↳ Architected and scaled an multi-agent system, integrating autonomous agents and multi-agent workflows for dynamic enterprise tasks

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

↳ Led prompt engineering, LLM 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

AI Frontier Network

AI Ambassador

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

MentorCruise

Served as a dedicated mentor, empowering aspiring tech professionals to navigate their career paths and develop crucial skills in the rapidly evolving tech landscape.

Key Responsibilities:
- Provided one-on-one guidance to mentees transitioning into or advancing within the tech industry
- Shared expert knowledge in Python, Cloud technologies, and Go programming language
- Offered personalized career advice, including resume reviews, interview preparation, and job search strategies

Impact:
- Contributed to the growth of the tech community by nurturing new talent and promoting best practices
- Fostered a supportive learning environment, encouraging mentees to overcome challenges and pursue continuous improvement
- Strengthened personal leadership and communication skills through diverse mentoring experiences

Peer Mentor

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

Opinions expressed are solely my own and do not reflect the views of my employer.

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

Lately, I’ve been getting a lot of questions around the difference between 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜, 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀, and 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜.

Here’s how I usually explain it — without the jargon.

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜

This is what most people think of when they hear “AI.” It can write blog posts, generate images, help you code, and more.

It’s like a super-smart assistant — but only when you ask. 
No initiative. 
No memory. 
No goals.
Tools like ChatGPT, Claude, and GitHub Copilot fall into this bucket.

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
Now we’re talking action. An AI Agent doesn’t just answer questions — it 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝗻𝗴𝘀.
It can:
• Plan tasks
• Use tools
• Interact with APIs
• Loop through steps until the job is done
Think of it like a junior teammate that can handle a process from start to finish — with minimal handholding.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
This is where things get interesting.
Agentic AI is not just about completing a single task. It’s about having 𝗴𝗼𝗮𝗹𝘀, 𝗺𝗲𝗺𝗼𝗿𝘆, and the ability to 𝗮𝗱𝗮𝗽𝘁.

It’s the difference between:

"Write me a summary" vs. "Go read 50 research papers, summarize the key trends, update my Notion, and ping me if there’s anything game-changing."

Agentic AI behaves more like a 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 than a chatbot. It can collaborate, improve over time, and even work alongside other agents.

Personally, I think we’re just scratching the surface of what agentic systems can do. We’re moving from building apps to 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀.

And that’s a massive shift.

Curious to hear from others building in this space — what tools or frameworks are you experimenting with? LangGraph, AutoGen, CrewAI ?

Brij kishore Pandey

We’re witnessing a shift from static models to 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘁𝗵𝗶𝗻𝗸, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁—not just respond.

But with so many disciplines converging—LLMs, orchestration, memory, planning—how do you 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 to master it all?

Here’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 to navigate the Agentic AI landscape, designed for developers and builders who want to go beyond surface-level hype:

↳ 𝟭. 𝗥𝗲𝘁𝗵𝗶𝗻𝗸 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: Move from model outputs to goal-driven autonomy. Understand where Agentic AI fits in the automation stack.

↳ 𝟮. 𝗚𝗿𝗼𝘂𝗻𝗱 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Before agents, there’s learning—deep learning, reinforcement learning, and the theories powering adaptive behavior.

↳ 𝟯. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Dive into 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜—frameworks enabling coordination, planning, and tool use.

↳ 𝟰. 𝗚𝗼 𝗗𝗲𝗲𝗽 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝘀: Learn how tokenization, embeddings, and memory management drive better reasoning.

↳𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Agents aren’t lone wolves—they negotiate, delegate, and synchronize in distributed workflows.

↳𝟲. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Understand how 𝗥𝗔𝗚, vector stores, and semantic indexing turn short-term chatbots into long-term thinkers.

↳𝟳. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗦𝗸𝗶𝗹𝗹: Build agents with layered planning, feedback loops, and reinforcement-based self-improvement.

↳𝟴. 𝗠𝗮𝗸𝗲 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗗𝘆𝗻𝗮𝗺𝗶𝗰: From few-shot to chain-of-thought, prompt engineering is the new compiler—learn to wield it with intention.

↳𝟵. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 + 𝗦𝗲𝗹𝗳-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Agents that improve themselves aren’t science fiction—they're built on adaptive loops and human feedback.

↳𝟭𝟬. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Master hybrid search and scalable retrieval pipelines for real-time, context-rich AI.

↳𝟭𝟭. 𝗧𝗵𝗶𝗻𝗸 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗗𝗲𝗺𝗼𝘀: Production-ready agents need low latency, monitoring, and integration into business workflows.

𝟭𝟮. 𝗔𝗽𝗽𝗹𝘆 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: From copilots to autonomous research assistants—Agentic AI is already solving real problems in the wild.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗼𝘂𝘁𝗽𝘂𝘁𝘀—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹, 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

If you're serious about building the next wave of intelligent systems, this roadmap is your compass.

Curious—what part of this roadmap are you diving into right now?

Google Did It Again!


Google just launched the 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗞𝗶𝘁 (𝗔𝗗𝗞) — a fully open-source framework to build, orchestrate, evaluate, and deploy multi-agent systems.

Why this launch matters:

• Create a working multi-agent system in under 100 lines of Python
• Code-first: full control over agent behavior, orchestration, and tools
• Built-in evaluation, debugging tools, and flexible deployment options
• Designed for real-world use cases 
• It’s open-source, with the community in mind from day one
  
This could become a foundational tool in the agentic AI stack.

The GitHub repo is in the comments.

As we move from LLM-powered chatbots to truly 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, understanding 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 becomes non-negotiable.

Agentic AI isn’t just about plugging an LLM into a prompt—it’s about designing systems that can 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲, 𝗽𝗹𝗮𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻 in dynamic environments.

Here’s where most teams struggle:  

They underestimate the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 required to support agent behavior.

To build effective AI agents, you need to think across four critical dimensions:

1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Agents should break down goals into executable steps and act without constant human input.

2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Agents need long-term and episodic memory. Vector databases, context windows, and frameworks like Redis/Postgres are foundational.

3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Real-world agents must invoke APIs, search tools, code execution engines, and more to complete complex tasks.

4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 – Single-agent systems are powerful, but multi-agent orchestration (planner-executor models, role-based agents) is where scalability emerges.

The ecosystem is evolving fast—with frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜 making it easier to move from prototypes to production.

But tools are only part of the story.

If you don’t understand concepts like 𝘁𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀, 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻, and 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀, your agents will remain shallow, brittle, and unscalable.

The future belongs to those who can 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗟𝗟𝗠 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗿𝗼𝗯𝘂𝘀𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻. That’s where real innovation happens.

2025 will be the year we go from prompting to architecting.

We’re witnessing a massive shift in how AI is evolving—from being a reactive tool to becoming an active decision-maker.

To make sense of this transformation, I’ve created the 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗦𝘁𝗮𝗶𝗿𝗰𝗮𝘀𝗲—a visual framework that outlines the progression from basic AI capabilities to fully autonomous agentic systems.

Here's a quick breakdown:

➤ 𝗕𝗮𝘀𝗶𝗰 𝗟𝗮𝘆𝗲𝗿 

↳ Starts with foundational components like LLMs, embeddings, vector databases, prompt engineering, and API integration. 
↳ These are the building blocks, helping AI understand context and access external knowledge.

➤ 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗟𝗮𝘆𝗲𝗿 

↳ Introduces memory, tool use, multi-step reasoning, and agent orchestration. 
↳ This is where AI becomes more interactive and capable of handling complex workflows with memory and logic.

➤ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗮𝘆𝗲𝗿 

↳ Encompasses autonomous planning, agentic workflows, self-learning, and ultimately, fully autonomous agents. 
↳ Here, AI agents are not just assisting—they're acting independently with minimal human intervention.

The future of AI isn’t just smarter models—it’s 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 that collaborate, learn, and make decisions autonomously.

This staircase is not just a roadmap—it's a reflection of where we’re heading in enterprise AI, product development, and autonomous systems.

Would love to hear your thoughts: What’s the biggest challenge you've faced in climbing this AI maturity curve?

As AI evolves from automation to true autonomy, 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 isn’t optional — it’s foundational.

Whether you're building a GenAI product, orchestrating autonomous workflows, or designing agentic RAG pipelines, the core question remains:

𝘞𝘩𝘢𝘵 𝘵𝘺𝘱𝘦 𝘰𝘧 𝘢𝘨𝘦𝘯𝘵 𝘢𝘳𝘦 𝘺𝘰𝘶 𝘥𝘦𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘴𝘺𝘴𝘵𝘦𝘮?

To help you think more clearly about this, I created a visual on the 𝟴 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 — from simple rule-followers to highly adaptive, reasoning-based LLM-powered agents.

Here’s a breakdown of the intelligence spectrum:

↳ 𝗦𝗶𝗺𝗽𝗹𝗲 𝗥𝗲𝗳𝗹𝗲𝘅 𝗔𝗴𝗲𝗻𝘁𝘀 – React to current input. No memory. Think: Thermostats or Rule-based chatbots.  
↳ 𝗠𝗼𝗱𝗲𝗹-𝗕𝗮𝘀𝗲𝗱 𝗥𝗲𝗳𝗹𝗲𝘅 𝗔𝗴𝗲𝗻𝘁𝘀 – Track internal state and context. Used in bots that respond based on past inputs.  
↳ 𝗚𝗼𝗮𝗹-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 – Don't just act — they pursue objectives. Ideal for pathfinding and planning tasks.  
↳ 𝗨𝘁𝗶𝗹𝗶𝘁𝘆-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 – Choose the best option among many using utility functions. Common in recommendation systems.  
↳ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 – Improve performance over time. Learn from feedback.  
↳ 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 – Think, plan, act, and adapt — all without human oversight.  
↳ 𝗟𝗟𝗠-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 – Leverage the reasoning power of large language models to simulate human-like cognition.  
↳ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 – Multiple agents working together to solve problems collaboratively or competitively. Think swarm intelligence or agentic RAG.

As we move toward 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀, we’re shifting from 𝘵𝘰𝘰𝘭𝘴 𝘵𝘩𝘢𝘵 𝘢𝘴𝘴𝘪𝘴𝘵 to 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘵𝘩𝘢𝘵 𝘤𝘰-𝘤𝘳𝘦𝘢𝘵𝘦 — digital workers that can collaborate, reason, and even negotiate.

If you’re building in AI, this isn’t just theory — it’s design strategy.

Many people think prompting is just about asking the right question… but 𝘁𝗵𝗲 𝙝𝙤𝙬 matters just as much as the 𝘸𝘩𝘢𝘵.

Here are 𝗮 𝗳𝗲𝘄 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 that can help you go from basic prompts to expert-level interactions with large language models (LLMs):

↳ 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 – Just ask, no examples needed. Great for speed, not always for accuracy.  

↳ 𝗢𝗻𝗲-𝗦𝗵𝗼𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 – One example = one big step toward better context.  

↳ 𝗙𝗲𝘄-𝗦𝗵𝗼𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 – Give 2-5 examples and watch your model learn patterns fast. 

↳ 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗼𝗧) – “Think step-by-step.” Perfect for complex, reasoning-heavy queries.  

↳ 𝗦𝗲𝗹𝗳-𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 – Sample multiple responses and choose the best. Think consensus-building with AI. 

↳ 𝗥𝗼𝗹𝗲-𝗕𝗮𝘀𝗲𝗱 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 – Want legal advice or a coding buddy? Set the role, get focused answers.  

↳ 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗧𝘂𝗻𝗶𝗻𝗴 – Structured inputs = high precision for tasks like summarization or classification. 

↳ 𝗥𝗲𝗔𝗰𝘁 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗔𝗰𝘁𝗶𝗼𝗻) – Let the LLM think, act (e.g., call an API), then think again. Real-time magic.  

↳ 𝗧𝗮𝘀𝗸-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 – Highly tailored instructions for tasks like sentiment analysis, grading, or data extraction.

Whether you’re building an AI-powered product, refining your workflows, or just exploring what's possible—understanding these techniques can drastically improve 𝗼𝘂𝘁𝗽𝘂𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗱𝗲𝗽𝘁𝗵, 𝗮𝗻𝗱 𝘁𝗮𝘀𝗸 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆.

Which prompting style do 𝘺𝘰𝘶 use most?  

Have you tried mixing multiple techniques together?

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic.

This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration.

The Workflow at a Glance
1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools).
2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components.
3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment.
4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.
   
This feedback loop mirrors principles from:
• The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act)
• 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI
• 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks

Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires:

• A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making 
• Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness 
• Tool-use and reasoning across multiple steps 
• 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion 
• The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback
  
This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break.

Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking.

Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯.

Curious to hear how you're modeling cognition in your systems.

Most people think of RAG (Retrieval-Augmented Generation) as: 

𝘘𝘶𝘦𝘳𝘺 → 𝘝𝘦𝘤𝘵𝘰𝘳 𝘋𝘉 → 𝘓𝘓𝘔 → 𝘈𝘯𝘴𝘸𝘦𝘳

But that’s just step one.

In 2025, we’re seeing a shift toward 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 systems—where LLMs don’t just retrieve and respond, but also 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗽𝗹𝗮𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁.

This visual breakdown captures the core idea: 

→ A query is embedded and used to fetch relevant chunks from a vector DB. 
→ An 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 leverages those chunks to craft context-aware prompts. 
→ It can also invoke external tools:  
 
• Web Search   
• APIs   
• Internal Databases

This unlocks workflows that are:
• Dynamic
• Context-aware
• Action-oriented
  
It's not just answering — it's deciding 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁.

Toolkits like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗖𝗿𝗲𝘄𝗔𝗜, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗗𝗞, and 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 are making this architecture practical for real-world systems.

What tools or techniques are 𝘺𝘰𝘶 using to take your LLM apps beyond static chatbots?

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