Empowering with a Vision | Education 4.0 Enabler | Building Ed-Tech Global Leaders | Offline/Online Education Genius | Management Guru | Education Innovation | Transforming Education | Loving my Edupreneurial Journey

I am a management aficionado with over 25 years of expertise in Education,Research,Strategy,Communication and Project Management.
Thinking outside the box comes naturally to me as I truly believe ‘Ordinary is Insipid’.
Building Global Intellectual Capital is what I strive to achieve.
Further,I believe Authentic Philanthropy is the need of the hour and I wish to spread the JJ Foundation tenet to Educate-Enable-Empower!

Prof.Gaganjit Singh

Disclaimer

All views are strictly my own. I do not speak on behalf of my employer. My content focuses on publicly available knowledge, frameworks, and industry trends that help advance the developer and AI community.

I am an AI Architect and Enterprise Architecture leader with 16+ years of experience, focused on Generative AI, Agentic AI, and enterprise-scale AI platforms, with a strong foundation in classical machine learning and data-driven systems.

In my current role at Wells Fargo, I work within Enterprise Architecture, designing scalable, secure AI and data platforms across the bank’s global footprint. My work spans classical ML, Generative AI, and Agentic systems, applying advanced LLMs, multi-agent orchestration, and AI governance frameworks alongside traditional ML models to real-world financial services use cases in a regulated, production environment.

I specialize in architecting end-to-end AI systems—from feature engineering, model training, and evaluation to RAG pipelines, agentic decision layers, tool-using agents, and autonomous workflows. My architectures integrate classical ML techniques (regression, classification, clustering, time-series forecasting, anomaly detection) with modern LLM-based systems, enabling robust, explainable, and scalable AI solutions.

My work includes cloud and data engineering on AWS, Azure , GCP, designing resilient, high-performance architectures using modern data stacks, while ensuring security, compliance, governance, and operational excellence across on-prem, hybrid, and multi-cloud environments.

Beyond delivery, I provide technical and architectural leadership across product, engineering, and business teams—aligning enterprise AI strategy with hands-on execution and translating emerging AI capabilities into measurable business outcomes.


Core Focus Areas:

Generative AI & Agentic Systems
LLMs, Multi-Agent Architectures, Agent Orchestration, RAG, Tool Calling, Autonomous Workflows, AI Reasoning & Planning

Classical Machine Learning
Regression, Classification, Clustering, Time-Series Forecasting, Anomaly Detection, Feature Engineering, Model Evaluation

Enterprise AI Platforms & MLOps
Production ML & LLM Systems, Model Governance, MLflow, CI/CD for AI, Monitoring & Observability, Responsible AI

Architecture & Leadership
Enterprise Architecture, AI Strategy, Technical Roadmapping, Cross-functional Collaboration, Team Mentorship

Brij Pandey

Sometimes you have to draw it out to see the big picture. 

The AI landscape can get overwhelming with new terminology dropping every day. I find it helpful to visualize it as layers of capability.

We started with Classical AI (Rules), moved to Deep Learning (Patterns), exploded with Generative AI (Creation), and are now entering the era of Agentic AI (Action).

The top layer is where the magic happens next—giving AI the ability to plan and use tools.

A simple sketch to explain a complex evolution.

Brij kishore Pandey

Many people talk about “Agentic AI” like it’s just GenAI + tools.

This diagram shows the full stack — and why most agent projects fail above the model layer, not inside it.

Here’s the mental model 

Think of it as 5 concentric layers of capability:
1) AI & ML (decisions)
 Classical learning: supervised / unsupervised / RL — systems that turn data into predictions.

2) Deep Learning (representation)
 Neural networks + transformers — the machinery that learns features and patterns at scale.

3) GenAI (generation)
 LLMs + multimodal generation — content/code creation, RAG, ASR/TTS, image/video/audio.

4) AI Agents (execution)
 This is where GenAI becomes operational:planning (ReAct / CoT / ToT)
tool use & orchestration (actions/plugins)
context management (state + history)
human-in-the-loop (oversight when stakes are high)

5) Agentic AI (autonomy at scale)
 The “real” production layer people underestimate:
governance, safety & guardrails
observability & tracing (debuggability > demos)
memory governance (retention policies, consent, compliance)
rollback + failure recovery
cost/resource management
multi-agent coordination and handoff protocols
The big takeaway
Models don’t create reliable autonomy. Systems do.

If your agent can’t be audited, traced, rolled back, and constrained, it’s not “agentic” — it’s a risky script with confidence.
If you’re building agents in 2026, shift your question from:

“Which model/framework should I use?”
 to
 “How will this system behave under partial context, tool failure, ambiguity, and conflicting goals?”

That’s where real architecture begins.

Stop structuring your Agentic AI projects like standard GenAI apps. 

When we were building simple RAG apps in 2023, a utils folder and app. py were enough. 

But Agentic AI requires a fundamental shift in architecture.

This cheat sheet outlines the gold standard for robust agent systems.
Key differences to notice:

The core/ Directory: Agents need persistent state. You'll see explicit modules for memory. py and decision_maker. py.

The environment/ Directory: Agents don't just talk; they interact. You need a simulation layer.

Collaborative Patterns: The structure supports multi-agent systems right out of the box (collaborative_agent. py).

If you are building autonomous systems this year, bookmark this structure.

Which folder do you find the most difficult to implement correctly? 

For me, it's always the memory management.

It’s not Coding vs No-Coding anymore.

It’s Coding vs Vibe-Coding vs No-Coding — and each exists for a reason.

Coding
 You write logic line by line.
 You control architecture, performance, scalability, security.
 This is where deep engineering lives — and always will.

No-Coding
 You assemble systems visually.
 Drag-and-drop workflows, automations, business logic.
 Perfect for speed, internal tools, and non-engineering teams.

Vibe-Coding (the new layer)
 You describe intent.
 AI writes, refactors, debugs, and integrates.
 You still think like an engineer — but you type less and design more.

The mistake I see people making:
 👉 Treating these as replacements instead of layers.

Modern builders don’t choose one.
They combine all three.
 • Code for core systems
 • Vibe-code for velocity
 • No-code for workflows and operations

The real skill in 2026 isn’t “learning another framework.”

 It’s knowing when to use which abstraction level.

How to become an AI Engineer in 2026 (without getting lost)

Everyone wants to become an AI Engineer in 2026.
But most people are learning the wrong way.

They spend months:

-watching tutorials
-collecting certificates
-trying 25 tools
-copying notebooks from GitHub

And still can’t build even one production-ready AI system.

Because AI Engineering in 2026 is not about “knowing AI tools”.
It’s about building AI systems like an engineer.

Here’s the real roadmap.

Step 1: Learn Engineering first (AI comes later)
Before LLMs, agents, and fine-tuning…
You need fundamentals:
Python (clean code, OOP, async basics)
Data structures & algorithms (not LeetCode obsession, just core thinking)
APIs (FastAPI), JSON, auth, retries
Git + testing
If you can’t ship an API with tests, you’re not ready for production AI.

Step 2: Understand ML like a system, not a subject
You don’t need PhD math.
But you must understand:
supervised vs unsupervised learning
overfitting, bias/variance
evaluation metrics
feature engineering mindset
Because the biggest AI failure is not model quality.
 It’s wrong evaluation.

Step 3: Become strong in data + retrieval (this is where jobs are)
In 2026, most AI Engineering work will still be:
pipelines
ingestion
chunking
embedding strategy
retrieval + ranking
RAG is not “upload PDFs and chat”.
Real RAG requires:
metadata strategy
hybrid search
reranking
caching
observability
If you learn this properly, you become employable fast.

Step 4: Learn LLM application architecture (the real differentiator)
Stop thinking “prompt”.
Start thinking:
prompt templates and versioning
function calling/tool use
structured outputs
guardrails
cost control (token budgets)
failure handling
In production, your AI system must survive:
bad queries
empty context
hallucinations
rate limits
partial tool failures
That’s engineering.

Step 5: Master Agentic AI (but only after Step 4)
Agents are the future.
But agents without fundamentals are just demos.
Learn:
planning vs execution
memory patterns
multi-agent orchestration
human-in-the-loop workflows
tool reliability
Your goal is not “cool agents”.
Your goal is predictable automation.

Step 6: Build a portfolio like a real AI engineer
Not 20 small projects.
Build 3 serious systems:
Project 1: Production-grade RAG
ingestion
embeddings
hybrid retrieval + reranking
eval harness
Project 2: AI Workflow Agent
multi-step tool execution
retries and guardrails
structured output
Project 3: AI Observability + Cost Dashboard
tracing
latency and token metrics
evaluation and quality reports
This portfolio beats certificates every time.

The truth:
In 2026, “AI Engineer” doesn’t mean model researcher.
It means someone who can:
ship AI into real products
make it reliable
make it measurable
make it cost-efficient
If you can do that,
you won’t just get hired.
You’ll become hard to replace.

Most people start building AI projects by writing . py files.

But sustainable, production-ready generative AI isn’t built on code alone.
It’s built on structure.

The biggest reason AI projects struggle isn’t lack of model accuracy —it’s poor architecture, organization, and maintainability.

So I built a structured Generative AI Project Template →
 designed for scalability, modularity, and real-world production use.

This isn’t just a folder layout.

It’s a blueprint for building AI systems that don’t collapse as they grow.

Github Link in comment -

I want to learn GenAI / Agentic AI, but I don't know the first step

The most common question I get isn't "What can AI do?" but rather "Where do I actually start?"

The landscape is moving fast. Yesterday it was RAG, today it’s Multi-Agent Systems, Tomorrow it’s something else. 

If you are trying to figure out your Day 1, I created this roadmap to clear the confusion.

How to use this map to get started:

➛ Start with the "Why" (Section 1): Understand the shift from Output (GenAI) to Outcome (Agentic AI). If you don't need autonomy, you might not need an Agent. 

➛ Grasp the Core (Section 2): Before writing code, learn the concepts. What is a Reasoning Loop? How does Short-term vs. Long-term memory work in an LLM context? 

➛ Pick ONE Framework (Section 3): Don't try to learn them all. Pick LangGraph if you want control, or CrewAI if you want to orchestrate a team. Stick to one until you build your first POC.

Don't let the complexity paralyze you. Just follow the line.

AI Architect | AI Engineer | Generative AI | Agentic AI

Traditional AI vs Agentic AI vs Agentic RAG

Most conversations mix these up. They’re not the same evolution step.

Let’s break it down simply 

𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗦𝘁𝗮𝘁𝗶𝗰 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲)

This is the classic ML setup most of us started with:

 • Define the task

 • Collect & preprocess data

 • Train a model

 • Deploy

 • Predict

 • Evaluate

It’s linear and one-way.

Once deployed, the model doesn’t think, adapt, or re-plan.
If data or conditions change → you retrain offline.

𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: stable, well-defined problems
𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻: no real-time learning or adaptation

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 (𝗚𝗼𝗮𝗹-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀)

Now things change.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜:

 • Sets objectives

 • Chooses models

 • Plans multi-step actions

 • Uses tools & APIs

 • Executes decisions

 • Learns from feedback

This is not “prompt → response”.
It’s plan → act → observe → refine.

Agents behave more like operators, not predictors.

𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: workflows, automation, decision-making systems
𝗞𝗲𝘆 𝘀𝗵𝗶𝗳𝘁: autonomy + reasoning loops

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗖𝗼𝗻𝘁𝗲𝘅𝘁)

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 adds the missing layer: memory & knowledge evolution.

It combines:

 • Agentic planning

 • Semantic + vector search

 • External knowledge bases

 • Long-term memory

 • Continuous learning

Now the system:

 • Remembers past interactions

 • Writes back validated results

 • Improves future responses

 • Adapts with context, not just prompts

This is where AI starts behaving like a context-aware system, not a chatbot.

The real shift isn’t:

 • Bigger models
 • Better prompts

The real shift is:

 • From pipelines → loops
 • From responses → decisions
 • From retrieval → reasoning + memory

Question for you:

Did I miss any critical layer in this evolution?

Where do you see the biggest adoption gap today?

Let’s discuss.

The Generative AI space is evolving rapidly from simple request-response interfaces to true "Agentic Systems." 

But drawing a box labeled "AI Agent" on a whiteboard is easy. 

Building one that works reliably in production is incredibly complex.

I’ve been refining this System Design template to capture the necessary components for building robust, goal-oriented AI.

Key Insights from this architecture:

The LLM is just the engine; it's not the car. To make AI truly useful, you need a massive amount of supporting infrastructure:

The "Brain" needs Structure: Notice the distinction between the Task Planner (breaking down goals) and the Agent Instances (executing the work). Autonomy requires separating strategy from tactics.

Memory is Multi-Tiered: A simple vector DB isn't enough. We need short-term context and long-term structured knowledge bases to maintain state across complex workflows.

The Feedback Loop is Crucial: The "Tool Use Feedback Loop" and "Validation Service" are what differentiate a hallucinating demo from a reliable product. 

The system must be able to perceive its own mistakes and correct course.

We are moving from asking AI questions to assigning AI responsibilities. This requires serious system design.

What is the hardest part of this diagram to implement in your experience?

Real Agentic AI is a layered system — and each layer solves a different class of problems (accuracy, safety, autonomy, compliance, operations).

Here’s a clean way to visualize it 👇

Agentic AI Layers (from core → enterprise-grade)
1) LLM (Core Reasoning Layer)
 This is the foundation: language + reasoning + generation.
 But by itself, it’s not reliable for enterprise work.

2) RAG (Grounding Layer)
 RAG upgrades the LLM from “guessing” to “retrieving”.
 It pulls relevant context at runtime so outputs are grounded in real data.

3) Knowledge Base (Enterprise Context Layer)
 The KB is what gives RAG meaning: structured/unstructured knowledge, semantic consistency, governance boundaries.

4) Ethics & Safety Layer
 This is not optional.
 You need safety filters, policy checks, redaction, PII controls, and guardrails before output becomes action.

5) Interaction Interface Layer
 The bridge between the user/system and the model.
 This is where UX meets intelligence: prompts, memory, conversation state, function calling patterns.

6) External Interactions Layer
 This is where “agent” becomes real: APIs, tools, workflows, automations, external services.

7) Operational Independence Layer
 Autonomy.
 The agent decides what to do, when to do it, and how to sequence actions — without needing humans for every step.

8) Governance & Auditability Layer
 The layer most teams forget until it’s too late.
 Logs, traceability, lineage, evaluations, approvals, decision tracking.

9) Regulated AI Layer
 Where agentic systems become enterprise-ready: ethical operation + legal compliance + regulatory alignment + transparency.

The takeaway:
If your system has only LLM + tools, you don’t have Agentic AI.

You have a prototype.
Real Agentic AI requires architecture.

If you look at the current state of AI, it’s easy to get lost in the buzzwords. 

But when you visualize it like this—layer by layer—it becomes clear that Agentic AI isn't a replacement for what came before.

Building a production-grade Retrieval-Augmented Generation (RAG) system today feels less like coding and more like orchestrating a complex supply chain.

It’s not just about choosing between GPT-5 or Claude anymore. 

It’s about every step of the data journey.

Here is the breakdown of the modern stack:

1. Data Extraction (The Foundation) :Garbage in, garbage out. Tools like FireCrawl and Docling are becoming just as critical as the model itself because if you can't parse the web or complex PDFs cleanly, your context window is useless.

2. The "Open" vs. "Closed" Debate 
LLMs: We are seeing a massive surge in open weights. Llama 3.3, Phi-4, and DeepSeek are challenging the closed giants like OpenAI and Gemini.
Embeddings: The same split applies here. Using Voyage AI for ease or running Ollama/Nomic locally for privacy?

3. The Vector Layer :The memory of the system. Whether it's Pinecone for managed services or Qdrant/Milvus for control, this layer defines your retrieval speed and accuracy.

4. The Orchestrators :LangChain and LlamaIndex continue to be the glue holding it all together, though simpler, custom pipelines are gaining traction too.

5. Evaluation (The Missing Link) : You can't improve what you don't measure. Integrating tools like Ragas and Giskard is the only way to move from "it feels right" to "it works."

The stack is getting deeper, but the tools are getting better. 

The biggest shift I'm seeing is the move toward local/private stacks (Ollama + Open Models) for enterprise data security.

Everyone is talking about AI Agents.

Almost no one is talking about what actually runs them.
This diagram shows the truth:
Agentic AI is not a model.
It is a full-stack system.

If you remove any one of these layers, your “agent” quietly breaks:

→ Models reason
→ Runtimes execute
→ Protocols coordinate
→ Orchestrators plan
→ Tools act
→ Applications interact
→ Governance keeps it safe

Most failed agent projects don’t fail because of bad prompts.
They fail because one of these layers was missing.
An agent without memory is just a chatbot.
An agent without orchestration is just a script.
An agent without protocols is just a silo.
An agent without observability is a liability.
Real Agentic AI is closer to an operating system than a feature.

We’re moving from:
LLMs → Workflows → Agents → Multi-Agent Systems → AI Control Planes

If you’re still thinking in terms of “calling a model”, you’re already behind.

The future belongs to teams who build agent infrastructure, not just agent demos.

Save this.

This stack is how autonomous systems will actually be built.

If you look at the evolution of AI, it looks a lot like a set of building blocks. 
For years, we focused on the foundation: the math, the logic, and the pattern recognition.

But looking at this stack, the shift at the very top is what should have everyone’s attention right now.

We are currently graduating from the Pink Layer (Generative AI) to the Purple Layer (Agentic AI).

Here is the breakdown of the climb:

The Foundation (Classical & ML): This is where we taught computers to understand rules, logic, and patterns. It was about classification and prediction.
The Explosion (Generative AI): This has been the reality for the last two years. LLMs, Diffusion Models, and VAEs gave us the power to create—text, code, images, and video.
The Frontier (Agentic AI): This is the new peak. Notice the difference in keywords in that top purple layer: "Planning," "Tool Use," "Autonomous Execution."

We are no longer just asking models to generate a poem or an email. We are building systems that have agency. 

Systems that can:
Retain Memory of past interactions.
Plan a multi-step workflow.
Execute tasks by using external software tools.

Agentic AI is not  LLMs + tools + a loop.

What you’re seeing in this whiteboard is the real system design behind production-grade Agentic AI.

Agentic AI is not a single model decision.

It’s an architecture problem.

A few things this diagram makes clear:

 • You don’t “call an agent” — you orchestrate planning, execution, memory, tools, and validation
 • Agents without planners drift
 • Agents without memory repeat themselves
 • Agents without feedback loops hallucinate confidently
 • Agents without orchestration don’t scale

The hardest parts are not:
 – Which LLM you use
 – Which framework you pick

The hardest parts are:
 – Task decomposition
 – State & memory management
 – Tool reliability
 – Control vs autonomy
 – Feedback, validation, and recovery

That’s why most “agent demos” look impressive…
and most agent systems fail in production.

Real Agentic AI looks like this:
 Client → Orchestration → Planning → Agents → Tools → Memory → Validation → Observability
 …with feedback loops everywhere.

Models reason.
Systems decide.

If you’re building Agentic AI:

Start thinking like a systems architect, not a prompt engineer.

Once you move past demos, “agent = LLM + tools” stops being useful because production systems demand coordination, control, and feedback.

What you’re really building is a distributed system with intelligence embedded at every layer.

This diagram is how I now think about an Agentic AI System in practice 

Not as one smart model —
but as coordination, control, memory, feedback, and accountability working together.

A few things this architecture makes explicit:

• Agents don’t replace systems — they sit inside them
 Load balancers, gateways, queues, planners, executors… all still matter.
• Planning is a first-class citizen
 Goal decomposition, plan storage, re-planning loops — this is where most “agent demos” quietly fail in production.
• Memory ≠ just a vector DB

 Short-term context, long-term knowledge, feedback loops, validation signals — memory is a system, not a feature.

• Tools need governance
 Who can call what, when, with which constraints — otherwise autonomy turns into chaos.
• Feedback closes the loop
 Without validation, monitoring, and learning from execution… you don’t have agents.

You have expensive retries.
This is why Agentic AI feels hard.

Because it’s not a prompt problem.
It’s a systems design problem.

It’s 2026.
AI is no longer “emerging tech.”

It’s infrastructure.

Everyone has access to models.
Everyone has access to copilots.
Everyone has access to automation tools.

So what separates the winners now?

Not who uses AI.
But who can build AI systems that produce outcomes.

That’s why I put together this infographic: the 12 AI skills that actually matter in 2026 — not hype, not theory.

➠AI Agents – autonomous execution, not chatbots
➠Agentic AI – planning, self-correction, multi-step reasoning
➠RAG – AI connected to private/company knowledge
➠Workflow Automation – AI → real ROI workflows
➠Prompt Engineering – structured instruction design for systems
➠LLM Management – evals, cost, latency, reliability, monitoring
➠AI Tool Stacking – building connected AI workflows across tools
➠Multimodal AI – text + image + audio + video understanding
➠AI Content Generation – scalable content pipelines, not one-off posts
➠AEO / GEO – optimizing content for AI answer engines
➠AI Integrations & APIs – builders beat users
➠Autonomous Workflows – self-running operations, minimal supervision

2026 isn’t rewarding people who “know AI tools.”

It’s rewarding people who can:

 ➠design workflows
 ➠engineer agent behavior
 ➠connect AI to real systems
 ➠measure and improve outputs

taplio

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Brij Pandey

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