AWS Managed Services

Deploying Agentic AI with MCP and A2A Capability on Amazon EKS

In-Short

CaveatWisdom

Caveat: You’ve built intelligent agents with MCP tools (Post 1) that communicate autonomously via A2A (Post 2). But deploying them without proper orchestration creates disasters: exposed credentials in containers, inability to handle load spikes when 500 RFQs hit simultaneously, no isolation between agents and MCP tools leading to security breaches, and operational nightmares with manual scaling. Ad-hoc deployments work in development but create security vulnerabilities and scalability bottlenecks in production.

Enterprise Procurement Reimagined – How Agentic AI with MCP Tools Transforms B2B Negotiations

In-Short

CaveatWisdom

Caveat: Your AI procurement agent needs real-time data from Logistics, Production, Stores, Finance, and Operations to make informed decisions. But here’s the trap: each enterprise system speaks a different language with proprietary APIs. Without standardized tool protocols, you end up with brittle agents that have hard-coded integrations. Every API change breaks your agent. Every new system means weeks of custom development. Your agent makes suboptimal decisions because it can’t access complete enterprise data.

Industrial Evolution – Helping Engineers to Slash Break-Down Maintenance Time with Gen AI

In-Short

CaveatWisdom

Caveat: If root cause during a machine break-down maintenance is not identified with-in time and proper procedures to fix the problem safely is not followed in a challenging industrial environment then they can lead to lot of production loss and create safety concerns for maintenance personal.

Wisdom: Expert level understanding and huge experience in interpreting the problem in machines is required to troubleshoot and fix machines. By leveraging cutting edge Generative AI and IoT technologies which help in understanding root cause and fix problems, break-down maintenance time can be slashed from days to few hours.

Understanding Industrial Protocols in the Perspective of IoT and Cloud

In-Short

CaveatWisdom

Caveat: To take advantage of latest technologies like Generative AI on Cloud, data is being ingested from different sources into the Cloud, coming to real-time industrial data, it’s important to understand the nature of data and it’s flow from its source on shop floor of the industry to its destination in the cloud.

Wisdom: To understand the nature of data and its flow, we need to understand the protocols involved at different levels of data flow, like Modbus, Profibus, EtherCAT, DNP3, OPC, MQTT, etc.

The Response from Generative AI depends on Our Intelligence more than the Intelligence within It

In-Short

CaveatWisdom

Caveat:

It is easy to type a question and get a response from the Generative AI, however it is important to get the right answer as per the context, because Large Language Models (LLMs) of Generative AI are designed to predict only the next word and they can hallucinate if they don’t get the context right or if they don’t have the required information with-in them.

Below is the screenshot of above example and response from Gen AI model in Amazon Bedrock

Applying Mechanical Sympathy with Built-In Algorithms of Amazon SageMaker

Mechanical Sympathy

The term Mechanical Sympathy was coined by racing driver Jackie Stewart, he said that “You don’t have to be an engineer to be a racing driver, but you do have to have Mechanical Sympathy.” He meant that understanding how a car works makes you a better driver. In case of Machine Learning by understanding when to use a specific algorithm we can get maximum efficiency from the resources we provision in the cloud.

Running Containers on AWS as per Business Requirements and Capabilities

We can run containers with EKS, ECS, Fargate, Lambda, App Runner, Lightsail, OpenShift or on just EC2 instances on AWS Cloud. In this post I will discuss on how to choose the AWS service based on our organization requirements and capabilities.

In-Short

CaveatWisdom

Caveat: Meeting the business objectives and goals can become difficult if we don’t choose the right service based on our requirements and capabilities.

Wisdom:

1. Understand the complexity of your application based on how many microservices and how they interact with each other.
2. Estimate how your application scales based on business.
3. Analyse the skillset and capabilities of your team and how much time you can spend for administration and learning.
4. Understand the policies and priorities of your organization in the long-term.

In-Detail

Interweaving Purpose-Build Databases in the Microservices Architecture

It is best practice to have a separate database for each microservice based on its purpose. In this post we will understand how to analyse the purpose based on a scenario and choose the right database.

In-Short

CaveatWisdom

Caveat: We can easily run into cost overruns if we do not choose the right database and design it properly based on the purpose of our application.

Wisdom:

1. Understand the access patterns (Queries) which you make on our database.
2. Understand how your database storage scales, will it be in terra bytes or petabytes.
3. Analyse what is most important for your application among Consistency, Availability and Partition Tolerance.
4. Choose Purpose-Built databases on AWS cloud based on Application Purpose.

In-Detail

Basics of AWS Cloud Formation Template

Learn the Anatomy of AWS CloudFormation Template

Managed Services for Open-Source Technology on AWS Cloud

In-Short

CaveatWisdom

Caveat: Developing Solutions with Open-Source technologies gives us freedom from licensing and also run them anywhere we want, however it becomes increasingly complex and difficult to scale and manage at high velocities with Open Source.

Wisdom:

We easily can offload the management and scalability to the managed services in the cloud and concentrate more on our required business functionality. This can also save total cost of ownership (TCO) in the long term.