Learn - Share - Innovate - Elevate
DataOps Newsletter Volume 24 - December 2025
Learn
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
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