Deep dive into Agentic AI stack 

By Rajesh Dangi

The field of Artificial Intelligence (AI) is undergoing a transformative evolution, moving beyond simple  automation to create systems capable of reasoning, learning, and acting autonomously. At the forefront  of this evolution is the concept of “Agentic AI,” where intelligent agents are designed to make  independent decisions, execute tasks, and even engage in long-term planning. These agents operate  with a degree of autonomy, adaptability, and contextual awareness that mimics human-like  intelligence, enabling them to tackle complex problems across industries. 

Globally, Agentic AI is gaining traction as organisations and governments recognise its potential to drive  innovation and efficiency. In India, the adoption of AI technologies is accelerating, with the government  launching initiatives like the National AI Strategy and establishing centres of excellence to foster AI  research and development. Indian startups and tech giants are also leveraging AI to address challenges  in healthcare, agriculture, and education. For instance, AI-powered diagnostic tools are being deployed  in rural areas to improve access to healthcare, while AI-driven precision farming solutions are helping  farmers optimise crop yields. 

On the global stage, Agentic AI is reshaping industries and economies. In the United States, tech giants  like Google, Microsoft, and OpenAI are pioneering advancements in large language models (LLMs) and  autonomous systems. Europe, with its emphasis on ethical AI, is developing frameworks to ensure that  AI systems are transparent, fair, and accountable. The European Union’s AI Act, for example, aims to  regulate high-risk AI applications while promoting innovation. This article delves into the architecture  of a typical Agentic AI Stack, breaking down its layers and highlighting the key components that 

empower these intelligent agents. It also explores the challenges, ethical considerations, and future  directions of Agentic AI, offering insights into how this transformative technology is shaping the digital  world. 

The layered architecture  

The hypothesis of AI agents suggest they can achieve autonomous decision-making and task execution  by organising their functionalities into distinct, interconnected layers, each specialising in a specific  aspect of intelligence. This architecture is built on the premise that separating concerns such as  knowledge retrieval, action orchestration, reasoning, learning, and security thus allows for modular,  scalable, and efficient development of AI systems.  

By structuring the agent’s capabilities in this way, each layer can evolve independently while seamlessly  integrating with others, enabling the agent to reason, learn, and act in complex, dynamic environments.  This layered approach not only enhances the agent’s adaptability and performance but also provides a  framework for addressing challenges like transparency, ethical governance, and continuous  improvement, making it a robust foundation for the future of autonomous AI systems. These layers  work in harmony to provide the agent with the tools, knowledge, reasoning power, and adaptability  required to perform complex tasks. Below is a detailed exploration of these layers… 

1. Tool / Retrieval layer 

The Tool / Retrieval Layer forms the backbone of an intelligent agent’s ability to gather, process, and  apply knowledge. It enables the agent to retrieve relevant information from diverse data sources,  ensuring it has the necessary context to make informed decisions and execute tasks effectively. By 

integrating various databases, APIs, and knowledge structures, this layer acts as a bridge between raw  data and actionable intelligence, equipping the agent with a robust understanding of its environment. 

Data sources 

An intelligent agent relies on multiple data sources to function effectively, spanning both structured  and unstructured formats. Structured data includes operational databases, enterprise resource  planning (ERP) systems, and customer relationship management (CRM) tools that store well-defined  records. Unstructured data, such as web search results, documents, and social media content, provides  additional contextual information that helps the agent navigate complex scenarios. Additionally, APIs  grant access to real-time data from SaaS platforms, IoT devices, and proprietary data services, ensuring  that the agent can operate with the most current and relevant information available. Seamlessly  integrating these diverse sources is essential for building a highly functional and responsive AI system. 

Vector databases 

To enable efficient retrieval of semantically relevant information, the retrieval layer employs vector  databases such as Pinecone, Weaviate, and FAISS. Unlike traditional databases that rely on exact  keyword matches, vector databases store and index data in a high-dimensional space, capturing  contextual and semantic relationships. This allows the agent to perform similarity searches, retrieving  relevant documents, past interactions, or knowledge snippets based on meaning rather than exact  wording. Vector retrieval is particularly useful for natural language processing (NLP) applications,  recommendation systems, and conversational AI, ensuring that responses are contextually accurate  and insightful. 

Knowledge graphs 

While vector databases focus on semantic similarity, knowledge graphs enhance the agent’s reasoning  capabilities by representing information as a network of entities and relationships. These graphs store  knowledge in a structured format, making it easier for the agent to infer connections, detect patterns,  and apply domain-specific reasoning. For example, a supply chain management AI could leverage a  knowledge graph to understand relationships between suppliers, logistics providers, and customers,  enabling it to optimise delivery schedules and anticipate disruptions. By combining explicit knowledge  representations with deep retrieval mechanisms, knowledge graphs improve the agent’s ability to  provide well-informed and logically sound responses. 

Business logic 

For an intelligent agent to align with an organisation’s objectives, it must adhere to business logic, which  dictates the rules, constraints, and workflows governing its operations. Business logic defines the  decision-making framework within which the agent operates, ensuring compliance with industry  standards, regulatory requirements, and internal policies. For example, an AI-driven financial assistant  might be programmed to follow specific risk assessment guidelines before approving transactions, or  an HR automation agent might enforce company policies on leave approvals. By embedding business  logic within the retrieval layer, the agent can ensure that all retrieved information and subsequent  actions remain aligned with predefined objectives and constraints.

User interface 

To facilitate human interaction, the retrieval layer incorporates various user interface (UI) components,  enabling users to provide inputs, receive outputs, and refine the agent’s responses. UI implementations  range from chatbots and voice assistants to graphical dashboards and API endpoints that allow  seamless integration into existing enterprise applications. A well-designed UI enhances usability,  allowing users to interact naturally with the system while providing valuable feedback that can refine  the agent’s responses over time. Advanced UI implementations may also include multimodal interfaces,  integrating text, speech, and visual data for a more immersive and accessible user experience. 

Key components and tools 

Several tools and frameworks power the retrieval layer, ensuring efficient data processing, integration,  and retrieval. SingleStore, a high-performance database, enables real-time analytics and transactional  workloads. FastAPI, a lightweight and high-speed API framework, allows developers to build flexible and  scalable retrieval endpoints. Additionally, external APIs for web search, data enrichment, and third 

party integrations extend the agent’s reach, enabling it to pull in relevant information dynamically.  These tools work in unison to create a seamless retrieval infrastructure that balances speed, accuracy,  and scalability. 

By incorporating structured and unstructured data sources, leveraging advanced retrieval mechanisms  like vector databases and knowledge graphs, enforcing business logic, and providing user-friendly  interfaces, the Tool / Retrieval Layer forms the foundation of an intelligent agent’s knowledge system.  As AI continues to evolve, this layer will play an increasingly vital role in ensuring that agents can access,  interpret, and apply information effectively, enabling smarter decision-making and automation across  various domains. 

2. Action / Orchestration layer 

The Action / Orchestration Layer is a critical component in an intelligent agent’s architecture,  responsible for transforming insights and understanding into concrete, executable actions. It serves as  the bridge between perception and execution, ensuring that workflows are effectively managed, tasks  are executed efficiently, and system interactions remain seamless. This layer must handle the  complexity of decision-making, automation, and resource coordination while maintaining adaptability  to dynamic conditions. 

Task management 

One of the primary functions of this layer is task management, which involves breaking down high-level  objectives into smaller, executable sub-tasks. By structuring tasks hierarchically, the agent can optimise  execution based on priority, dependencies, and resource constraints. For instance, if an agent is  responsible for processing customer support queries, it must first classify the request, determine the  necessary sub-tasks (e.g., retrieving user history, checking issue logs, drafting a response), and execute  them in an orderly fashion. Efficient scheduling and prioritisation algorithms ensure that urgent and  dependent tasks are handled appropriately, minimising bottlenecks and maximising throughput.

Persistent memory 

For an intelligent agent to be effective over long-term interactions, it must maintain persistent memory  to store contextual information. This memory allows the system to retain user preferences, past  interactions, and learned experiences, enabling a more personalised and efficient response over time.  Memory structures can range from simple key-value storage solutions to more sophisticated  architectures like vector databases and neural memory networks. Persistent memory is particularly  useful in applications requiring long-term contextual awareness, such as AI-powered assistants,  customer relationship management, and knowledge-based automation. 

Automation scripts 

To enhance efficiency, this layer employs automation scripts that handle repetitive and predictable  tasks without requiring real-time decision-making. Automation scripts allow the agent to execute  predefined sequences of actions, such as data entry, report generation, or system monitoring, thereby  reducing human workload and increasing operational consistency. These scripts are often integrated  with workflow automation platforms, enabling seamless connectivity between different services, APIs,  and databases. By offloading routine operations to automation, the agent can allocate more  computational resources to higher-order reasoning and decision-making tasks. 

Event logging 

A robust event logging mechanism is essential for monitoring and auditing the agent’s actions. Every  decision, action, and system event is recorded, creating a transparent and traceable execution history.  Logs provide insights into system performance, error detection, and user interactions, making them  invaluable for debugging, compliance, and analytics. In regulated environments, such as finance and  healthcare, logging is a fundamental requirement to ensure accountability and compliance with  industry standards. Advanced logging frameworks also incorporate real-time monitoring and alerting  mechanisms to detect anomalies and optimise system performance dynamically. 

Key components and tools 

Several frameworks and tools facilitate the implementation of the Action / Orchestration Layer.  Technologies like CrewAI, LangChain, and LangGraph provide structured approaches to agent  orchestration, allowing developers to design complex multi-agent workflows with ease. Additionally,  automation platforms like Zapier and n8n enable seamless integration with third-party services, APIs,  and databases, extending the agent’s capability to interact with diverse ecosystems. These tools help  streamline execution, minimise manual intervention, and enhance the overall intelligence of AI-driven  workflows. 

3. Reasoning layer as the heart of intelligence 

The Reasoning Layer is where the agent’s cognitive processes take place, enabling it to analyse data,  understand context, draw inferences, and make informed decisions. This layer bridges raw data  retrieval and actionable execution by leveraging advanced AI models and structured reasoning  techniques. By integrating sophisticated language models, decision frameworks, and contextual 

analysis mechanisms, the Reasoning Layer allows AI agents to operate with intelligence, adaptability,  and autonomy. 

Large Language Models (LLMs) 

At the core of modern AI reasoning capabilities are Large Language Models (LLMs) such as GPT-4,  Claude, and Gemini. These models, trained on vast amounts of text data, enable the agent to  understand, generate, and reason about human language with remarkable fluency. LLMs facilitate a  wide range of reasoning tasks, including summarisation, question-answering, code generation, and  problem-solving. By incorporating retrieval-augmented generation (RAG) techniques, LLMs can extend  their reasoning capabilities by fetching relevant external information in real-time, ensuring accuracy  and context-awareness in their responses. 

Contextual analysis 

For an AI agent to make sound decisions, it must engage in contextual analysis, evaluating the situation,  historical data, user preferences, and environmental factors before determining the best course of  action. This process involves identifying implicit dependencies, underlying constraints, and potential  long-term effects of a decision. For example, an AI-driven customer support agent must not only  analyse the current user query but also factor in past interactions, urgency levels, and company policies  to provide a well-informed response. Effective contextual analysis enhances decision quality and  ensures a coherent, relevant, and adaptive reasoning process. 

Decision trees 

For structured decision-making, the Reasoning Layer often employs decision trees—hierarchical  models that guide the agent through a series of conditions and outcomes. These trees decompose  complex decisions into logical steps, making them particularly useful in rule-based systems, diagnostics,  and troubleshooting workflows. For example, an AI fraud detection system might use a decision tree to  assess transaction legitimacy based on factors like location, transaction history, and behavioural  patterns. Decision trees enhance transparency and interpretability, ensuring that AI-driven decisions  remain traceable and explainable. 

Natural Language Understanding (NLU) 

A crucial component of the Reasoning Layer is Natural Language Understanding (NLU), which enables  the agent to grasp intent, extract meaning, and handle linguistic nuances in user interactions. Unlike  basic text processing, NLU allows the agent to identify sentiment, detect entities, recognise context  shifts, and infer implicit requests. For example, an AI legal assistant using NLU can differentiate between  legal jargon, contractual clauses, and client inquiries to provide precise interpretations. Pre-trained NLU  models, such as those powered by spaCy, Hugging Face Transformers, or OpenAI embeddings,  significantly improve the agent’s ability to handle diverse and complex language inputs. 

Key components and tools 

To implement advanced reasoning capabilities, this layer leverages cutting-edge AI frameworks and  libraries:

• Transformers (Hugging face): Provides pre-trained LLMs and NLP models for contextual  reasoning and text processing. 
• spaCy: A robust NLP library for named entity recognition (NER), syntactic parsing, and text  classification. 
• TensorFlow & PyTorch: Core machine learning frameworks that enable the development and  fine-tuning of reasoning models. 
• ONNX runtime: Optimises model inference across different hardware environments, ensuring  efficiency in real-world applications. 

By integrating LLMs, structured reasoning techniques, and natural language understanding, the  Reasoning Layer forms the cognitive core of an AI agent. It enables adaptive, intelligent decision making, allowing AI-driven systems to understand complex scenarios, infer logical conclusions, and  engage in sophisticated interactions. As AI models evolve, this layer will continue to enhance the depth,  accuracy, and interpretability of machine intelligence, driving advancements across industries. 

4. Feedback / Learning layer 

The Feedback / Learning Layer is essential for ensuring that an AI agent continuously evolves and  improves over time. By integrating user feedback, model training techniques, and performance  monitoring, this layer enables the agent to refine its responses, adapt to new challenges, and optimise  its reasoning and execution capabilities. Without this continuous improvement loop, an AI system risks  stagnation, producing outdated or suboptimal results. 

User feedback loop 

A key mechanism for improvement is the user feedback loop, which allows users to directly or indirectly  shape the agent’s learning process. Explicit feedback includes ratings, comments, corrections, and  manual annotations, providing direct insights into the quality of the agent’s outputs. Implicit feedback,  on the other hand, is derived from user behaviour—how users interact with the agent, what responses  they accept or reject, and how often they rephrase queries. For example, if a virtual assistant frequently  receives corrections for misinterpreting a user’s intent, the system can learn to adjust its language  model accordingly. By continuously capturing and analysing feedback, the agent refines its decision making processes and improves its accuracy. 

Model training 

Once feedback is collected, the next step is model training, where the agent’s AI models are updated  based on new data. This process may involve: 

• Fine-tuning: Adjusting pre-trained models with new labelled data to improve domain-specific  accuracy. 
• Reinforcement Learning (RL): Rewarding successful predictions and penalising incorrect ones,  enabling models to refine their decision-making dynamically. 
• Self-Supervised Learning: Using large-scale, unlabelled data to discover patterns and improve  generalisation.

For example, Reinforcement Learning from Human Feedback (RLHF) is widely used in LLM-based  agents, ensuring that responses align with human preferences and ethical considerations. Periodic  retraining allows AI systems to stay relevant, unbiased, and high-performing in evolving environments. 

Performance metrics 

To assess and guide improvements, the Feedback / Learning Layer tracks key performance metrics,  which help identify strengths and areas needing refinement. Common metrics include: 

• Task Completion Rate: Measures how effectively the agent accomplishes assigned tasks. • Error Rate: Tracks incorrect responses, false positives/negatives, or misinterpretations. • User Satisfaction Score: Based on explicit ratings or engagement levels. 
• Latency and Efficiency: Evaluates response time and system resource utilisation. 

By continuously monitoring these indicators, AI developers can make data-driven decisions about when  and how to improve the model, ensuring it remains efficient, accurate, and aligned with user needs. 

Key components and tools 

To implement an effective learning framework, this layer leverages powerful tools… 

• LangSmith: Enables detailed evaluation and debugging of LLM-driven agents. • Weights & Biases: Tracks model performance, logs training experiments, and optimises ML  workflows. 
• Hugging Face: Provides infrastructure for fine-tuning models, dataset management, and  community-driven improvements. 

5. Security / Compliance layer: Ensuring responsible AI 

The Security / Compliance Layer is fundamental to ensuring that AI agents operate in a safe, ethical,  and legally compliant manner. As AI systems process sensitive data and make decisions that impact  individuals and businesses, it is crucial to implement robust security controls, enforce compliance  policies, and maintain accountability. This layer mitigates risks such as data breaches, unauthorised  access, regulatory violations, and unethical decision-making, fostering trust in AI-driven systems. 

Data encryption 

One of the core security measures in AI systems is data encryption, which ensures that sensitive  information remains protected from unauthorised access, tampering, or interception. Encryption  methods such as AES-256 (Advanced Encryption Standard) and end-to-end encryption help safeguard  user inputs, retrieval data, and model outputs. For example, in healthcare AI, encryption is vital for  securing electronic health records (EHRs), ensuring compliance with standards like HIPAA (Health  Insurance Portability and Accountability Act). Secure encryption frameworks ensure that even if data is  compromised, it remains unreadable to malicious actors.

Access control 

To prevent misuse or unauthorised access, AI agents must enforce strict access control mechanisms  based on user roles, authentication protocols, and permissions management. This involves  implementing:

– Role-Based Access Control (RBAC): Restricting functionalities based on user roles (e.g., admin,  operator, end-user).

– Multi-Factor Authentication (MFA): Adding additional layers of verification beyond just  passwords.

– Zero Trust Security: Verifying every request, regardless of whether it originates from inside or  outside the organisation.

For example, an AI-powered financial advisory system must ensure that only certified professionals can  modify investment strategies, while clients can only view recommendations. Effective access control  minimises the risk of data leaks and unauthorised modifications. 

Compliance monitoring 

AI systems must adhere to global regulations, industry-specific policies, and ethical AI guidelines to  ensure legal and responsible operation. The Compliance Monitoring function continuously assesses  whether the AI agent follows applicable standards, such as: 

• DPDP Act – Most crucial act for Personal data protection enacted in India • GDPR (General Data Protection Regulation): Governing data privacy and user rights in the EU. • HIPAA: Ensuring the security and confidentiality of medical records. 
• ISO 27701: A framework for Privacy Information Management Systems (PIMS). • SOC 2: Addressing security, availability, and data integrity in cloud-based AI services. 

By implementing automated compliance checks, AI agents can detect and mitigate potential data  privacy violations, policy breaches, and unethical practices in real time. 

Audit trails 

Maintaining comprehensive audit logs is essential for accountability, transparency, and regulatory  compliance. An audit trail records: 

• Every action performed by the AI agent (e.g., decision-making, data access, API calls). • User interactions and modifications (e.g., changes in security settings or permissions). • System alerts and security incidents (e.g., unauthorised access attempts, compliance  violations). 

Audit logs help organisations trace security incidents, investigate anomalies, and demonstrate  compliance during regulatory audits. For example, a legal AI assistant handling sensitive contracts must  log every instance of document access and modifications to ensure traceability.

Key components and tools 

To effectively implement security and compliance, organisations rely on various security frameworks  and compliance tools, including: 

• Vault (by HashiCorp): Manages secrets, encryption keys, and access policies securely. • Encryption Frameworks: Such as OpenSSL, AWS KMS (Key Management Service), and Google  Cloud KMS for securing data at rest and in transit. 
• Compliance Platforms: Tools like OneTrust and Drata automate compliance tracking and  reporting. 

By integrating these tools, the Security / Compliance Layer ensures that AI agents are protected against  threats, operate within legal frameworks, and maintain ethical integrity. As AI continues to evolve, this  layer remains critical in building trustworthy, responsible, and resilient AI systems. 

6. Challenges and ethical considerations 

Agentic AI presents transformative potential but also raises significant challenges and ethical concerns  that must be carefully managed. Bias and fairness remain critical issues, as AI agents trained on biased  datasets can perpetuate and even amplify discrimination in areas like hiring, lending, and law  enforcement. Ensuring algorithmic fairness requires continuous data auditing, bias detection, and  transparency in decision-making. Additionally, the black-box nature of many AI models makes it difficult  for users to understand or challenge an agent’s decisions, leading to trust and accountability concerns.  To address this, AI systems should incorporate explainability mechanisms, such as human-readable  justifications and interpretable decision frameworks, to enhance transparency. Privacy and security also  pose risks, as AI agents interact with sensitive personal and corporate data. Without proper safeguards,  they may inadvertently expose confidential information or fall victim to adversarial attacks. Enforcing  strict data protection protocols, encryption, and access controls is essential to prevent misuse and  ensure compliance with privacy regulations like GDPR and HIPAA. 

Another key challenge is finding the right balance between AI autonomy and human oversight. While  autonomous agents can enhance efficiency and decision-making, unchecked autonomy may result in  unintended consequences or misaligned actions. Over-reliance on AI could also lead to human  complacency, where users blindly trust AI decisions without verification. To mitigate these risks, AI  systems should implement “human-in-the-loop” mechanisms, where humans retain control over  critical decisions, particularly in high-stakes applications like healthcare and finance. Additionally, AI  agents should be aligned with ethical principles and legal guidelines, ensuring that their actions remain  within acceptable boundaries. Incorporating fail-safe mechanisms and override controls can further  prevent AI agents from taking actions that could harm individuals or organisations. By proactively  addressing these challenges, developers and policymakers can ensure that Agentic AI remains a  powerful yet responsible tool, unlocking its full potential while safeguarding against ethical and security  risks. 

In Summary, The Agentic AI Stack represents a significant leap forward in the evolution of artificial  intelligence. By combining powerful tools, advanced models, and robust frameworks, this architecture  enables the creation of intelligent agents that can reason, learn, and act autonomously. As the field  continues to advance, even more sophisticated and impactful applications of Agentic AI are expected 

to emerge across various domains, including healthcare, finance, education, and entertainment. These  agents will transform the way humans interact with technology, solve complex problems, and drive  innovation in the years to come. However, realising this potential will require ongoing collaboration  between researchers, developers, and policymakers to address the technical, ethical, and societal  challenges that lie ahead.