Ankit Kumar

Workforwin

Chief Executive Officer

Hiron AI

Connecting talent with opportunities worldwide. Whether you're looking for your next role or your next hire, Hiron AI brings everyone together.

Director

Solitera  Limited 

Product Manager

Associate Product Manager

Making education affordable and practical

Founder

Product Manager || ❤️ System Design || ✍️ Python

I’m Ankit Kumar, a product-first founder on a mission to reinvent how hiring works for job seekers and recruiters. As the founder and CEO of Hiron AI, I'm leading the creation of an AI-powered hiring ecosystem where candidates don't apply; they get discovered.

💡 Why Hiron AI?
Because resumes, job boards, and mass applications are outdated.
We’re building a future where talent is matched instantly, hiring is seamless, and every fresher or pro user gets a real shot at growth.

From building healthcare and education platforms to scaling AI-driven products, my work has always centered around solving real problems through clean, smart, and human-first tech.

🔧 My Core Strengths
 — Building AI-first B2B & B2C products from scratch
 — Leading cross-functional teams through execution and growth
 — Turning complex user problems into simple, elegant solutions
 — Staying deeply technical — Python, Cloud, ML, AI

🌱 Outside of building products
I teach. I share. I grow communities.
Helping new developers and product minds thrive is something I care deeply about.

⚽ Also, football is my second love. Strategy, speed, and teamwork — on and off the field.

🧠 Let’s build what the future needs, not what the past made popular.
Follow along as we shape the hiring world for 2035 — today.

📬 Feel free to connect, collaborate, or just say hi.

Project Team Lead

Wells Fargo

While my formal title is Executive Director (a banking band level), my work is hands-on architecture and engineering:

Enterprise Architecture – designing scalable AI and data platforms across the bank’s global footprint.

Generative AI & Agentic Systems – applying advanced LLMs, multi-agent orchestration, and AI governance frameworks to real-world financial use cases.

Cloud & Data Engineering – building secure, resilient, and high-performance architectures leveraging GCP and modern data stacks.

Architecture Leadership – collaborating with product, engineering, and business teams to align enterprise strategy with technical execution.

Principal Architect - Generative AI ( Executive Director)

↳ Designed a Hybrid RAG system combining vector search and keyword-boosted retrieval, increasing knowledge discovery accuracy by over 60%

↳ Led prompt engineering, Fine-tuning, and backend integration for production-grade GenAI services

↳ Optimized Python-based data processing pipelines, boosting throughput by 45% and enabling real-time analytics

↳ Reduced infrastructure costs and system downtime through cloud-native engineering using AWS Lambda, Fargate, and SQS

↳ Embedded explainability, bias detection, and privacy-aware design into AI services to ensure responsible AI practices

↳ Built end-to-end MLOps pipelines and CICD workflows to enable rapid iteration and secure deployment

↳ Hands-on coding, writing infrastructure-as-code, and mentoring engineering teams on LLMOps and agentic system design

↳ Collaborated across cross-functional teams to align AI innovation with enterprise goals and operational reliability

Principal Engineer

Packt

Authored the groundbreaking technical book "Building ETL Pipelines with Python," filling a critical gap in the data engineering literature.

- Authored pioneering technical book on production-grade ETL pipelines using Python, addressing a critical gap in data engineering literature
- Focused on scalable architectures for petabyte-level data processing, bridging academic theory and industry practices
- Provided comprehensive, hands-on guide with advanced ETL topics, real-world applications, and practical case studies
- Empowered data professionals with actionable insights for enterprise-level data infrastructures
- Established new benchmark in practical, industry-oriented literature for Python-based ETL solutions

Technical Author

Led complex software development projects, focusing on cloud-based solutions and microservices architecture.

Key Achievements:
- Spearheaded the migration of monolithic applications to microservice-based architecture using AWS, Python, Go, and Flask, significantly improving system scalability and maintainability
- Developed and implemented production-grade Proof of Concepts (POCs) for innovative solutions
- Designed and built high-performance, scalable RESTful APIs using Python (Flask) and AWS API Gateway, enhancing enterprise application capabilities
- Orchestrated efficient data pipelines leveraging AirFlow, Dask, Pandas, and Numpy for large-scale data processing

Data Engineer

Pioneered data processing and ETL solutions, focusing on big data technologies and cloud-based data services.

Key Achievements:
- Architected and implemented a sophisticated data pipeline using Airflow, Kafka, and Python, capable of processing billions of records with limited computational resources
- Developed a persistent Data as a Service (DaaS) system, ensuring 24/7 data availability for users and critical business operations
- Optimized data processing workflows, significantly reducing processing time and resource utilization

Technical Contributions:
- Leveraged advanced technologies including PySpark, Kafka, and Elasticsearch for efficient big data processing
- Implemented microservices architecture using Go, RPC, AMQP, and Nameko, enhancing system modularity and scalability
- Utilized containerization and orchestration technologies like Docker and Kubernetes for streamlined deployment and management
- Employed cloud technologies including AWS Lambda, ECS, Elasticache, DynamoDB, and API Gateway to build robust, scalable data solutions

Python Data Engineer at Cigna

Cognizant

Key Contributions:
- Architected and developed a distributed, robust Crew Management System using Python, Django, and RabbitMQ
- Implemented real-time data processing and visualization, enhancing decision-making capabilities for administrators
- Designed efficient data pipelines and analytics workflows using Pandas, Numpy, and Matplotlib
- Optimized database operations through complex SQL queries and stored procedures
- Developed full-stack solutions, integrating Python backend with JavaScript frontend

Full Stack Engineer (Python) at Alaska Airlines

- Led development of IVoRI (Independent Valuation Reporting Infrastructure), a strategic reporting application, using Python, WxPython, and Flask
- Engineered efficient data pipelines and migrated complex ETL processes from Informatica to Python (Pandas), significantly improving system performance
- Implemented Test-Driven Development (TDD) practices and enhanced unit test coverage, elevating overall code quality
- Collaborated with cross-functional teams and clients to deliver mission-critical financial applications, troubleshoot issues, and implement enhancements
- Technologies: Python, WxPython, Flask, Pandas, SQL (Sybase), NoSQL, Groovy, JavaScript, Shell Scripting

Senior Python Developer at JPMorgan Chase & Co.

- Led development of the Amex Personalization Utility, a strategic project transforming product and service marketing to clients, using Java and Python
- Engineered automated solutions for data-related tasks and content management, saving 80 man-hours per week and streamlining seasonal card offer updates
- Developed Employee Roster Management system for 3M Company as a full-stack developer, utilizing Java, JavaScript, Spring, HTML, and CSS
- Implemented code coverage tasks and monitored thousands of lines of code to ensure robust software operations
- Cultivated strong client relationships, maintaining open communication channels for frequent interaction and progressive status updates

Backend Engineer at Amex

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

I am an AI Architect and Principal Engineer with 15+ years of experience building scalable, intelligent systems using Python, Go, Cloud platforms, Databricks, and AI.

At ADP, I lead the design and development of cloud-native, microservices-based applications and AI-driven platforms. I architect end-to-end solutions combining ETL pipelines, LLMs, and real-time analytics, collaborating cross-functionally with product, SRE, and security teams to deliver business-critical innovations.

As an AI strategist, author, and technology mentor, I specialize in applying Large Language Models, MLOps, and modern AI architectures to solve real-world problems. I’m passionate about creating value through intelligent automation, empowering teams, and driving enterprise transformation with AI.

Core Skills & Expertise

AI & ML
Generative AI, LLMs, LangChain, LangGraph, LlamaIndex, MLOps, AI-driven Analytics, MLflow, TensorFlow

Cloud & Platforms
AWS (Lambda, ECS, ElastiCache, DynamoDB, API Gateway), Azure, GCP, Databricks

Programming & Frameworks
Python, Go, JavaScript | Django, Flask, FastAPI

Data Engineering
Airflow, Spark, Pandas, NumPy | ETL/ELT Pipeline Architecture

Databases
RDBMS, NoSQL, GraphDB, Vector DBs

DevOps & Architecture
Microservices, Docker, CI/CD, API-first Architecture

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

Tools
GitLab, Bitbucket, Jira, Copilot, Postman, Splunk

Brij kishore Pandey

If you’re still feeding your LLMs with plain JSON, you’re leaving tokens, speed and budget on the table.

TOON launched last week — and it’s a game changer for anyone building with LLMs

TOON (Token-Oriented Object Notation) is the serialization format built for the era of large context, agentic workflows and multi-tool LLM systems. 

Here’s what TOON delivers:

Token efficiency: Up to 30-60% fewer tokens than formatted JSON for uniform arrays. 

LLM-friendly structure: Explicit lengths and field sets boost parsing and retrieval accuracy. 

Minimal syntax: No excess braces, quotes or repeated keys — cleaner input for faster comprehension. 

Seamless JSON compatibility: Spend in JSON, push to TOON for LLMs, convert back when needed. 

❓ When to use TOON

Ideal for large datasets with uniform object arrays (e.g., telemetry logs, user lists, product catalogs)

Less optimal for deeply nested, non-uniform structures — JSON might still win there. 

If you build agents, copilots, or any LLM system that sends structured data into a prompt — seriously, make TOON your new standard. 

Your token budget, model throughput and reliability will thank you.

AI Architect | Strategist | Generative AI | Agentic AI

RAG is no longer a “technique.”

It’s a whole ecosystem — and a developer’s stack.

Over the past 18 months, Retrieval-Augmented Generation (RAG) has quietly evolved from a simple “search + LLM” pattern into a complete engineering discipline.

Today, building AI systems isn’t just about choosing a model.

It’s about architecting the entire pipeline — from extraction to embeddings, from vector databases to evaluation, from closed models to open-source innovators.

Let me tell you about two students: Arjun and Meera.

Both are smart.

Both want to ace their exams.

But they have completely different strategies.

Arjun: The Retriever (RAG)

Arjun’s method?

He builds a giant notebook —

filled with summaries, notes, and past lessons.

Whenever he needs to answer something:

 -He quickly flips through his pages
 -Finds relevant notes
 -Connects the dots
 -And gives his answer

Efficient. Organized. Reliable — as long as his notes are updated.
But sometimes…

If his notes didn't cover a new topic,

he has to go back, learn, and re-index them
before answering confidently.

That’s RAG: Retrieval-Augmented Generation
 → fetch relevant context → answer accurately.

Meera: The Context Thinker (CAG)

Meera prepares differently.

She doesn’t just store notes —
she builds a deep understanding of context.
She learns how subjects relate,
 keeps frameworks in mind, and knows how to adapt knowledge.

When a new question comes, she:

- Understands the situation
- Injects domain context
- Merges it with prior understanding
- Cross-checks consistency
- Enriches her response with nuance

Her answers feel more natural, human-like, situationally aware.

That’s CAG: Context-Augmented Generation→ infuse domain intelligence → synchronize context → generate richer responses.

The Difference in One Line

RAG retrieves facts.

CAG understands situations.

RAG is your library.

CAG is your commonsense + domain intuition engine.

Both have strengths.

Winners combine them.

RAG built the bridge.
 CAG will accelerate the future.

 The Future Stack

 --RAG for truth
 --CAG for intelligence
 --Memory for continuity
 --Agents for action
 --Feedback loops for improvement

AI is growing not just smarter — but contextually aware.

Picture Credit - Rahul Agarwal

Most Generative AI projects don’t fail because of weak models — they fail because the engineering foundation is weak.

Teams often begin with prompts, APIs, and notebooks.

But when it’s time to scale, integrate, audit, or productionize, everything starts to break.

What really decides if your AI project moves from demo to product?

How well it is structured from day one.

The visual you see is a structured Generative AI project layout.

It covers modularity, maintainability, configuration management, prompt engineering, token usage, and logging.

It helps solve common challenges like:
 • Unstructured notebooks and prototype chaos
 • Hardcoded prompts and model settings
 • No token tracking, caching, or rate limiting
 • Mixing prompt logic with core application code
 • Difficulty moving from proof-of-concept to production

What this structure includes effectively:
 • YAML-based configurations for prompts, models, and logging
 • Modular LLM clients (GPT, Claude, Base)
 • Prompt engineering utilities: templates, chaining, few-shot design
 • Token counting, caching, logging, and rate limiters
 • Separate example scripts and experimental notebooks
 • Clear separation between utilities, handlers, and core logic

What is not included — but should be added in any production-ready GenAI system:

Security and Governance
 • Secret management (Vault, AWS Secrets Manager)
 • PII handling and compliance safeguards
 • Guardrails, content moderation, responsible use

Observability and Monitoring
 • Token usage analytics and latency tracking
 • Failure metrics, tool-call history, retry insights
 • Integration with LangSmith, Honeycomb, or Arize

Testing and Evaluation
 • Prompt regression testing
 • Response quality evaluation frameworks (RAGAS, TruLens)
 • Model behaviour tracking across iterations

Agent, Memory, and Context Management
 • Memory layers (short-term, long-term, vector-based)
 • Agent orchestration with structured task flows
 • Document retrieval, tool routing, function handlers

Ops and Deployment
 • CI/CD and container orchestration
 • Scalable API deployment (FastAPI, Lambda, Kubernetes)
 • API gateway, authentication, throttling, access control

This framework is a great start.

But scaling GenAI is less about prompts—and more about disciplined engineering.

Structure is not just documentation.

In AI systems, structure itself is a feature.

The complete GitHub template code for this structure is available in the comments.

A lot of people keep telling me one thing:

“I want to learn Agentic AI… but I genuinely don’t know where to start.”

If that’s you, here’s the simplest answer: start here. 

For free.(Link in the comment section)

Microsoft has put together one of the cleanest, highest-quality learning paths for Agents, RAG, Vector Databases, MCP/A2A protocols, design patterns, memory, and production architecture.
Short lessons. Text + Code. Video. Extra readings. Zero fluff.

Here’s your free 13-lesson roadmap:

 • Intro to AI Agents and Use Cases
 • Exploring Agentic Frameworks
 • AI Agentic Design Patterns
 • Tool-Use Design Pattern
 • Agentic RAG
 • Building Trustworthy AI Agents
 • Planning Design Pattern
 • Multi-Agent Design Pattern
 • Metacognition Design Pattern
 • AI Agents in Production
 • Using Agentic Protocols (MCP, A2A, NLWeb)
 • Context Engineering
 • Agentic Memory Management

We’re sitting at November 9 today.

That means 52 days left in 2025.

If you spend just 30 minutes every day until Christmas, you’ll finish this entire learning path and walk into 2026 with skills that 90 percent of people will only start thinking about next year.

Start now.

This is the easiest runway you’ll ever get into Agentic AI.

Free API Courses - High Quality 



I created these API guides in 2022.

 Updated it for three years.

 Shared it freely — because APIs are not just a technical skill.

They are the foundational language of intelligence systems.

If you ever want to build AI agents, orchestrators, or Agentic systems —APIs are where it truly begins.

So here is my small gift to this community — one last time.

A 12-day, focused API learning journey with carefully curated free resources:

Day 1: API Fundamentals Simplified
 ↳ lnkd.in/e8eMet_k
 ↳ lnkd.in/er9JiGxw
Day 2: API Methods & Terminologies
 ↳ lnkd.in/ey9v7-hU
 ↳ lnkd.in/eRsPMzpd
Day 3: API Authentication & Status Codes
 ↳ lnkd.in/eNPfpAdE
 ↳ lnkd.in/egXizUrS
Day 4: REST vs GraphQL + Integration Basics
 ↳ lnkd.in/eZHREdgC
 ↳ lnkd.in/eDASPP5m
Day 5: Deep Dive into API Integration
 ↳ lnkd.in/eZwFVrH7
 ↳ lnkd.in/e4WGDffA
Day 6: API Testing Fundamentals
 ↳ lnkd.in/emgmWJqH
 ↳ lnkd.in/eCPnGTGi
Day 7: Working with APIs in Python
 ↳ lnkd.in/eM23ah2y
Day 8: API Scaling Essentials
 ↳ lnkd.in/e3mZSvmn
Day 9: Developing Robust APIs
 ↳ lnkd.in/eBXzbFyg
Day 10: Hands-on with Postman
 ↳ lnkd.in/ezue3d4B
Day 11: API Security Deep Dive
 ↳ lnkd.in/e79ZYfPa
Day 12: Final Project – Build your own API solution
 ↳ Combine everything into a real API project.


If you're just starting — or guiding someone who is — use it while it's still here.

Share it with someone who needs their first win.

Their first moment of clarity.
Their first bit of confidence.

Because sometimes, the right roadmap doesn’t just teach.

It restores belief.
And that is worth giving — especially today.

We all know the basic RAG recipe: 

Chunk ➔ Embed ➔ Retrieve ➔ Generate. 

But when you move from a weekend prototype to an enterprise solution, that simple linear path breaks down.

I mapped out the architecture for building a truly Robust RAG System. 

To handle complex queries and ensure accuracy, we need to orchestrate several advanced components:

🔹 Query Construction: It’s not just about vector search. We need to leverage Graph DBs for relationships and SQL for structured data. 

🔹 Intelligent Routing: Deciding before retrieval whether to take a semantic route or a logical one. 

🔹 Advanced Indexing: Utilizing techniques like RAPTOR (recursive abstractive processing) and ColBERT for better semantic understanding. 

🔹 The Evaluation Loop: You can't improve what you don't measure. Integrating frameworks like Ragas or DeepEval is non-negotiable.

RAG isn't just a feature anymore; it's a complex system that requires engineering rigor.

Which part of this pipeline are you finding the most challenging to optimize right now?

Building agents isn’t just about plugging an LLM into a workflow. 

It’s about stitching together dozens of capabilities that make autonomy safe, explainable, and enterprise-ready.

When you look at the structure this way, a pattern emerges:

• Foundational AI gives the agent its baseline intelligence
 • RAG + knowledge layers provide truth, relevance, and domain grounding
 • Interaction + safety layers make the agent usable and trustworthy
 • External integrations unlock action
 • And the top layers ensure everything is governed, compliant, and auditable

This is why true agentic AI feels less like “just prompt it” and more like engineering a digital organisation:data, context, tooling, safety, oversight, and a decision loop that can stand on its own.

If you're working on agents today—inside enterprises or building your own frameworks—this is the blueprint to keep in mind.

It’s not one feature. It’s not a single model. It’s the stack working together.

Most people think of RAG (Retrieval-Augmented Generation) as a single technique — fetch, merge, respond.

But in reality, RAG has evolved into an entire ecosystem of specialized architectures, each optimized for specific goals like accuracy, personalization, reasoning, and speed.

To help you see the bigger picture, I’ve mapped out the Top 25 Types of RAG — from foundational methods like Standard RAG and Conversational RAG, to emerging patterns like Agentic RAG, Speculative RAG, and Chain-of-Retrieval (CoR).

Each one represents a different way to make LLMs more contextually grounded, self-correcting, and autonomous.

Here are some of the key trends shaping the next wave of AI systems:
Adaptive & Context-Aware Retrieval: dynamically adjusts what and how information is fetched.

Memory-Augmented & Self-RAG: enables continuity across sessions and long-term reasoning.

RL-RAG & REFEED Models: use reinforcement and feedback loops to improve retrieval quality.

Agentic & Federated RAGs: enable distributed, multi-agent, and cross-database intelligence.

As we move toward Agentic AI, mastering these retrieval types will be essential for designing reliable, domain-aware, and explainable AI systems.

Save this post as your visual guide — a quick reference for how RAG is diversifying and what comes next in retrieval intelligence.

Why TOON Matters More Than Ever for GenAI & Agent-Workflows

You probably know my stance: AI/data pipelines + multi-agent systems are changing how we build software. 

But one piece of the stack rarely discussed? 

How we structure the data we pass between agents, models, and services.

That’s where TOON comes in — and why I think it’s one of the most exciting data-format innovations today.

Three Deep Insights About TOON:

1. Token- & Model-Efficient Structure

TOON is designed for large-language model (LLM)-centric workflows:
It “provides a lossless serialization of … the same objects, arrays, and primitives as JSON, but in a syntax that minimizes tokens and makes structure easy for models to follow.” 

Example: Arrays of uniform objects with TOON’s tabular layout reduce token count significantly versus JSON. 

For us in agentic AI / data pipelines, every token matters (cost, latency, context window). TOON gives better “bang for token.”

2. Schema-Aware and Model-Friendly

TOON adds structural metadata—e.g., array length declarations (like items[3]), field sets ({sku,qty,price}) — which give models more “hints” about structure and validation. 

In multi-agent systems or orchestration, structured contracts matter. 

If you’re passing bundled data between agents, you want less ambiguity. 

TOON supports that.

3. Strategic Fit: Not Always Better than JSON — But Often the Right Choice
 Importantly, the spec calls out when TOON isn’t ideal:

Deeply nested or highly non-uniform data ➜ JSON may be more token-efficient. 
Pure tabular flat data ➜ CSV may still win. 

For technical leaders: this means TOON isn’t a blanket “replace JSON everywhere” tool. 

It’s a tool for modern workflows where arrays + models + tokens converge.

My Take: 3 Use-Cases in Your AI/DevOps World

Model-to-Model Communication: When one agent or LLM passes structured data to another, TOON’s shorthand + schema signals reduce token usage and improve reliability.

Prompting & Retrieval Pipelines: Large prompts with structured reference data (e.g., metadata, user history, logs) → TOON shrinks size, boosts interpretation.

Microservices / Data Contracts in AI-Driven Systems: Instead of JSON APIs where every service guesses field semantics, TOON’s explicit schema optics help surface contract drift earlier.


In the era of generative AI + multi-agent systems + massive context windows, our old data-formats are showing their age. 

JSON helped build the modern JSON API era — but TOON is asking: What if we optimized the format for the new era?

If you’re building systems where token-efficiency, structured clarity, and model-friendly data matter — TOON deserves a seat at the architecture table.

TOON (Token-Oriented Object Notation)

Agentic AI System Blueprint — More Than Just Tools. 

It’s a Structured Intelligence.

Everyone talks about AI agents.

But very few understand what truly makes them autonomous, reliable, and enterprise-ready.

Most assume it's just prompt chaining, API calling, or tool invocation.

But that’s only one layer — Execution.

The real power of Agentic AI lies in how all its layers work together as a coherent system.

Here’s the full stack that transforms a basic LLM into a self-directed, resilient, and auditable AI agent:

🔹 Planning & Tasks
 Where intelligence begins. Goal definition, task decomposition, coordination, role assignment, and dynamic re-prioritization — this is where agents plan, not just respond.

🔹 Memory & Context
 Short-term context is not enough. Enterprise agents rely on semantic memory, episodic recall, vector databases, knowledge graphs, and temporal context to make decisions based on past experiences — just like humans.

🔹 Execution
 Where most people start — but it's actually the third layer. Tool invocation, function execution, reasoning, API calling, document processing, and multi-step autonomy.

🔹 Monitoring
 Real-time tracking, workflows, agent health checks, logs, metrics, recovery, auditing — because what you cannot monitor, you cannot trust.

🔹 Optimization
 Self-adaptation, feedback loops, reinforcement learning, performance scoring, and reward models enable agents to continuously improve rather than repeat the same mistakes.

🔹 Governance
 Data privacy, compliance audits, guardrails, human validation, explainability, access control — the foundation of trustworthy AI in regulated industries.

🔹 Infrastructure
 The unsung hero — vector DBs, model hosting, GPUs, API gateways, observability, caching, orchestration (Kubernetes/Docker). The plumbing that makes everything scale.

Agentic AI isn’t just prompting.

It is a full-stack architecture.

Most people learn AI in fragments — a course here, a model there, a few tools, a Kaggle notebook… and then wonder why their skills don’t compound.

AI mastery isn’t about learning everything — it’s about learning in the right order with the right foundation.

So I created this Complete AI Roadmap to help you go from fundamentals to building real-world, agentic AI systems.

Here’s how to approach your journey:

Start with Fundamentals:

Understanding intelligence, probability, logic, and data → not just tools.
Master Core AI Concepts

ML, deep learning, optimization, MLOps, prompt engineering, and structured thinking.

Learn Tools & Frameworks:

Python, PyTorch, TensorFlow, LangChain, Hugging Face, AutoGen, Ray, Jupyter, Streamlit.

Dive into LLMs & AI Agents:

Transformer models, vector DBs, RAG pipelines, fine-tuning, agent frameworks, evaluation.

Automate Workflows:

No-code → API automation → full stack AI workflows.

Build Real Projects:

Healthcare, finance, retail, content platforms, enterprise automations, copilots.

Grow Your Career:

Certifications, research, portfolio, networking, and doing real work that compounds.

This isn’t a sprint. It’s a systems journey — one that rewards consistency, experimentation, and curiosity.

Agentic AI isn’t just about retrieval — it’s about reasoning, memory, orchestration, and adaptability.

For years, most people have seen RAG as a simple retrieve → generate cycle.

But as AI systems matured, RAG started evolving into Agentic RAG —where multiple intelligent agents collaborate, reason, use tools, manage memory, and continuously improve.

I sketched this workflow to show how Agentic RAG actually works in practice:

• The Agent Orchestrator coordinates decisions, tools, and workflows

• Search, Vector Store, Code Interpreter, and Context Manager actively work together

• Knowledge isn’t just retrieved — it is interpreted, linked, synthesized, and reused

• Memory isn’t static — it is adaptive, persistent, and context-aware

Knowledge operates at multiple levels:

Sentence → Paragraph → Document → Iterative Reasoning

So the system doesn’t just answer —it remembers, interprets, connects, and evolves.

This blueprint is shaping the next generation of:

 • AI copilots that retain context across sessions
 • Digital assistants that learn user preferences over time
 • CX agents that reason, not just retrieve
 • Enterprise AI systems that comply, explain, and improve at scale

RAG connects data.
Agentic AI connects intelligence.

Over the last year, through my own learning, research, experimentation, and conversations across the AI ecosystem, I noticed one clear trend: AI isn’t growing in a straight line. It’s branching into multiple new disciplines at once.

So I put together a visual guide highlighting the 12 AI skills that will shape the next wave of builders, engineers, creators, analysts, and architects.

These aren’t tied to any employer or project.

They’re based entirely on my personal study and independent understanding of where the field is moving.

Here’s the snapshot:

1. AI Agents
 Systems that can plan, reason, and take actions with human oversight.

2. Agentic AI
 AI that adapts, self-corrects, and works across dynamic contexts.

3. RAG (Retrieval-Augmented Generation)
 Still one of the most important production patterns in the industry.

4. Workflow Automation
 Eliminating manual steps and connecting tools into intelligent flows.

5. Prompt Engineering
 Evolving into structured, guided, and constraint-based prompting.

6. LLM Management
 Monitoring cost, reliability, and performance across complex setups.

7. AI Tool Stacking
 Combining multiple tools to build scalable end-to-end workflows.

8. Multimodal AI
 Blending text, images, audio, and video to create richer experiences.

9. AI Content Generation
 Scaling high-quality written, visual, and audio assets responsibly.

10. AEO / GEO (AI Search Optimization)
 Preparing for AI-first search engines and assistant-driven discovery.

11. AI Integrations & APIs
 Connecting models and tools through APIs for automated capabilities.

12. Autonomous Workflows
 Agent-led systems that trigger, run, and self-manage with minimal input.

I created this list to help anyone looking to future-proof their AI journey.

If you focus on even a handful of these over the next few months, you’ll be in a completely different place by the time 2026 arrives.

Vector search gave LLMs memory.


Graph databases gave LLMs relationships.

But neither could give LLMs real-time reasoning.

That’s the next frontier.

Because agents don't just need content — they need connected knowledge that they can reason over, instantly.

And here’s where the traditional stack fails:

Most graph databases still “walk” through data — one node, one edge, one hop at a time.

Exactly like humans flipping pages in a directory.

That works for analytics.
It collapses for AI agents.

The core idea:
What if graphs stopped behaving like “maps”…
and started behaving like “math”?

That’s the FalkorDB breakthrough.

Instead of hopping from node to node — FalkorDB converts the entire graph into a sparse matrix.

Your data becomes a mathematical object.
And once your graph is math — queries become math too.

Not traversal.
Not step-by-step.
Just matrix computation using linear algebra.

And math doesn’t walk.

It computes.

Which means:

Real-time graph reasoning for agents. At scale.

Why this changes the game for LLMs:

Vector search tells you what is similar.
Graphs tell you what is connected.
But sparse matrix graphs tell you what is structurally meaningful — instantly.
It’s the difference between finding a document…

 …and finding the truth inside a network of relationships.

That's how agents will think.

FalkorDB brings this into the real world:

 🔹 Graphs as sparse matrices — zero traversal overhead
 🔹 Linear algebra-powered queries — orders-of-magnitude faster
 🔹 Redis-native, open-source, lightweight deployment
 🔹 OpenCypher compatible — no need to learn a new language
 🔹 Built specifically for LLM context, agent memory, and reasoning

I tested it — queries that took seconds now feel like function calls.

Agents that relied on retrieval now reason in real-time.

The future isn't LLMs with bigger context windows.
It’s LLMs with smarter knowledge structures.

And frameworks like FalkorDB will power that shift.

I’ve shared their GitHub link in the comments — explore it, run it, stress it.

It feels like where Agent Memory is heading.

Agentic AI is not a platform, not a library, and not a single framework.

It is an architecture ecosystem.

Most conversations around Agentic AI focus on tools—LangChain, AutoGen, CrewAI, DSPy, LangGraph, etc.

Useful, yes. But tools are not the system.

Real Agentic AI—when implemented in enterprise consists of layered capabilities.

Just like cloud has layers (IaaS / PaaS / SaaS), Agentic AI has its own foundational stack.

I have mapped the Agentic AI technology stack into seven layers.

Not theoretical. Not hype.

This is what is actually used to operationalize multi-agent systems.

Memory Systems:
Vector memory, episodic memory, shared memory, structured memory, dynamic context management.
Without adaptive memory, it's not an agent system—it's a chat interface.

Tool Integration & Reasoning Engines:
Function calling, retrieval, symbolic reasoning, planning, execution tracking, guardrails, API governance.

Multi-Agent Collaboration Frameworks:
Task specialization, negotiation, delegation, dependency resolution, arbitration, goal alignment.

Workflow Orchestration & Autonomy Platforms:
State management, decision loops, human-in-the-loop escalation, routing logic, fallback strategies.

Evolution, Feedback & Debugging Systems:
Self-improvement, log-based learning, meta-evaluation, execution trace analysis, agent QA.

Deployment & Scaling Infrastructure:
Versioned agents, state checkpoints, event streams, reproducibility, sandboxing, inference routing.

Monitoring, Governance & Compliance:
Policy enforcement, reliability, auditability, red-teaming, safe autonomy, risk monitoring.
The moment you start thinking in layers, Agentic AI shifts from “which tool should I use?” to“which layer am I designing, and which one is missing?”

That is where real architecture begins.

This visual is a snapshot of the Agentic AI Technology Landscape — 2025 Edition.

Each tool falls into a specific capability layer, not a category.

If you design, evaluate, or deploy agent systems, this layered view changes how you make decisions.

Which layer do you think is currently the weakest across the ecosystem?

Drowning in AI frameworks? Here's your quick reference guide!

I've compiled 31 agentic AI frameworks you should know about in 2026. 

Whether you're building autonomous agents, multi-agent systems, or intelligent automation pipelines - there's a framework for you.

The best framework isn't always the most popular one—it depends on your use case, team expertise, and requirements.

Which framework are you using? Drop a comment below!

Not all multi-agent systems think the same way.

Beneath the orchestration and workflow layers lie patterns of reasoning — architectural choices that determine how agents learn, adapt, and collaborate.

There are many ways to design such systems, but when viewed through the lens of how intelligence organizes itself, as Kence Anderson introduced in his book “Designing Autonomous AI (O’Reilly), three foundational patterns stand out:

1. The Strategy Pattern
 Intelligence through adaptation.
 Each agent develops distinct strategies for different contexts. An orchestrator selects and executes the optimal one, much like a coach adjusting tactics mid-game.

2. The Functional Pattern
 Intelligence through process.
 Agents operate in a structured sequence, each mastering a specific function — analyzing, planning, or executing — where success at one stage enables the next.

3. The Perception Pattern
 Intelligence through awareness.
 Agents incorporate sensory or perceptual capabilities — classification, prediction, and contextual understanding — enhancing decision quality and autonomy.

Together, these patterns illustrate that multi-agent systems are not simply task automation frameworks.

They represent an emerging form of distributed cognition — systems capable of reasoning, coordinating, and improving collectively.

The real question isn’t how many agents can be deployed,but how intelligently they are designed to think together.

After designing more than 200 agents at Microsoft and writing THE book on Autonomous AI, Kence is putting these principles into practice at AMESA(Advanced Modular Enterprise Systems of Autonomy)— creating an AI proving ground where teams of agents can be designed, trained, and validated safely before deployment.
It’s a significant step forward:

 from experimenting with prompts to engineering intelligence,

 from simulating autonomy to proving it in real-world systems.
Thanks to AMESA for sponsoring this post.

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