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Welcome toBIPro112 – Expert-Led Edition, your weekly digest on the latest in business intelligence and data innovation.

This week,we’refeaturing an expert perspective fromSagar Lad, Data & AI Solution Architect, who unpacks the fundamentals, enablers, and challenges ofAgentic AI,the next leap toward intelligent, goal-driven systems. Expect a deep dive into how AI is evolving from reactive models to autonomous, adaptive agents.

Alongside this thought leadership, we round up the newest updates shaping the BI landscape:

Looker’s Conversational Analytics API: Bringing natural language analytics into everyday apps

Power BI’s Semantic Model Refresh Templates: Streamlining refresh workflows with guided pipelines

Microsoft Fabric Real-Time Intelligence with Schema Registry: Building schema-driven, reliable event pipelines

As always,we’vecurated these updates to help you stay ahead of the curve in data and BI.Let’sjump in.

AI adoption brings real pressures:

That’s why we built TechLeader Voices by Packt — a newsletter that delivers real-world playbooks, frameworks, and lessons from frontline AI leaders.

Subscribe before Friday and unlock the Executive Insights Pack — including 1 report, 1 case study, and 5 power talks.

Cheers,

Merlyn Shelley

Growth Lead, Packt

Artificial Intelligence has already transformed industries with predictive analytics, natural language understanding, and generative capabilities. But most AI systems today are reactive — they respond to prompts, execute predefined tasks, or generate outputs within bounded contexts. The next evolution is Agentic AI: systems that can act autonomously, pursue goals, adapt to environments, and coordinate with other agents to achieve outcomes with minimal human intervention.

This article explores what Agentic AI is, why it matters, its architectural principles, key enablers, technical challenges, and enterprise applications.

What is Agentic AI?

At its core, Agentic AIrepresentsa shift from stateless, prompt-driven systems (e.g., today’s chatbots and LLMs) to autonomous, goal-oriented agents. An agentic AI system can:

Perceive— Gather information from structured and unstructured sources (APIs, sensors, documents).

Reason— Apply contextual knowledge, logic, and planning todeterminethe best course of action.

Act— Execute tasks, trigger workflows, or interact with digital/physical systems.

Adapt— Learn from feedback, outcomes, and environment changes to improve future performance.

Agentic AI at its Core

Unlike traditional automation or AI models that need constant supervision, agentic systems can plan, prioritize, and execute multi-step tasks independently.

The convergence of several technological trends is accelerating the rise of Agentic AI:

Large Language Models (LLMs) as Reasoning Engines: Modern LLMs can interpret vague instructions, break them into sub-tasks, and suggest solutions.

Tool Augmentation: APIs and plugins extend AI capabilities beyond text generation into search, data retrieval, code execution, and robotic control.

Memory Architectures: Vector databases and knowledge graphs allow agents to store, recall, and refine knowledge over time.

Orchestration Frameworks: Platforms like LangChain, Semantic Kernel, and Microsoft Prompt Flow enable chaining of multiple reasoning steps and tool calls.

Cloud-Native AI Platforms: Services like Azure AI Foundry and AWS Bedrock are simplifying deployment and scaling of multi-agent systems.

This technological maturity makes it possible to design agents that can operate with goal-directed autonomy while still adhering to enterprise safety, governance, and compliance standards.

Architectural Principles of Agentic AI

Agentic AI solutions typically follow a layered architecture:

Perception Layer: Responsible for gathering and interpreting data from the environment. Technologies include sensors, Natural Language Processing (NLP), and Computer Vision to perceive text, images, and speech.

Cognitive Layer: The brain of the system, encompassing reasoning and decision-making. Employs machine learning models, including reinforcement learning, to analyze inputs and predict outcomes.

Action Layer: Executes decisions through physical or digital means. Incorporates feedback loops for self-correction and continuous improvement.

Communication Layer: Enables interaction with users and other systems. Supports multimodal communication (e.g., text, voice, visual) for seamless integration.

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Dive deeper and read the full piece on PacktHub Medium.

🔳How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques?AI-powered search has become the norm since LLMs took off in 2022, with RAG as the backbone behind tools like ChatGPT and Gemini. This piece explores how to build and scale AI search systems, covering RAG basics, speed, uptime, evaluation, and advanced techniques like contextual retrieval, so you can deliver fast, reliable, and smarter search experiences.

🔳Transforming business meetings to get real-time answers to data questions using Amazon Q in QuickSight.Business meetings often stall waiting on dashboards or reports. Amazon Q inQuickSightchanges this by letting teams ask natural language questions and get instant, multi-visual answers. This post shows how to use Amazon Q for real-time insights in meetings, analyzing sales, refining queries, and exploring trends without SQL or dashboards, making decision-making faster and moredata-driven.

🔳Transform your Google Sheets data into powerful analytics with Amazon QuickSight:AmazonQuickSightnow supports Google Sheets as a data source, making BI workflows more accessible and collaborative. This guide walks admins through setup, including enabling AWS Secrets Manager for secure credential storage, configuring permissions, and connecting Sheets toQuickSight. With this integration, teams can analyze, visualize, and share insights directly from Google Sheets inQuickSight,faster, simpler, and more secure.

🔳Committing to Apache Iceberg with our ecosystem partners:AI is pushing data architectures beyond traditional warehouses and lakes. Apache Iceberg has become the open standard for unifying siloed data, enabling interoperability, governance, and real-time analytics across platforms. Google Cloud, alongside partners like Databricks, Snowflake, Confluent,dbt,Fivetran, and Informatica, is doubling down on Iceberg to power openlakehouses, unlocking flexible, secure, and AI-ready data ecosystems without silos.

🔳Understanding Looker’s Conversational Analytics API:Google Cloud has launched the Conversational Analytics API in public preview, bringing natural language data exploration into everyday apps and workflows. Backed by Looker’s semantic layer andBigQuery, the API lets developers embed chat-based analytics that deliver trusted answers, charts, and insights in real time. With agentic architecture, context retrieval, and enterprise security, it makes “chat with your data” a reality.

🔳Schema Registry: Creating type-safe pipelines using Schemas and Eventstreams (Preview):Microsoft Fabric Real-Time Intelligence now includes Schema Registry forEventstreams, giving teams a centralized way to define, manage, and enforce event data structures. By registering schemas, organizations can ensure consistent, high-quality, and predictable pipelines while improving governance and data contracts between producers and consumers. Available in preview, Schema Registry strengthens real-time analytics, automation, and decision-making across Fabric RTI.

🔳Meet Your Healthcare Regulation and Compliance Requirements with Purview Data LossPrevention (DLP) Policies:Healthcare organizations face strict compliance demands around PHI, from HIPAA to regional privacy laws. As teams adopt Microsoft Fabric for analytics and collaboration, Microsoft Purview Data Loss Prevention (DLP) helps protect sensitive data with automated PHI discovery, real-time user guidance, and detailed audit trails. With Purview DLP, healthcare providers can secure patient data,maintaincompliance, and build trust while enabling better outcomes.

🔳Semantic Model Refresh Templates in Power BI (Preview):Power BI’s new Semantic Model Refresh Templates let you orchestrate and automate refresh workflows using Fabric Data pipelines. From scheduled and event-driven refreshes to incremental updates and sequencing multiple models, these templates simplify setup with guided tours and a gallery of common scenarios. You can even add post-refresh alerts, making refresh management more flexible, reliable, and collaborative.

We’ll be back with more soon!