<\/i>Table of Contents<\/h2>\n\n- \u2192<\/span>Introduction: The Critical Role of AI Architecture<\/li>\n
- \u2192<\/span>High-Level Overview of Generative AI Architecture<\/li>\n
- \u2192<\/span>Large Language Models (LLMs): The Core Engine<\/li>\n
- \u2192<\/span>Retrieval Augmented Generation (RAG): Grounding AI in Your Data<\/li>\n
- \u2192<\/span>AI Agents: From Answers to Autonomous Action<\/li>\n
- \u2192<\/span>How LLMs, RAG, and AI Agents Work Together<\/li>\n
- \u2192<\/span>Enterprise Use Cases Across Business Functions<\/li>\n
- \u2192<\/span>Build vs. Buy: Strategic Decisions for AI Implementation<\/li>\n
- \u2192<\/span>Governance, Security, and Responsible AI<\/li>\n
- \u2192<\/span>Future Outlook and Conclusion<\/li>\n<\/ul>\n<\/div>\n
<\/p>\n
\nGen AI Architecture: LLMs, RAG, and AI Agents<\/h2>\n
Building scalable, secure AI systems that move from experimentation to enterprise impact<\/p>\n<\/div>\n
<\/p>\n
\n\n$2.6T<\/div>\nto $4.4T annual value potential (McKinsey)<\/div>\n<\/div>\n\nTop Cause<\/div>\nPoor data quality & architecture cause AI failures (Gartner 2025)<\/div>\n<\/div>\n\n63+<\/div>\nHigh-value use cases across industries<\/div>\n<\/div>\n<\/div>\n<\/p>\n
\nGenerative AI has jumped from being a trend to being an actual tool for businesses. Various organizations use this AI to generate content, respond to queries, automate work, and help make better decisions. Most businesses, though, struggle with this technology since they only focus on the “tool” and not the “architecture.”<\/p>\n
\n\u26a0\ufe0f Gartner 2025 Research Warning<\/div>\n“Lack of AI-ready data puts AI projects at risk”, with organizations reporting that poor data quality and architecture are among the top causes of AI project failures.<\/p>\n<\/div>\n
In this regard, for success, it is important that leaders understand what generative AI architecture entails and why it is important. Generative AI refers to more than an AI model or a chatbot, for that matter. Rather, it consists of many interlocked pieces that form a larger system.<\/p>\n
In today\u2019s world, businesses apply Generative AI solutions to increase speed, lower costs, and discover insights in their own data. This explains why enterprise generative AI architecture and the role of generative AI architecture consulting have become so important in digital transformations.<\/p>\n
\n\u26a0\ufe0f McKinsey’s Analysis<\/div>\nGenerative AI could add $2.6 trillion to $4.4 trillion annually across 63 use cases.<\/p>\n<\/div>\n<\/div>\n
<\/p>\n
\n\n\ud83c\udfd7\ufe0f<\/div>\nHigh Level Overview of Generative AI Architecture<\/h2>\n<\/div>\n
At a high level, Generative AI works as a layered system. Each layer has a specific role and responsibility. Together, these layers define modern generative AI system design.<\/p>\n
<\/p>\n
\n<\/p>\n
\n\n5<\/div>\nApplications Layer<\/h3>\n<\/div>\n
Chatbots, copilots, internal dashboards, workflow automation tools<\/p>\n<\/div>\n
<\/p>\n
\n\n4<\/div>\nAI Agents Layer<\/h3>\n<\/div>\n
Plan actions, make decisions, interact with tools and APIs autonomously<\/p>\n<\/div>\n
<\/p>\n
\n\n3<\/div>\nAugmentation Layer (RAG)<\/h3>\n<\/div>\n
Retrieval augmented generation, security filters, business rules, access controls<\/p>\n<\/div>\n
<\/p>\n
\n\n2<\/div>\nLLM Layer<\/h3>\n<\/div>\n
Core reasoning engine, understands language, generates responses<\/p>\n<\/div>\n
<\/p>\n
\n\n1<\/div>\nData Foundation Layer<\/h3>\n<\/div>\n
Structured databases, unstructured documents, emails, PDFs, knowledge bases<\/p>\n<\/div>\n<\/div>\n
The foundation of the system is data. Enterprise data is the most valuable asset, but it must be used carefully. When these layers work well, AI can be trusted, secure, and scalable. However, when they do not, AI generates confusion, risk, and wasted investment.<\/p>\n
Above the data layer sits the large language model architecture. This is the core reasoning engine. The LLM understands language, interprets questions, and generates responses.<\/p>\n
Next comes the augmentation layer. This includes retrieval augmented generation architecture, security filters, business rules, and access controls. This layer ensures that the AI uses trusted data and follows company policies.<\/p>\n
Then there are AI agents. These agents can plan actions, make decisions, and interact with tools and APIs.<\/p>\n
At the top are applications. These include chatbots, copilots, internal dashboards, and workflow automation tools. Together, these layers define modern generative AI system design.<\/p>\n<\/div>\n
<\/p>\n
\n\n\ud83e\udde0<\/div>\nLarge Language Models as the Core Engine<\/h2>\n<\/div>\n
Large Language Models, or LLMs, are the heart of Generative AI. They are trained on massive amounts of text and learn how language works. This allows them to understand questions and generate human-like responses.<\/p>\n
\n\nLLM Capabilities<\/h3>\n\n- Understand natural language queries<\/li>\n
- Generate human-like responses<\/li>\n
- Summarize complex documents<\/li>\n
- Explain technical concepts<\/li>\n
- Draft emails and reports<\/li>\n<\/ul>\n<\/div>\n\n
LLM Limitations<\/h3>\n\n- Hallucination:<\/strong> Generate confident but incorrect answers<\/li>\n
- Data Blindness:<\/strong> Don’t know your company data<\/li>\n
- Compliance Gap:<\/strong> No built-in security rules<\/li>\n
- Context Limits:<\/strong> Limited memory for long conversations<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n
Enterprise Insight:<\/strong> In an enterprise setting, LLMs act as reasoning engines. They summarize reports, explain complex topics, draft emails, and answer questions in natural language. This is why LLM integration architecture is so important in AI system design.<\/p>\n<\/div>\nLLMs are powerful, but they are not perfect. One major limitation is hallucination. This means the model can generate answers that sound confident but are not correct. Another challenge is that LLMs do not know your company data unless you connect it. They also do not automatically understand compliance or security rules.<\/p>\n
Because of these limits, enterprises cannot rely on LLMs alone. They need additional layers to make AI safe and useful.<\/p>\n<\/div>\n
<\/p>\n
\n\n\ud83d\udd0d<\/div>\nRetrieval Augmented Generation and Why It Matters<\/h2>\n<\/div>\n
Retrieval Augmented Generation, or RAG, solves one of the biggest problems of LLMs. It grounds AI responses in real enterprise data, improving accuracy, reducing hallucinations, and supporting compliance.<\/p>\n
<\/p>\n
\nRAG Architecture Flow<\/h3>\n\n\n1<\/div>\nUser Query<\/div>\nNatural language question<\/div>\n<\/div>\n\n2<\/div>\nSemantic Search<\/div>\nAcross vector databases<\/div>\n<\/div>\n\n3<\/div>\nRetrieve Context<\/div>\nRelevant enterprise data<\/div>\n<\/div>\n\n4<\/div>\nLLM Generation<\/div>\nGrounded in retrieved data<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/p>\n
\n\nRAG Architecture<\/h3>\n\n- \u2713<\/span>Keeps model general<\/li>\n
- \u2713<\/span>Connects to fresh data<\/li>\n
- \u2713<\/span>More flexible & easier to maintain<\/li>\n
- \u2713<\/span>Reduces hallucinations<\/li>\n
- \u2713<\/span>Supports compliance<\/li>\n<\/ul>\n<\/div>\n\n
Fine-Tuning Architecture<\/h3>\n\n- \u26a1<\/span>Changes model itself<\/li>\n
- \u26a1<\/span>Requires more time & cost<\/li>\n
- \u26a1<\/span>Complex maintenance<\/li>\n
- \u26a1<\/span>Model can become outdated<\/li>\n
- \u26a1<\/span>Higher technical expertise needed<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
In a typical RAG flow, the system performs a semantic search across vector databases where enterprise data is stored as embeddings. This retrieval step ensures that the most contextually relevant information is selected, rather than relying on simple keyword matches. The retrieved trusted data is then passed to the LLM, which generates a response grounded in this enterprise knowledge.<\/p>\n
This approach improves accuracy, reduces hallucinations, and supports compliance. It also keeps sensitive data inside the enterprise environment.<\/p>\n
Many organizations compare RAG vs fine tuning architecture. Fine tuning changes the model itself and requires more time and cost. RAG keeps the model general and connects it to fresh data. For most enterprises, RAG is more flexible and easier to maintain.<\/p>\n
If you are asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n
\nDecision Guide:<\/strong> For most enterprises, RAG is more flexible and easier to maintain. If you’re asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n<\/div>\n<\/div>\n\n\n\ud83e\udd16<\/div>\nAI Agents and Autonomous Systems<\/h2>\n<\/div>\n
AI agents represent the next step in the evolution of Generative AI. Unlike traditional chatbots, agents do more than simply respond to user queries. They can plan tasks, make decisions, take actions, and interact autonomously with tools, systems, and APIs to achieve specific goals.<\/p>\n
<\/p>\n
\nAI Agent Architecture Components<\/h3>\n\n\n\ud83c\udfaf<\/div>\nGoal\/Objective<\/div>\nClear task or outcome to achieve<\/div>\n<\/div>\n\n\ud83e\udde0<\/div>\n
<\/p>\n
Gen AI Architecture: LLMs, RAG, and AI Agents<\/h2>\n
Building scalable, secure AI systems that move from experimentation to enterprise impact<\/p>\n<\/div>\n
<\/p>\n
<\/p>\n
Generative AI has jumped from being a trend to being an actual tool for businesses. Various organizations use this AI to generate content, respond to queries, automate work, and help make better decisions. Most businesses, though, struggle with this technology since they only focus on the “tool” and not the “architecture.”<\/p>\n
“Lack of AI-ready data puts AI projects at risk”, with organizations reporting that poor data quality and architecture are among the top causes of AI project failures.<\/p>\n<\/div>\n
In this regard, for success, it is important that leaders understand what generative AI architecture entails and why it is important. Generative AI refers to more than an AI model or a chatbot, for that matter. Rather, it consists of many interlocked pieces that form a larger system.<\/p>\n
In today\u2019s world, businesses apply Generative AI solutions to increase speed, lower costs, and discover insights in their own data. This explains why enterprise generative AI architecture and the role of generative AI architecture consulting have become so important in digital transformations.<\/p>\n
Generative AI could add $2.6 trillion to $4.4 trillion annually across 63 use cases.<\/p>\n<\/div>\n<\/div>\n
<\/p>\n
High Level Overview of Generative AI Architecture<\/h2>\n<\/div>\n
At a high level, Generative AI works as a layered system. Each layer has a specific role and responsibility. Together, these layers define modern generative AI system design.<\/p>\n
<\/p>\n
<\/p>\n
Applications Layer<\/h3>\n<\/div>\n
Chatbots, copilots, internal dashboards, workflow automation tools<\/p>\n<\/div>\n
<\/p>\n
AI Agents Layer<\/h3>\n<\/div>\n
Plan actions, make decisions, interact with tools and APIs autonomously<\/p>\n<\/div>\n
<\/p>\n
Augmentation Layer (RAG)<\/h3>\n<\/div>\n
Retrieval augmented generation, security filters, business rules, access controls<\/p>\n<\/div>\n
<\/p>\n
LLM Layer<\/h3>\n<\/div>\n
Core reasoning engine, understands language, generates responses<\/p>\n<\/div>\n
<\/p>\n
Data Foundation Layer<\/h3>\n<\/div>\n
Structured databases, unstructured documents, emails, PDFs, knowledge bases<\/p>\n<\/div>\n<\/div>\n
The foundation of the system is data. Enterprise data is the most valuable asset, but it must be used carefully. When these layers work well, AI can be trusted, secure, and scalable. However, when they do not, AI generates confusion, risk, and wasted investment.<\/p>\n
Above the data layer sits the large language model architecture. This is the core reasoning engine. The LLM understands language, interprets questions, and generates responses.<\/p>\n
Next comes the augmentation layer. This includes retrieval augmented generation architecture, security filters, business rules, and access controls. This layer ensures that the AI uses trusted data and follows company policies.<\/p>\n
Then there are AI agents. These agents can plan actions, make decisions, and interact with tools and APIs.<\/p>\n
At the top are applications. These include chatbots, copilots, internal dashboards, and workflow automation tools. Together, these layers define modern generative AI system design.<\/p>\n<\/div>\n
<\/p>\n
Large Language Models as the Core Engine<\/h2>\n<\/div>\n
Large Language Models, or LLMs, are the heart of Generative AI. They are trained on massive amounts of text and learn how language works. This allows them to understand questions and generate human-like responses.<\/p>\n
LLM Capabilities<\/h3>\n\n- Understand natural language queries<\/li>\n
- Generate human-like responses<\/li>\n
- Summarize complex documents<\/li>\n
- Explain technical concepts<\/li>\n
- Draft emails and reports<\/li>\n<\/ul>\n<\/div>\n\n
LLM Limitations<\/h3>\n\n- Hallucination:<\/strong> Generate confident but incorrect answers<\/li>\n
- Data Blindness:<\/strong> Don’t know your company data<\/li>\n
- Compliance Gap:<\/strong> No built-in security rules<\/li>\n
- Context Limits:<\/strong> Limited memory for long conversations<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n
Enterprise Insight:<\/strong> In an enterprise setting, LLMs act as reasoning engines. They summarize reports, explain complex topics, draft emails, and answer questions in natural language. This is why LLM integration architecture is so important in AI system design.<\/p>\n<\/div>\nLLMs are powerful, but they are not perfect. One major limitation is hallucination. This means the model can generate answers that sound confident but are not correct. Another challenge is that LLMs do not know your company data unless you connect it. They also do not automatically understand compliance or security rules.<\/p>\n
Because of these limits, enterprises cannot rely on LLMs alone. They need additional layers to make AI safe and useful.<\/p>\n<\/div>\n
<\/p>\n
\n\n\ud83d\udd0d<\/div>\nRetrieval Augmented Generation and Why It Matters<\/h2>\n<\/div>\n
Retrieval Augmented Generation, or RAG, solves one of the biggest problems of LLMs. It grounds AI responses in real enterprise data, improving accuracy, reducing hallucinations, and supporting compliance.<\/p>\n
<\/p>\n
\nRAG Architecture Flow<\/h3>\n\n\n1<\/div>\nUser Query<\/div>\nNatural language question<\/div>\n<\/div>\n\n2<\/div>\nSemantic Search<\/div>\nAcross vector databases<\/div>\n<\/div>\n\n3<\/div>\nRetrieve Context<\/div>\nRelevant enterprise data<\/div>\n<\/div>\n\n4<\/div>\nLLM Generation<\/div>\nGrounded in retrieved data<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/p>\n
\n\nRAG Architecture<\/h3>\n\n- \u2713<\/span>Keeps model general<\/li>\n
- \u2713<\/span>Connects to fresh data<\/li>\n
- \u2713<\/span>More flexible & easier to maintain<\/li>\n
- \u2713<\/span>Reduces hallucinations<\/li>\n
- \u2713<\/span>Supports compliance<\/li>\n<\/ul>\n<\/div>\n\n
Fine-Tuning Architecture<\/h3>\n\n- \u26a1<\/span>Changes model itself<\/li>\n
- \u26a1<\/span>Requires more time & cost<\/li>\n
- \u26a1<\/span>Complex maintenance<\/li>\n
- \u26a1<\/span>Model can become outdated<\/li>\n
- \u26a1<\/span>Higher technical expertise needed<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
In a typical RAG flow, the system performs a semantic search across vector databases where enterprise data is stored as embeddings. This retrieval step ensures that the most contextually relevant information is selected, rather than relying on simple keyword matches. The retrieved trusted data is then passed to the LLM, which generates a response grounded in this enterprise knowledge.<\/p>\n
This approach improves accuracy, reduces hallucinations, and supports compliance. It also keeps sensitive data inside the enterprise environment.<\/p>\n
Many organizations compare RAG vs fine tuning architecture. Fine tuning changes the model itself and requires more time and cost. RAG keeps the model general and connects it to fresh data. For most enterprises, RAG is more flexible and easier to maintain.<\/p>\n
If you are asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n
\nDecision Guide:<\/strong> For most enterprises, RAG is more flexible and easier to maintain. If you’re asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n<\/div>\n<\/div>\n\n\n\ud83e\udd16<\/div>\nAI Agents and Autonomous Systems<\/h2>\n<\/div>\n
AI agents represent the next step in the evolution of Generative AI. Unlike traditional chatbots, agents do more than simply respond to user queries. They can plan tasks, make decisions, take actions, and interact autonomously with tools, systems, and APIs to achieve specific goals.<\/p>\n
<\/p>\n
\nAI Agent Architecture Components<\/h3>\n\n\n\ud83c\udfaf<\/div>\nGoal\/Objective<\/div>\nClear task or outcome to achieve<\/div>\n<\/div>\n\n\ud83e\udde0<\/div>\n
LLM Limitations<\/h3>\n\n- Hallucination:<\/strong> Generate confident but incorrect answers<\/li>\n
- Data Blindness:<\/strong> Don’t know your company data<\/li>\n
- Compliance Gap:<\/strong> No built-in security rules<\/li>\n
- Context Limits:<\/strong> Limited memory for long conversations<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n
Enterprise Insight:<\/strong> In an enterprise setting, LLMs act as reasoning engines. They summarize reports, explain complex topics, draft emails, and answer questions in natural language. This is why LLM integration architecture is so important in AI system design.<\/p>\n<\/div>\nLLMs are powerful, but they are not perfect. One major limitation is hallucination. This means the model can generate answers that sound confident but are not correct. Another challenge is that LLMs do not know your company data unless you connect it. They also do not automatically understand compliance or security rules.<\/p>\n
Because of these limits, enterprises cannot rely on LLMs alone. They need additional layers to make AI safe and useful.<\/p>\n<\/div>\n
<\/p>\n
\n\n\ud83d\udd0d<\/div>\nRetrieval Augmented Generation and Why It Matters<\/h2>\n<\/div>\n
Retrieval Augmented Generation, or RAG, solves one of the biggest problems of LLMs. It grounds AI responses in real enterprise data, improving accuracy, reducing hallucinations, and supporting compliance.<\/p>\n
<\/p>\n
\nRAG Architecture Flow<\/h3>\n\n\n1<\/div>\nUser Query<\/div>\nNatural language question<\/div>\n<\/div>\n\n2<\/div>\nSemantic Search<\/div>\nAcross vector databases<\/div>\n<\/div>\n\n3<\/div>\nRetrieve Context<\/div>\nRelevant enterprise data<\/div>\n<\/div>\n\n4<\/div>\nLLM Generation<\/div>\nGrounded in retrieved data<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/p>\n
\n\nRAG Architecture<\/h3>\n\n- \u2713<\/span>Keeps model general<\/li>\n
- \u2713<\/span>Connects to fresh data<\/li>\n
- \u2713<\/span>More flexible & easier to maintain<\/li>\n
- \u2713<\/span>Reduces hallucinations<\/li>\n
- \u2713<\/span>Supports compliance<\/li>\n<\/ul>\n<\/div>\n\n
Fine-Tuning Architecture<\/h3>\n\n- \u26a1<\/span>Changes model itself<\/li>\n
- \u26a1<\/span>Requires more time & cost<\/li>\n
- \u26a1<\/span>Complex maintenance<\/li>\n
- \u26a1<\/span>Model can become outdated<\/li>\n
- \u26a1<\/span>Higher technical expertise needed<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
In a typical RAG flow, the system performs a semantic search across vector databases where enterprise data is stored as embeddings. This retrieval step ensures that the most contextually relevant information is selected, rather than relying on simple keyword matches. The retrieved trusted data is then passed to the LLM, which generates a response grounded in this enterprise knowledge.<\/p>\n
This approach improves accuracy, reduces hallucinations, and supports compliance. It also keeps sensitive data inside the enterprise environment.<\/p>\n
Many organizations compare RAG vs fine tuning architecture. Fine tuning changes the model itself and requires more time and cost. RAG keeps the model general and connects it to fresh data. For most enterprises, RAG is more flexible and easier to maintain.<\/p>\n
If you are asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n
\nDecision Guide:<\/strong> For most enterprises, RAG is more flexible and easier to maintain. If you’re asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n<\/div>\n<\/div>\n\n\n\ud83e\udd16<\/div>\nAI Agents and Autonomous Systems<\/h2>\n<\/div>\n
AI agents represent the next step in the evolution of Generative AI. Unlike traditional chatbots, agents do more than simply respond to user queries. They can plan tasks, make decisions, take actions, and interact autonomously with tools, systems, and APIs to achieve specific goals.<\/p>\n
<\/p>\n
\nAI Agent Architecture Components<\/h3>\n\n\n\ud83c\udfaf<\/div>\nGoal\/Objective<\/div>\nClear task or outcome to achieve<\/div>\n<\/div>\n\n\ud83e\udde0<\/div>\n
Enterprise Insight:<\/strong> In an enterprise setting, LLMs act as reasoning engines. They summarize reports, explain complex topics, draft emails, and answer questions in natural language. This is why LLM integration architecture is so important in AI system design.<\/p>\n<\/div>\n LLMs are powerful, but they are not perfect. One major limitation is hallucination. This means the model can generate answers that sound confident but are not correct. Another challenge is that LLMs do not know your company data unless you connect it. They also do not automatically understand compliance or security rules.<\/p>\n Because of these limits, enterprises cannot rely on LLMs alone. They need additional layers to make AI safe and useful.<\/p>\n<\/div>\n <\/p>\n Retrieval Augmented Generation, or RAG, solves one of the biggest problems of LLMs. It grounds AI responses in real enterprise data, improving accuracy, reducing hallucinations, and supporting compliance.<\/p>\n <\/p>\n <\/p>\n In a typical RAG flow, the system performs a semantic search across vector databases where enterprise data is stored as embeddings. This retrieval step ensures that the most contextually relevant information is selected, rather than relying on simple keyword matches. The retrieved trusted data is then passed to the LLM, which generates a response grounded in this enterprise knowledge.<\/p>\n This approach improves accuracy, reduces hallucinations, and supports compliance. It also keeps sensitive data inside the enterprise environment.<\/p>\n Many organizations compare RAG vs fine tuning architecture. Fine tuning changes the model itself and requires more time and cost. RAG keeps the model general and connects it to fresh data. For most enterprises, RAG is more flexible and easier to maintain.<\/p>\n If you are asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n Decision Guide:<\/strong> For most enterprises, RAG is more flexible and easier to maintain. If you’re asking when to use RAG vs AI agents, RAG is best when the goal is to provide correct, explainable answers from trusted sources.<\/p>\n<\/div>\n<\/div>\n AI agents represent the next step in the evolution of Generative AI. Unlike traditional chatbots, agents do more than simply respond to user queries. They can plan tasks, make decisions, take actions, and interact autonomously with tools, systems, and APIs to achieve specific goals.<\/p>\n <\/p>\nRetrieval Augmented Generation and Why It Matters<\/h2>\n<\/div>\n
RAG Architecture Flow<\/h3>\n
RAG Architecture<\/h3>\n
\n
Fine-Tuning Architecture<\/h3>\n
\n
AI Agents and Autonomous Systems<\/h2>\n<\/div>\n
AI Agent Architecture Components<\/h3>\n
![\"How](\"https:\/\/dm.impressicocrm.com\/impressico\/wp-content\/uploads\/2026\/01\/How-LLMs-RAG-and-AI-Agents-Work-Together.webp\")
<\/p>\n<\/div>\n