AI AGENTS Generative AI Cognitive architecture

International Journal of Advanced Information Technology (IJAIT) Vol.15, No.1/2, April 2025
DOI: 10.5121/ijait.2025.15202 9
AI AGENTS
Ganesh Viswanathan, Gaurav Samdani , Yawal Dixit
USA
ABSTRACT
This whitepaper explores the concept of Generative AI agents, their cognitive architecture, and the
foundational components driving their autonomy, reasoning, and action-oriented behaviour. By delving
into the intricacies of these systems, we provide insights into the current capabilities of Generative AI
agents and their potential for driving innovation across various applications.
KEYWORDS
Generative AI, AI Agents, Cognitive architecture, Autonomous AI, AI reasoning and decision-making,
multi-agent systems, large language model fine tuning, React (Reason + Act), Chain of Thought (CoT),
Tree of Thought (ToT), retrieval augmented generation (RAG), explainable AI(XAI), Reinforcement
learning.
1. INTRODUCTION
Humans are skilled at recognizing patterns and often use tools like books, search engines, or
calculators to enhance their knowledge and decision-making. Similarly, Generative AI models
can use external tools for real-time information or specific actions, such as retrieving customer
data for personalized recommendations or using APIs for tasks like composing emails or
completing transactions.
To achieve this, models must interface with external tools and autonomously plan, reason, and
execute tasks, transforming them into "agents" with advanced capabilities. This whitepaper
explores the principles, design, and operations of these agents, highlighting their potential
applications
1.1. What are AI Agents
At its core, a Generative AI agent is an application designed to achieve a specific objective by
perceiving its environment and taking actions based on the tools available. These agents are
autonomous, capable of operating independently without direct human oversight when provided
with clear goals or objectives. Agents can reason for subsequent actions to move toward their
ultimate objective even without explicit human instructions.
While the concept of agents in artificial intelligence is broad and versatile, this whitepaper
focuses on the types of agents that can be constructed using contemporary Generative AI models.
To understand the functionality of these agents, it is important to examine the foundational
elements that govern their behaviour, actions, and decision-making processes. These elements
together are referred to as the agent's cognitive architecture. Various architectures can be
designed through the strategic combination of these components. This discussion centres on the
three core components that form the basis of an agent’s cognitive architecture, as shown in
Figure 1.

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Figure 1: AI Agent General Architecture
1.2. Components of AI Agent
The Planning module/ layer: In an AI agent, the "the planning module" refers to the module
which is often integrated to a language model that drives decision-making. The agent may use
various language models, from small to large, capable of instruction-based reasoning like ReAct,
Chain-of-Thought, or Tree-of-Thoughts. These models can be general-purpose, multimodal, or
fine-tuned to meet the agent's specific needs. For optimal performance, select a model aligned
with the intended application and trained on relevant datasets. The model might not be pre-
trained with the agent's exact settings but can be refined through curated examples showcasing
the agent's tools and reasoning strategies. This ensures greater adaptability and precision in the
agent's tasks.
The tools layer: Foundational AI models, despite their impressive capacity to generate text and
images, are inherently constrained by their inability to directly interact with external systems and
real-world data. Tools play a crucial role in bridging this gap by allowing AI agents to engage
with external data sources and services, thus substantially broadening the range of actions these
agents can perform beyond the foundational model's inherent capabilities. These tools typically
integrate with standard web API operations, such as GET, POST, PATCH, and DELETE,
enabling agents to execute diverse and sophisticated tasks. For example, a tool might update
customer records in a database, retrieve weather data to inform travel recommendations, or
perform an API call to obtain real-time supply chain information. Such functionalities allow AI
agents to process and act on real-world data, making them indispensable in enterprise-grade IT
applications. Additionally, tools empower agents to support advanced paradigms like Retrieval-
Augmented Generation (RAG), which combines the reasoning and generation capabilities of
foundational models with real-time access to domain-specific knowledge. This integration
permits AI agents to extend their functionalities to specialized IT workflows, including dynamic
resource allocation, personalized service delivery, and automated incident management. In
summary, tools serve as the vital connection between an agent's internal computational
capabilities and the external IT ecosystem, unlocking a wider range of possibilities and driving
innovation in intelligent systems. Their significance lies in enabling agents to seamlessly adapt to
and operate within the dynamic, complex landscape of the IT industry.

11
Agent core: The agent core represents a driver process that governs how an agent gathers
information, performs internal reasoning, and uses that reasoning to guide its next action or
decision. This typically continues until the agent reaches its goal or a defined stopping point. The
complexity of the agent core can vary widely based on the agent and the task at hand. Some
blocks may involve straightforward calculations and decision rules, while others may include
chained logic, additional machine learning algorithms, or probabilistic reasoning techniques.
Memory Module: The memory module is a big part of the agent which does session management
and also retains information about past interactions and partial results which can be used for
completing tasks and providing context.
2. ARCHITECTURE OF AGENTS
Agents use cognitive architecture to achieve their goals by repeatedly processing information,
making decisions, and refining actions based on previous inputs. The orchestration layer is
crucial in agent cognitive architectures, aiding memory retention, state management, reasoning,
and decision-making. It uses prompt engineering frameworks to improve reasoning and task
execution, enhancing the agent’s environmental interactions. Here are some widely adopted
frameworks and reasoning techniques.
React (Reason + Act): React is a method where the model systematically solves problems step by
step, using tools or resources to act.
Chain of Thought (CoT): This method breaks down complex problems into sequential steps for
better solutions. Key sub-methodologies include:
Self-Consistency: Combines multiple reasoning paths for stronger answers. Active-Prompting:
Selects relevant examples to improve reasoning.
Multimodal CoT: Extends reasoning to both text and images.
Tree of Thought (ToT): this approach extends the Chain of Thought (CoT) approach
systematically exploring multiple reasoning paths, akin to branching structures in a tree. It is
particularly effective for tasks that require strategic foresight, such as planning and game-
solving, by allowing models to evaluate different reasoning trajectories and backtrack when
necessary. This method enhances flexibility and creativity in problem-solving but comes with
challenges, including high computational demands and increased implementation complexity.
2.1. Tools Utilized by Agents
AI Agents are empowered by various tools which makes them real-time, context-aware and
provide them ability to interact with external systems

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Feature Extensions Functions Data Stores
Definition Standardized interfaces that
allow agents to interact
seamlessly with APIs, guiding
API usage through structured
examples.
Reusable code
components that
automate tasks and allow
models to decide when
and how to use them.
Mechanisms that
provide LLMs with
access to external,
up-to-date information
without retraining.
Execution Runs on the agent-side,
enabling built-in
capabilities like reasoning,
session management, and tool
usage.
Executes on the client-
side, giving
developers control over
execution,
security, and data flow.
Acts as a vector
database accessible
during runtime for
retrieval of
supplemental
information.
Key Features -Independent but must be
included in agent
configuration.
- Uses dynamic selection at
runtime to choose the most
suitable extension.
-Outputs function name
and arguments but does
not execute API calls.
- Keeps execution within
the developer’s
environment for
security and control.
- Converts
documents into
vector embeddings for
retrieval.
- Supplies additional
data in its original
format without
retraining.
Use Cases -Enabling seamless API
execution across different
implementations.
- Providing pre-built
functionalities like Vertex
Search and Code Interpreter.
-Managing long-
running background
tasks.
- Keeping sensitive
operations secure by
executing on
controlled environments.
- Enhancing
Retrieval- Augmented
Generation (RAG)
applications.
- Supporting
structured and
unstructured data
sources.
Reference Figures 3.1 3.2 3.3
Fig:3.1
Fig:3.2

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Fig:3.3
3. SCOPE FOR FINE-TUNING AND TRANSFER LEARNING IN AIAGENTS
The adaptability and effectiveness of AI agents depend significantly on their ability to generalize
knowledge and refine their decision-making abilities. Fine-tuning and transfer learning serve as
critical techniques to enhance agent performance, ensuring efficient deployment across diverse
tasks and domains.
3.1. Fine-Tuning Capabilities
Fine-tuning involves updating pre-trained models with domain-specific datasets to improve their
accuracy and contextual relevance. AI agents benefit from fine-tuning in the following ways:
Domain Specialization: Customizing agents for industry-specific applications such as finance,
healthcare, or legal analytics.
Task Optimization: Refining agents for complex reasoning tasks, improving contextual
understanding and response generation.
Personalization: Adapting agent behavior based on user interactions, leading to more effective
and human-like engagement.
Ethical & Bias Mitigation: Fine-tuning with carefully curated datasets can reduce biases and
align agents with ethical guidelines.
3.2. Transfer Learning Capabilities
Transfer learning enables AI agents to apply knowledge from one domain to another,
accelerating training and reducing resource requirements. Key benefits include:
Cross-Domain Adaptability: AI agents trained in one sector (e.g., customer service) can be
adapted for another (e.g., technical support) with minimal retraining.
Efficient Learning: Reduces computational costs by leveraging existing pre-trained models
instead of training from scratch.
Multi-Tasking Enhancement: Improves an agent’s ability to handle multiple tasks by transferring
foundational knowledge from one application to another.
Generalization: Enhances reasoning capabilities, allowing AI agents to handle novel problems

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beyond their initial training scope.
By incorporating fine-tuning and transfer learning, AI agents can achieve superior adaptability,
efficiency, and contextual intelligence, ensuring their responsible and scalable deployment across
industries.
4.CHALLENGES AND LIMITATIONS
Despite having frameworks like langchain and haystack users must build agents from scratch and
fine tune them to suite the needs of the application. There is no one size that fits all. LLM’s are
evolving daily and there is no prescribed language model which works best with agents. Even if
we create one, AI agents face several challenges:
Context handling: Helping AI agents to maintain the context over multiple step process is still an
art due to context length limitations and ability for important information to get lost in the
translation process.
Prompt Maintenance: Carefully crafted prompts play a really important role in AI agents. They
may degrade and drift over time. Without a good system for managing and refining prompts even
best ai agents can fail
Error Handling: Ai agents have several simple and complex moving parts and integration points
which are always prone to failure. Error handling of an agent is complex as there could be multi
point failures happening in tandem to cause issues which are extremely difficult to reproduce.
Security and privacy: Agents can interact with sensitive data where security becomes a priority.
Too much encryption and removal can also reduce the context of content to the large language
model.
Explainability and Transparency: The concepts of explainability and transparency in AI agents
encounter several challenges, primarily due to the complexities inherent in deep learning models,
which often operate as "black boxes" and obscure their decision-making processes. The absence
of standardized explainability metrics across various industries results in inconsistencies in
implementation. Additionally, the trade-off between explainability and performance presents a
dilemma as simpler, interpretable models frequently underperform relative to more complex
neural networks.
Bias and ethical concerns emerge when AI agents reinforce pre-existing biases without
transparent mechanisms for detection and mitigation. Security and privacy risks further
exacerbate transparency issues, as excessive disclosure about a model's internal workings can
expose it to adversarial attacks or compromise sensitive user data. Existing explainability
techniques, such as SHAP and LIME, offer predominantly post-hoc interpretations rather than
intrinsic transparency. Moreover, AI agents that continuously learn and adapt pose challenges to
providing consistent explanations.
The complexity increases with multi-agent systems, as they give rise to emergent behaviors that
are difficult to interpret. Regulatory frameworks like GDPR require "meaningful explanations"
yet lack clear implementation standards. These challenges contribute to user distrust and impede
adoption, especially in critical sectors such as healthcare, finance, and legal decision-making.
Advancements in Explainable AI (XAI), causal inference, and interpretable machine learning
techniques will be crucial in addressing these challenges while maintaining model performance

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and security.
Ethical Considerations: AI agents face challenges like bias, fairness, accountability, and
transparency. They often inherit biases from training data, leading to unfair decisions.
Transparency issues make assessing responsibility difficult. Privacy concerns stem from
extensive data collection, and misinformation risks harm trust. AI automation may cause job
displacement and widen social inequalities. Misuse in surveillance, warfare, and political
manipulation also raises ethical issues. Addressing these requires robust regulations, ethical
design, and continuous oversight.
Computational complexity: AI agents struggle with computational complexity issues such as
efficiency, scalability, and real-time decision-making. High computational costs, memory limits,
and scalability make large AI models resource intensive. Problems like combinatorial
optimization often have exponential time complexity, complicating real-time applications.
Latency in decisions, reinforcement learning inefficiencies, and multi-agent coordination
overheads further impact performance. Techniques like model compression help but can reduce
accuracy. Data processing bottlenecks and integrating quantum computing also add challenges.
Improving model efficiency, specialized hardware, and optimized algorithms are needed for
scalable AI deployment.
5. FUTURE DIRECTIONS
Future research will focus on improving multi-modal reasoning, integrating neuro-symbolic AI,
and ensuring robust ethical AI development.
The future of AI agents is expected to see significant advancements across multiple dimensions,
including autonomy, adaptability, and human-AI collaboration. Autonomous and self-improving
agents will utilize reinforcement learning, continual learning, and meta-learning to refine
decision-making in dynamic environments. Explainability and transparency are likely to improve
through advancements in interpretable AI, ensuring trust and regulatory compliance. Multi-agent
systems will enhance cooperative intelligence, enabling AI agents to work seamlessly in teams
across industries such as robotics, finance, and healthcare. Hybrid AI approaches, integrating
symbolic reasoning with deep learning, will enable agents to reason more effectively, combining
logic-based inference with data-driven methods.
AI agents are also expected to benefit from hardware acceleration, utilizing neuromorphic
computing and quantum AI to address computational limitations. Human-AI collaboration may
become more intuitive, with natural language processing (NLP) and multi-modal AI enabling
seamless interactions. Personalized AI agents could emerge, learning individual preferences and
acting as highly customized digital assistants. Additionally, ethical and responsible AI
development will emphasize bias mitigation, fairness, and regulatory alignment to ensure safe
deployment in sensitive domains.
The next generation of AI agents will likely be cloud-native and edge-deployable, optimizing for
real-time, low-latency decision-making in areas such as autonomous vehicles and smart cities.
AI-driven automation will further streamline business processes, enhancing productivity and
decision support. Finally, the convergence of AI with the Internet of Things (IoT) and blockchain
is expected to enable more secure, decentralized, and transparent AI ecosystems. Addressing
current limitations in explainability, efficiency, and adaptability will be important for AI agents
to achieve their full potential in transforming industries.

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6.CONCLUSION
Generative AI agents represent a major shift in artificial intelligence, evolving into autonomous
systems that can reason, make decisions, and interact with the real world. By combining
cognitive architecture, reinforcement learning, and prompt engineering, these agents are
transforming automation and problem-solving across industries.
However, challenges like context retention, security, ethical concerns, and computational
constraints persist. Continuous improvements in model design, error-handling, and regulatory
frameworks are needed to ensure transparency, fairness, and robustness.
Future advancements in neuro-symbolic AI, multi-modal reasoning, and human-AI collaboration
will shape AI agents' capabilities. Specialized hardware, hybrid AI, and enhanced reinforcement
learning will expand possibilities in healthcare, finance, and automation.
Balancing efficiency, ethical responsibility, and explainability is crucial for AI agents to drive
meaningful changes. With innovation and strong oversight, AI agents can integrate into daily
life, augment human abilities, and usher in intelligent automation.
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AUTHORS
Ganesh Viswanathan is an accomplished technology leader with expertise in AI, cloud engineering, and
intelligent automation. Currently serving as the AVP - Senior Principal AI Engineer at MetLife, Ganesh
plays a pivotal role in driving the company’s technology vision, modernizing infrastructure, and leading AI
initiatives like intelligent document processing and mainframe modernization. With a strong background in
test automation and cloud solutions, Ganesh previously contributed significantly at Ally Financials.
Beyond his professional achievements, he is a dedicated family man, balancing his career with parenting
two school-aged children. Ganesh is also data science enthusiast with master’s in data science and business
analytics and is passionate about learning and exploring cutting-edge technologies like Ai agents and
GitHub Copilot.
Gaurav has over 18 years of experience leading automation, AI and data analytics teams Currently
serving as Director Lead at Ally Financial. Gaurav lays a pivotal role in driving the company’s technology
vision, modernizing infrastructure, and leading AI initiatives like intelligent automation, Agentic AI and
technology modernization.
Yawal Dixit is a Solutions/Technology Architect at Cognizant Technology Solutions with over 14 years
of experience in intelligent automation, AI & ML, and software development. He specializes in leading
AI-driven intelligent automation projects to improve operational efficiency and productivity. A recognized
researcher and mentor in Intelligent Process Automation (IPA), Yawal is passionate about advancing
digital transformation. He holds a bachelor’s in information technology from Rajiv Gandhi Technical
University and certifications in AI, ML, and RPA. Known for his leadership and problem- solving skills,
he is dedicated to leveraging emerging technologies to create smarter business solutions

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