents in New York. For instance, case ABC and case DEF both reference XYZ, emphasizing similar contractual breach issues. These precedents establish a clear trend in judicial reasoning for contract breaches in this jurisdiction.

Summary:

• Agentic RAG is best when dealing with general, unstructured queries where semantic similarity is enough. It follows a straightforward cycle of retrieval, reflection, and generation.

• Agentic Graph RAG is preferred when data contains structured, interdependent relationships. By combining vector retrieval with graph traversal, it ensures multi-hop dependencies are captured, making it ideal for complex domains like legal research.

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Feel free to have a glance at blogs and share your thoughts

Leveraging Amazon Bedrock Knowledge Base with Amazon Nova for Complex Financial Use Cases

DeepSeek V3: Overview, Training, and Benchmark Performance

DeepSeek R1: Efficient Reinforcement Learning with GRPO

DeepSeek R1 vs DeepSeek V3

Join Amazing In Person Sessions

1) AWS re:Invent 2024 Recap Edition by AWS User Group Bengaluru

2) AI Conclave Bengaluru Friday, 24 January, 2025 09:00 AM - 5:00 PM Sheraton Grand, Whitefield, Bengaluru

Online Events:

• Revolutionizing Complex Applications with Amazon Bedrock Knowledge Bases - Monday, January 27, 2025 - 9:00 PM to 10:00 PM IST

• Deep Dive: Next-Generation Amazon SageMaker - The Unified Platform for Data, Analytics & AI - Monday, February 10, 2025 - 9:00 PM to 10:00 PM IST

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Innovate

In recent CES 2025, every one exited that NVidia CEO Jensen Huang Announced that Robot is Trillion Dollar Business. I am super excited that how Nvidia Blueprint is going to evolve and make impact in enterprises. I am sharing some small 101 about NVidia Blueprint.

NVIDIA Blueprints (formerly NIM Agent Blueprints) are pre-built, customisable AI workflows that help enterprises build and deploy generative AI applications quickly. Think of them as ready-to-use templates that include sample applications, reference code, customisation documentation, and deployment instructions. They work with NVIDIA's NeMo framework and NIM microservices to help developers create AI applications that can reason, plan, and take action.

Key Features:

1. Customisable: Organizations can modify blueprints using their business data

2. Production-Ready: Can be deployed across accelerated data centers and clouds

3. Data-Driven: Enables continuous refinement based on user feedback

4. Enterprise-Focused: Designed for canonical business use cases

5. Free to Experience: Available for developers to download and test

Most Impactful Use Case - Digital Human for Customer Service: This blueprint stands out because:

1. Advanced Integration: Combines multiple cutting-edge technologies:

   • NVIDIA ACE for animation

   • NVIDIA Omniverse RTX for rendering

   • NVIDIA Audio2Face for facial expressions

   • Nvidia NIM microservices for conversation

2. Business Impact:

   • Creates more engaging customer experiences through humanlike interactions

   • Integrates with existing enterprise RAG systems

   • Can operate 24/7 without human fatigue

   • Scales customer service operations efficiently

The digital human blueprint helps enterprises transform their customer service operations by providing an approachable, animated avatar interface that can handle complex customer interactions while maintaining a human-like presence. This addresses the growing demand for more engaging and efficient customer service solutions while leveraging the latest advances in AI technology.

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In this newsletter, I wish to share the project I am working on Building a Data-Driven Financial News and Content Correction App Using AWS Bedrock Multi-Agent Orchestrator.

Feel free to connect with me for collaboration,

For details blog - Please review

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Snowflake Build Conference at Bengaluru - 19th December 2024

Explore apps, data pipelines, and ML workflows in the age of LLMs and generative AI. - Sponsored by AWS

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• Let us Run LLAMA Model on EKS in Scalable Manner - Monday, December 30, 2024 9:30 PM to 10:15 PM IST

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• Learn AWS CDK with Veera- Session 10, Session 11, Session 12, Session 13

Seamless Multilingual Support with AWS and Nvidia Megatron NIM by

Innovate

In this newsletter, I wish to share an innovative tool I come across of MCP from Anthropic

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is an open standard that enables AI systems to connect seamlessly to various data sources, such as content repositories, business tools, and application environments. It simplifies integration by replacing custom, fragmented connections with a unified protocol, allowing AI models to access real-time and contextual data effectively.

Elevate

Lot of great annoucements from AWS Reinvent 2024

I am sharing my favourites other than all Keynotes.

AWS re:Invent 2024 - Kubernetes

AWS re:Invent 2024 - AI/ML

Join Amazing In Person Sessions

• TechXConf 2024:Asia's Largest AI & Cloud Conference - November 15 & 16

Online Events:

• Seamless Multilingual Support with AWS and Nvidia Megatron NIM - November 20, 2024 9:00 PM to 9:30 PM IST

• Session 10 - Hands-On - Build Event Driven Architecture - AWS CDK - Lambda, SNS - November 21st - 9.00 PM to 9.30 PM IST

• Session 11 - Hands-On - Build Event Driven Architecture - AWS CDK - Eventbridge - November 28th - 9.00 PM to 9.30 PM IST

• Session 12 -WorkFlow Orchestration - AWS Stepfunction and AWS CDK - December 5th - 9.00 PM to 9.30 PM IST

• Session 13 - Building Realtime Streaming Apps Leveraging AWS Kinesis and AWS CDK - December 12th - 9.00 PM to 9.30 PM IST

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• Deep-dive with Amazon Bedrock - Thanks to Dipali

• Learn AWS CDK with Veera - Session 7, Session 8, Session 9

Innovate

In this newsletter, I wish to share an innovative tool I come across of RAGENTIC from Microsoft.

RAGENTIC is an enhanced multi-agent architecture that integrates Retrieval-Augmented Generation (RAG) with autonomous agent networks. This tool aims to elevate the efficiency of agentic AI systems by enabling seamless coordination and decision-making through a multi-agent framework. The architecture includes a Master Agent that oversees specialized agents, each capable of performing distinct tasks such as data retrieval, analysis, and task execution, all supported by contextual decision-making capabilities.

Key features of RAGENTIC include:

• RAG-Powered Contextual Understanding: Enhances the accuracy of outputs by integrating data retrieval to supplement the intrinsic knowledge of the models.

• Autonomous Operation: Minimizes the need for human intervention, allowing the system to execute tasks independently.

• Modular Scalability: Supports the addition and customization of specialized agents for various applications.

• Robust Error Handling: Incorporates mechanisms for effective error management and recovery.

• Real-Time Processing: Ensures timely data processing and decision-making for dynamic applications.

RAGENTIC is designed to solve complex workflows by coordinating agents that each specialize in specific functions like market analysis, user behavior tracking, inventory management, and personalized product recommendations. The agents leverage an Assistant API to securely query databases, perform data processing, and share results, promoting collaborative and cohesive outcomes.

For your information: https://techcommunity.microsoft.com/blog/azure-ai-services-blog/ragentic-rag-enhanced-multi-agent-architecture/4287132

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Feel free to check on below blogs based on Ragentic Architecture

Revolutionizing Travel Booking with Agentic AI Systems

Join Amazing In Person Sessions

• Observability Marathon By-Two Edition - AWS UG Bengaluru x New Relic Oct meet-up by @AWS User Group Bengaluru

• TechXConf 2024:Asia's Largest AI & Cloud Conference - November 15 & 16

Online Events:

• Session 7 - Hands-On - Build Event Driven Architecture - AWS CDK - Lambda and S3 - October 17th - 9.00 PM to 9.30 PM IST

• Session 8 - Hands-On - Build Event Driven Architecture - AWS CDK - Lambda, SNS and S3 - October 24th - 9.00 PM to 9.30 PM IST

• Session 9 - Hands-On - Build Event Driven Architecture - AWS CDK - Lambda, SQS and S3 - November 7th - 9.00 PM to 9.30 PM IST

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• Unlocking AI Potential: Multi-Agent System Design and Deployment with AWS Bedrock with Aadhityaa(Aadhi) and Ayyanar(AJ)

Thanks to Madhu Kumar for Providing me an Opportunity to speak in AWS Community Day Hungary.

• Learn by Hands on Labs with AJ - AWS Bedrock LLM Guardrails - Part 2

• Learn AWS CDK with Veera - Session 4, Session 5, Session 6

Innovate

In this newsletter, I wish to share an innovative tool I come across of Multi-Agent Orchestrator

Swarm GitHub Repository

Introduction

Swarm is an experimental multi-agent orchestration framework developed by OpenAI, designed to coordinate and manage interactions between multiple specialized agents. It facilitates fine-grained control over how agents collaborate to complete complex tasks. By breaking down problems into smaller, specialized tasks handled by different agents, Swarm empowers developers to build robust and flexible solutions that can be tailored to specific domains and workflows.

Key Features:

• Handoff & Routines: Swarm uses a flexible pattern to delegate tasks between agents, enabling effective task sharing and division of labor.

• OpenAI API Integration: Swarm agents can interact using OpenAI’s Chat Completions API, allowing them to collaborate, exchange data, and perform specific functions based on user input.

• Stateless Coordination: Though each agent operates independently, they can exchange task results, creating a fluid workflow without maintaining complex state management.

Swarm is ideal for multi-agent systems, where each agent performs specialized tasks, contributing to a larger solution. It's particularly suited for experimentation, rapid prototyping, and educational purposes, offering developers a new way to explore agentic AI.

Use Cases to Leverage Swarm:

1. Travel Planning Agent:

   • Functionality: A multi-agent system where each agent specializes in booking flights, hotels, and transportation. One agent could calculate the total budget, another finds the best deals, and a third handles booking and confirmation.

   • Benefit: Streamlines travel planning by splitting the task across specialized agents, ensuring efficient coordination for a seamless experience.

2. Customer Support Automation:

   • Functionality: Swarm can be used to build a customer support system where agents address different customer queries—one handles general inquiries, another processes refunds, and a third provides product recommendations.

   • Benefit: Optimizes customer service workflows by delegating specific tasks to specialized agents, improving response times and accuracy.

3. Content Creation Pipeline:

   • Functionality: A system where each agent focuses on a particular stage of content creation—one generates text, another revises it, and another ensures SEO optimization.

   • Benefit: Automates the content creation process, breaking it into specialized tasks for more efficient, high-quality output.

4. Personalized Recommendation System:

   • Functionality: Build a multi-agent recommendation engine where one agent handles user profiling, another fetches relevant content from various sources, and a third delivers personalized suggestions.

   • Benefit: Enhances user experiences through multi-agent collaboration, ensuring personalized and relevant recommendations.

Conclusion

Swarm provides a powerful framework for building multi-agent systems that handle complex tasks through collaboration. Its lightweight and flexible nature make it an ideal solution for developers looking to explore the potential of multi-agent architectures and agentic AI workflows.

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Feel free to check on below blogs

Building an AI-Powered Workflow with AWS Bedrock, Elasticsearch, LangChain, and Pydantic

In this blog post, we’ll explore how to integrate AWS Bedrock for generating AI-based content, store and manage the outputs with Elasticsearch, handle queries using LangChain, and ensure data integrity with Pydantic. This powerful combination offers a scalable solution for working with AI models, validating output, and making results queryable.

Building a Travel Agent with Open AI Swarm - Multi Agent Orchestrator

Swarm is a lightweight, experimental multi-agent orchestration framework by OpenAI that allows you to coordinate and execute tasks between different agents. It's designed for developers who want fine-grained control over agent interactions. In this blog, we'll explore how to build a simple Travel Agent using Swarm that can help users search for the best hotels, flights, provide budget estimates, and even make bookings.

Join Amazing In Person Sessions

• TechXConf 2024:Asia's Largest AI & Cloud Conference - November 15 & 16

Online Events:

• Learn by Hands on Labs with AJ - AWS Bedrock LLM Guardrails - Part 2 - Monday, September 16, 2024 - 9:30 PM to 10:15 PM IST

• AWS Community Day - Hungary 2024 - Friday, October 4, 2024
  12:30 PM to 8:30 PM IST - I am speaking under the Topics - Multi-Agent System Design and Deployment with AWS Bedrock

  Learn AWS CDK with Veera

• Session 4: AWS DynamoDB and CRUD Operation - Thursday, September 12, 2024 - 8:30 PM to 9:00 PM IST

• Session 5 - Build AWS Serverless Application - Thursday, September 19, 2024 - 8:30 PM to 9:00 PM IST

• Session 6 - Secure your Rest APIs using Cognito -Thursday, September 26, 2024 - 8:30 PM to 9:00 PM IST

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We recently concluded an Evaluating Bedrock Large Language Models with Serverless Architecture and Amplify with Aadhityaa(Aadhi) and Ayyanar(AJ)

• Enhance Productivity using Amazon Q for Business and Confluence by Ranjini Ganesan

• Learn AWS CDK with Veera - Session 2 and Session 3

Innovate

In this newsletter, I wish to share an innovative paper I read which will take next leap on Agentic AI

Unleashing the Power of Automated Design of Agentic Systems (ADAS) -

Research Paper Link

Introduction

As artificial intelligence continues to evolve on the path of ANI —> ASI, the need for more sophisticated, adaptable, and efficient systems grows exponentially. Traditional AI development relies heavily on manual design and expert intervention, which can be time-consuming and limited by human creativity and experience. Enter Automated Design of Agentic Systems (ADAS), a groundbreaking approach that leverages meta-agents to automate the design, creation, and optimization of intelligent agents. ADAS represents a significant leap forward in the field of AI, offering the potential to create agents that are not only more powerful but also capable of continuous self-improvement.

What is ADAS?

Automated Design of Agentic Systems (ADAS) is a new research area in artificial intelligence that focuses on automating the design and creation of intelligent agents. Traditional agent design involves manually crafting specific models, prompts, or logic tailored to particular tasks. ADAS, on the other hand, uses a meta-agent to iteratively generate, evaluate, and refine agent designs, exploring a vast space of potential configurations to discover novel and highly effective solutions.

How ADAS Works:

1. Meta-Agent as a Designer: In ADAS, a meta-agent takes on the role of the designer. This meta-agent uses a foundation model, such as LLAMA 3.1, to generate new agents by writing code, creating prompts, or integrating tools that the agent might need to perform a specific task.

2. Iterative Improvement: The generated agents are then evaluated against a set of predefined criteria, such as performance, accuracy, or efficiency. The meta-agent learns from these evaluations, refining its approach in subsequent iterations to create even better agents.

3. Exploring the Design Space: ADAS leverages the Turing completeness of programming languages, allowing the meta-agent to explore an expansive design space that includes different control flows, tool uses, prompts, and agent architectures. This enables the discovery of innovative agent designs that might not be obvious to human designers.

4. Learning from Feedback: By maintaining an archive of successful agent designs, ADAS allows the meta-agent to learn from past successes and failures, using this knowledge to inform the design of future agents. This feedback loop drives continuous improvement and the evolution of more sophisticated agentic systems.

5. Cross-Domain Generalizability: Agents developed through ADAS are often highly adaptable and capable of generalizing across multiple domains. This means that a single agentic design can be applied to various tasks, reducing the need for specialized, task-specific agents.

Why ADAS Matters

ADAS represents a paradigm shift in how intelligent systems are designed and built. By automating the design process, ADAS can significantly reduce the time, cost, and expertise required to develop advanced agents. This opens up new possibilities for innovation, allowing researchers and developers to focus on high-level goals while the meta-agent handles the complexities of design and optimization.

Moreover, ADAS's ability to discover and refine novel agent designs means that it can often outperform traditional, hand-crafted agents. This makes it an invaluable tool in pushing the boundaries of what is possible with AI.

5 Key Use Cases for ADAS

1. Automated Scientific Research:

2. Dynamic Financial Modeling:

3. Complex Process Optimization

4. Adaptive Cybersecurity:

5. Personalized Learning Systems:

As AI continues to evolve, the role of ADAS will become increasingly prominent, driving the next generation of intelligent agents that are not only capable but also continuously improving. Embracing this technology today means being at the forefront of innovation, ready to harness the full potential of AI for tomorrow's challenges.

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Feel free to check on below blogs

AI/ML Career Launchpad: A Step-by-Step Guide to Mastering AI/ML in One Year

I like to share my AI/ML Career Exploration Course! This year-long journey is designed to help you explore and build a career in Artificial Intelligence and Machine Learning. Whether you are starting fresh or enhancing your skills, this course will guide you step-by-step, from the basics to advanced topics, with a clear path and resources.

The 3P Strategy to Ace AWS AI Practitioner Certification

Embarking on a journey to achieve an AWS certification can be both challenging and rewarding. Recently, I completed the AWS Certified AI Practitioner exam, a milestone that represents not just a credential but a testament to my dedication and learning journey. I am excited to share my experience and provide a practical approach that helped me succeed: the 3P Principle of Learning—Prepare, Practice, Plan. This exam is Level 150 - Anyone who practice below steps can achieve it.