AI Agent Engineering: Building LLM-COT-Powered Intelligent AI Agents (Total Guide)

AI Agent Engineering: Building LLM-COT-Powered Intelligent AI Agents (Total Guide)

In today’s rapidly advancing technological landscape, AI Agent Engineering stands as a critical pillar for the future of intelligent systems. It’s not just another tech trend — it’s the strategic foundation behind creating machines that think, reason, and act with increasing autonomy. Understanding AI agent engineering is important in 2025 is essential for anyone who wants to stay ahead in AI development, business innovation, or future-ready industries.

The future of artificial intelligence (AI) is here, and it’s more dynamic, adaptive, and intelligent than ever before. Powered by Large Language Models (LLMs) and Chain of Thought (CoT) reasoning, today's AI agents are designed to reason, learn, and act with unprecedented levels of sophistication.

This guide is your ultimate resource to understanding the cutting-edge technologies that make up AI agent engineering. From LLMs to CoT reasoning, and retrieval-augmented generation (RAG), we’ll explore how to build intelligent, multi-functional AI agents capable of complex problem-solving, adaptive decision-making, and context-aware interactions.

Autophagy in AI: Destructive vs. Constructive

Self-healing AI refers to artificial intelligence systems that can detect, diagnose, and fix their own issues—without human intervention—just like how our body repairs itself through processes like autophagy. Autophagy AI is a fusion of biological intelligence and machine intelligence

Autophagy, a cellular process that plays a critical role in maintaining the health and functionality of living cells, offers a compelling analogy for artificial intelligence (AI). In biological systems, autophagy refers to the process by which cells break down damaged or malfunctioning components and recycle them into useful building blocks, essentially ‘self-cleaning’ to preserve their overall health and efficiency.

However, when AI systems, and AI agents are allowed to engage in a self-reflective learning process without careful regulation, they can enter cycles of self-improvement that, much like cellular autophagy, can lead to either self-preservation or self-destruction.

AI-Driven PK/PD Modeling: Generative AI, LLMs, and LangChain for Precision Medicine

Pharmacokinetics (PK) and pharmacodynamics (PD) form the foundation of drug development and precision medicine. PK describes how a medicine moves through the body—its absorption, distribution, metabolism, and excretion (ADME)—while PD focuses on the drug’s biological effects and mechanism of action. Precision Medicine (PM) integrates PK/PD modeling in drug development by tailoring treatments based on individual patient variability.

This article explores the cutting-edge applications of AI in PK/PD modeling, with a focus on Generative AI, LLMs, and LangChain in pharmacokinetics, and precision medicine.

At our Compassionate AI Lab, we have explored a diverse range of AI-driven tools and techniques to enhance pharmacokinetic (PK) and pharmacodynamic (PD) modeling for precision health and healing. Traditional PK/PD models rely on differential equations, compartmental analysis, and statistical regression to describe drug absorption, distribution, metabolism, and elimination. However, these methods face significant limitations when dealing with complex biological variability, real-world uncertainty, and sparse patient data.

A multi-agent AI system in precision medicine enhances decision-making, improves treatment precision, and accelerates drug discovery by leveraging the combined strengths of multiple AI agents, achieving outcomes beyond the capabilities of single-agent AI models.

AI Brick Computing: Scalable and Sustainable Green AI

The recent craze for Data Center Free AI stems from growing concerns over the massive energy consumption, carbon footprint, and centralization of AI models reliant on cloud infrastructure. As a result, researchers and industries across the world are shifting fast towards decentralized, edge-based, and sustainable AI solutions that can operate efficiently without data centers, leveraging low-power devices, federated learning, and renewable energy to ensure scalability and environmental responsibility.

This article introduces Mobile AI Brick Computing, a groundbreaking concept that envisions autonomous, portable AI computing units designed for large-scale deployment of Knowledge-Augmented Generation (KAG), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Mixture of Experts (MOE) AI architectures.

The increasing demand for artificial intelligence (AI) applications has led to unprecedented reliance on centralized cloud computing, resulting in high energy consumption, increased latency, and concerns over data privacy. As AI models grow in complexity, the need for sustainable, decentralized, and scalable AI processing has become more evident. One emerging concept aimed at addressing these challenges is AI Mobile Brick Computing—a novel approach that leverages modular, self-sufficient AI processing units capable of running AI workloads without the need for centralized data centers.

AI Mobile Brick Computing envisions autonomous, portable AI computing units, designed for local AI inference, decentralized model training, and renewable-powered AI processing. By moving away from the traditional cloud-AI dependency, these compact AI bricks—which can be embedded in mobile devices, IoT nodes, or edge computing stations—offer a sustainable alternative for real-time AI processing and energy-efficient deployment.

What is an AI Brick?

An AI Brick is a modular, self-sufficient AI processing unit designed to run AI workloads without relying on centralized data centers. It is a portable, energy-efficient, and decentralized AI computing module that can perform inference, training, and distributed learning using low-power hardware, federated learning, and renewable energy sources.

AI Bricks are envisioned as building blocks of decentralized AI ecosystems, capable of running Knowledge-Augmented Generation (KAG), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Mixture of Experts (MOE) architectures at the edge. Unlike traditional AI deployments that depend on cloud-based GPUs, AI Bricks are designed for scalability, mobility, and sustainability, enabling AI applications to function efficiently in off-grid, remote, and distributed environments.

AI Bricks are not just AI processors—they are self-sustaining, intelligent AI nodes capable of operating independently and collaboratively within decentralized AI ecosystems. Their six capabilities—autonomy, decentralized learning, knowledge efficiency, AI collaboration, real-time execution, and energy optimization—make them ideal for next-generation AI deployment.

As the demand for Data Center Free AI grows, AI Bricks provide a scalable, efficient, and sustainable alternative to traditional AI models, enabling AI to be faster, smarter, greener, and compassionate.

Five Steps to Building AI Agents with Higher Vision and Values

Building reliable AI agents with higher values and safety is a challenge. It requires balancing advanced technological innovation with rigorous testing, transparency, and accountability. This article explores five key steps and the top ten ethical principles for developing reliable AI agents with higher values.

Developing AI agents is not merely a technological endeavor; it is an artistic process of harmonizing purpose, integrating human values, intelligence, and adaptability. At our Compassionate AI Lab, the deeper purpose of innovation is to bring clarity, harmony, compassion, and higher values to life. Future AI agents are envisioned not just as tools but as catalysts for transforming society toward greater compassion, care, and better society.

This article explores into the top ten ethical principles and five essential steps for creating an AI agent, blending technical innovation with a vision of enlightened progress.

The vision for future AI agents is not merely one of technological advancement but one of societal evolution, where compassion and intelligence collaborates to create a harmonious and enlightened world.

The 10 Ethical AI Indexes for LLM Data Training and Responsible AI

In this article, we present an exploration of ten indispensable ethical AI indexes that are paramount for the responsible AI development and deployment of Large Language Models (LLMs) through the intricate processes of data training and modeling.

Ethical AI

Within our compassionate AI Lab, we have diligently worked to create a series of AI indexes and measurement criteria with the objective of safeguarding the interests of future generations and empowering humanity. The names of the ten ethical AI indexes are as follows:

Ethical Responsibilities in Large Language AI Models: GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM

Large-language AI models like GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM have changed the field of artificial intelligence in a big way. However, ethical considerations are the biggest challenge for large-language AI models. These models are very good at generating language and have a huge amount of promise to serve humanity. But with a lot of power comes a lot of responsibility, and it's important to look into the social issues that come up when making and using these cutting-edge language models.

Ethical Responsibility in Large Language AI Models

Ethical Responsibility in Large Language AI Models

In this article, we explore the ethical considerations surrounding large language AI models, specifically focusing on notable models like GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM. If not carefully addressed now, the immense power and influence of these types of models can inadvertently promote biases and other chaos in the human society.

By critically examining the ethical implications of large language AI models, we aim to shed light on the importance of addressing these concerns proactively. These models possess the ability to generate vast amounts of text, which can significantly impact society and shape public opinion. However, if not appropriately managed, this power can amplify biases, reinforce stereotypes, and contribute to the spread of misinformation.

Calling for a Compassionate AI Movement: Towards Compassionate Artificial Intelligence

This is a call for a Compassionate AI Movement that advocates and promotes the creation and use of AI systems that put human safety and values like compassion, equity, and the common good first.

Calling for a Compassionate AI Movement

Calling for a Compassionate AI Movement:

Join the Compassionate AI Movement, championing the advancement and implementation of AI systems that place utmost importance on empathy, fairness, and the betterment of society.

"The true measure of AI's greatness lies not in its intelligence alone, but its ability to combine intelligence with compassion." - Sri Amit Ray

The moment has come for a Compassionate AI Movement to reshape the course of AI development and deployment. We can build AI systems that accord with our collective values and contribute to a more compassionate and equitable society by prioritizing safety, empathy, fairness, and social benefit.

From Data-Driven AI to Compassionate AI: Safeguarding Humanity and Empowering Future Generations

In this article, we explore the transition from data-driven AI to compassionate AI and how it holds the key to safeguarding humanity and empowering the next generation. We can unlock the full potential of AI while assuring fairness, encouraging well-being, and creating meaningful human-machine interactions by incorporating empathy, ethics, and societal values into AI systems. By embracing compassionate AI, which incorporates empathy and societal values, we can overcome these limitations, safeguard humanity, and empower future generations.

From Data-Driven AI to Compassion and Value-Driven AI: Safeguarding Humanity and Empowering Future Generations

Safeguarding Humanity and Empowering Future Generations

Recently, artificial intelligence (AI) has made enormous advances, revolutionizing sectors and reshaping the way we live and work. Data-driven AI has been at the forefront of this AI revolution, with its capacity to handle massive volumes of data and extract valuable insights.

However, as we continue to harness the power of AI, we must understand and confront the limitations of a data-only strategy. The transition to compassionate AI has arisen as a necessary and ethical necessity, with the goal of protecting mankind and empowering future generations.

We can unlock the full potential of AI while assuring fairness, encouraging well-being, and creating meaningful human-machine interactions by incorporating empathy, ethics, and societal values into AI systems. By embracing compassionate AI, which incorporates empathy and societal values, we can overcome these limitations, safeguard humanity, and empower future generations.

Artificial intelligence for Climate Change, Biodiversity and Earth System Models

How to make an accurate prediction of climate change and greenhouse carbon emissions with artificial intelligence? We are experimenting with several AI models for climate change and global warming. Sri Amit Ray explains the hybrid system of process-based earth system models and the deep learning networks of artificial intelligence.

As climate change and global warming have become the most urgent issues for human survival, researching ways to improve climate change and earth system models has become of the utmost importance.

Artificial intelligence (AI), specifically deep learning algorithms, has the ability to make decisive interpretations of large amounts of complex data. Moreover, modern machine learning techniques appear as the most effective tool for the analysis and understanding of ocean, earth, ice formations, CO2 emissions, biodiversity, and atmospheric data for situation and target specific dynamic prediction. One of the strengths of machine learning tools such as convolutional neural network is its capacity to combine a variety of methods.

Deep Transfer Learning Models for Biodiversity, Climate Change, and Global Warming

Deep Transfer Learning Models for Biodiversity, Climate Change, and Global Warming

Quantum Machine Learning: The 10 Key Properties

In this article, we discussed the 10 properties and characteristics of hybrid classical-quantum machine learning approaches for our Compassionate AI Lab projects. Quantum computers with the power of machine learning will disrupt every industry. They will change the way we live in this world and the way we fight diseases, care for old people and blind people, invent new medicines and new materials, and solve health, climate and social issues. Similar to the 10 V's of big data we have identified 10 M's of quantum machine learning (QML). These 10 properties of quantum machine learning can be argued, debated and fine-tuned for further refinements.

10 Quantum Machine Learning Properties By Amit Ray

Classical Quantum Hybrid

Hybrid Classical Quantum Machine Learning

The compassionate AI lab is currently developing a hybrid classical-quantum machine learning (HQML) framework - a quantum computing virtual plugin to build a bridge between the available quantum computing facilities with the classical machine learning software like Tensor flow, Scikit-learn, Keras, XGBoost, LightGBM, and cuDNN.

Presently the hybrid classical-quantum machine learning (HQML) framework includes the quantum learning algorithms like: Quantum Neural Networks, Quantum Boltzmann Machine, Quantum Principal Component Analysis, Quantum k-means algorithm, Quantum k-medians algorithm, Quantum Bayesian Networks and Quantum Support Vector Machines.

Five Key Benefits of Quantum Machine Learning

Five Key Benefits of Quantum Machine Learning

Here, Dr. Amit Ray discusses the five key benefits of quantum machine learning.

Quantum machine learning is evolving very fast and gaining enormous momentum due to its huge potential. Quantum machine learning is the key technology for future compassionate artificial intelligence. In our Compassionate AI Lab, we have conducted several experiments on quantum machine learning in the areas of drug-discovery, combating antibiotic resistance bacteria, and multi-omics data integration.

We have realized that in the area of drug design and multi-omics data integration, the power of deep learning is very much restricted in classical computer. Hence, with limited facilities, we have conducted many hybrid classical-quantum machine learning algorithm testing at our Compassionate AI Lab.

Benefits of Quantum Machine Learning

Five Benefits of Quantum Machine Learning

What's Holding Back Machine Learning in Healthcare

What is holding back the large scale implementation of machine learning systems in healthcare and precision medicine? In this article Dr. Amit Ray, explains the key obstacles and challenges of implementing large-scale machine learning systems in healthcare. Dr. Ray argued that lack of deeper integration, incomplete understanding of the underlying molecular processes of disease it is intended to treat, may limit the progress of implementing large-scale machine learning based reliable systems in healthcare. Here, nine obstacles of present day machine learning systems in healthcare are discussed.

What Holding Back Machine Learning in Healthcare

Machine Learning in Healthcare

Recently, machine learning algorithms, especially deep learning has shown impressive performance in many areas of medical science, especially in classifying imaging data in different clinical domains. In academic environment, Deep learning and Reinforcement learning methods of Artificial Intelligence (AI) has shown tremendous success in numerous clinical areas such as: Omics data integration (such as genomics, proteomics or metabolomics), prediction of drug-disease correlation based on gene expression, and finding combinations of drugs that should not be taken together. Deep learning is very successful in predicting cancer outcome based on tumour tissue images. Machine learning are used for medical decision support systems for ICU and critical care. Artificial Intelligence in Healthcare Current Trends discusses the current status of AI in healthcare.

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers

How many qubits are needed to outperform conventional computers? How to protect a quantum computer from the effects of decoherence? And how to design more than 1,000 qubits fault-tolerant large-scale quantum computers? These are the three basic questions we want to deal in this article.

Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing. In this article, we explain the practical issues of designing large-scale quantum computers.

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers

Quantum Computing with Many World Interpretation Scopes and Challenges

The Many-Worlds Interpretation (MWI) of quantum mechanics posits that all possible outcomes of quantum measurements are realized, each in a separate, non-communicating branch of the universe. This interpretation challenges the traditional Copenhagen view, which involves wave function collapse to a single outcome. In the context of quantum computing, MWI offers a framework for understanding quantum parallelism—the ability of quantum computers to process multiple computations simultaneously.

In this article, we explore how MWI aligns with quantum computing's principles, the opportunities it presents, and the challenges we must address to harness its full potential.

Quantum Computing with Many World Interpretation

Many scientists believe that Many Worlds Interpretation (MWI) of quantum mechanics is self-evidently absurd for quantum computing. However, recently, there are many groups of scientists increasingly believing that MWI has the real future in quantum computing, because MWI can provide true quantum parallelism. Here, I briefly discuss the scopes and challenges of MWI for future quantum computing for exploration into the deeper aspects of qubits and quantum computing with MWI.

This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing large-scale compassionate artificial intelligence projects.

Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Balance control in elderly people is one of the key issues of old age. Artificial Intelligence can play a big role to solve this issue. In this research work, we demonstrate the application of machine learning techniques for posture alignments and the control of the body center of mass for disabled people.

Designing automated balance control system for elderly people is one of the key projects of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent projects of AI using deep learning algorithms for automatic balance control of elderly people. He explains how machine learning algorithms can be used to study and improve the dynamical properties of postural stability of elderly people. The project focuses on how image recognition, human-body joint dynamics, and path navigation methods of artificial intelligence can be used to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.

AI for Balance-Control Fall Detection of Elderly People

Compassionate Artificial Intelligence can be used for helping elderly people in many ways. Here, we discuss about one of our recent project of using AI & deep learning techniques for automatic balance control. The machine learning algorithms are used to improve dynamical properties of postural stability. In this project AI based machine learning algorithms are used to find the insights into the person specific postural strategies for older adults in order to adapt to the postural challenges during sleeping, standing, turning and walking. To study the body movement behavior of elderly people accurately, it is necessary to observe and record their movement trajectory and joint movements quantitatively and precisely in three dimensions.

Quantum Computer with Superconductivity at Room Temperature

Quantum Computer with Superconductivity at Room Temperature

Quantum computer with superconductivity at room temperature is going to change the landscape of artificial intelligence. In the earlier article we have discussed quantum computing algorithms for artificial intelligence. In this article we reviewed the implication of superconductivity at room temperature on quantum computation and its impact on artificial intelligence.

Long coherence time (synchronized), low error rate and high scalability are the three prime requirements for quantum computing. To overcome these problems, presently, quantum computer needs complex infrastructure involving high-cooling and ultra-high vacuum. This is to keep atomic movement close to zero and contain the entangled particles, both of which reduce the likelihood of decoherence. The availability of superconductivity at room temperature will provide the quantum jump in quantum computer.

Quantum Computer with Superconductivity at Room Temperature

Quantum Computing Algorithms for Artificial Intelligence

Quantum Computing Algorithms for Artificial Intelligence

Dr. Amit Ray explains the quantum annealing, Quantum Monte Carlo Tree Search, Quantum algorithms for traveling salesman problems, and Quantum algorithms for gradient descent problems in depth.

This tutorial is for the researchers, developers, students and the volunteers of the quantum computing team of the Sri Amit Ray Compassionate AI Lab. Many of our researchers and students asked me to explain the quantum computing algorithms in a very simplistic term. The purpose of this article is to explain that.

Quantum Computing Algorithms for AI By Amit Ray

Earlier we have discussed Spin-orbit Coupling Qubits for Quantum Computing and the foundations of Quantum computing and artificial intelligence. This article is to explain the foundation quantum computing algorithms in depth in a simplistic way. Here we explained the concepts of quantum annealing, Quantum Monte Carlo Tree Search, quantum algorithms for traveling salesman problem and Quantum algorithms for gradient descent problems.

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria - tools, techniques, models, scopes and challenges are discussed. Antibiotic resistance bacteria are one of the key research area of our Compassionate AI Lab. Dr. Amit Ray explains how artificial intelligence can be used in combating these superbugs. Antibiotic resistance bacteria is becoming world’s biggest health crisis. We discussed here multi-agent deep reinforcement learning models for predicting behavior of bacteria and phages in multi-drug environments. We call this model as DeepCombat.

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Antibiotic resistant bacteria are bacteria that are not controlled or killed by antibiotics. They are able to survive and even multiply in the presence of an antibiotic. These bacteria currently kill an estimated 700,000 people globally each year – a death toll which could rise to 10 million a year by 2050 if we don’t act [1]. The main difficulty is that the bacteria are changing fast. They changing faster than we can change the drugs in response.

Artificial intelligence is showing alternative means of fighting these deadly infections and killer bacteria. Multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, thousands of lives lost. Artificial Intelligence for healthcare is progressing at an exponential rate. We are evaluating here, the role of artificial intelligence in fighting these superbugs. Especially, the use of AI for intelligent Phage therapy.

Navigation System for Blind People Using Artificial Intelligence

Do you know according to WHO, there are about 39 million people in the world who are blind? Artificial Intelligence is one of our key research area to overcome that challenge. Here, we explain the use of AI based grid cell, place cell and path integration strategies to solve the problems.

Dr. Amit Ray explains how grid cell, place cell and path integration strategies with artificial intelligence can be used for designing the navigation system for blind people. Here, we discuss the use of AI techniques for automatic navigation.

Brain-Computer Interface and Compassionate Artificial Intelligence

Brain-Computer Interface and Compassionate Artificial Intelligence

Dr. Amit Ray

The purpose of Compassionate AI is to remove the pain from the society and help humanity. We focused on developing AI based low cost BCI based interfaces for helping disable people.

Artificial Intelligence with Brain-Computer Interface (BCI) or Brain Machine Interface (BMI) is a fast-growing emerging technology for removing pains from the society. Here, Dr. Amit Ray explains how with the advancement of artificial intelligence and exploration of new mobile bio-monitoring devices, earphones, neuroprosthetic, wireless wearable sensors, it is possible to monitor thoughts and activities of brain neurons and serve humanity.

This research is going to be immensely beneficial for the physically and mentally challenged people as well as for the people who are suffering from post-traumatic stress disorder (PTSD), and other mental disorders or brain problems. Over the last 5 years, technologies for non-invasive transmission of information from brains to computers have developed considerably.

Brain-Computer Interface and Compassionate AI

Here, researchers focus to build a direct communication link between the human brain and the smartphones, earphone, computers or other devices. With BCI mind can speak silently with a smartphone or other devices. Recent advancement of neuroprosthetic, linking the human nervous system to computers and providing unprecedented control of artificial limbs and restoring lost sensory function.

BCI establishes two way communications between the brain and the machine. One is brain-computer interface, and another is called computer-brain interfaces (CBI). BCI hopes to create new communication channels for disabled or elderly persons using their brain signals.

Contact us | About us | Privacy Policy and Terms of Use |

Copyright ©AmitRay.com, 2010-2024, All rights reserved. Not to be reproduced.