Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

If you're feeling overwhelmed with how fast AI is evolving, you're not alone.

Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind.

But the good news is that you don’t need to learn everything all at once. What you need is structure.

So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable.

💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition.

If you're moving faster or slower based on time or experience, that’s okay too.
And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here.

👇 Here’s the roadmap:
🔖 Level 1: GenAI & Transformer Foundations
 Tokens, embeddings, transformers, decoding, and inference with open-weight models.

🔖 Level 2: Prompting & Language Model Behavior
 Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting.

🔖 Level 3: Retrieval-Augmented Generation (RAG)
 Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation.

🔖 Level 4: LLMOps & Tools
 LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data.

🔖 Level 5: Agents & Agent Frameworks
 Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation.

🔖 Level 6: Memory, State & Orchestration
 Vector and symbolic memory, episodic vs persistent state, memory compression.

🔖 Level 7: Multi-Agent Systems
 Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams.

🔖 Level 8: Evaluation & Reinforcement Learning
 LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops.

🔖 Level 9: Protocols & Safety
 MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates.

🔖 Level 10: Build & Deploy
 FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens.

📌 Bookmark this.
🛠️ Build something after every level.

And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will  teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 

💻 Pro-tip: Start with cookbooks!

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Hundreds of AI minds gathered in Central Park on a chilly Saturday morning, it was incredible :)

The past weekend in New York felt like one of those moments where community completely surprises you in the best way.

I was traveling to the city for a work event, and my friend Pranjal and I had a thought- What if we host a small AI meetup on Saturday morning in Central Park? Nothing fancy, just a space for people to connect.

So we put up a quick Luma link and shared it on LinkedIn. We expected maybe a handful of people. Instead, the sign ups crossed 450+. 

We put a small table with coffee, muffins, cookies and some cool swag- thanks to Teachable. We weren’t sure how it would go. We just hoped people would show up.

What happened next honestly moved me.

We reached the park at 9.20am to set up for a 10am meetup, and people were already there. In the cold. On a Saturday morning. People had traveled from Boston, Maryland, upstate New York, New Jersey, even Philadelphia. I kept talking to folks thinking, you really came all this way… for this?

The meetup was meant to last two hours. People stayed until 3pm! Almost six hours of conversations about AI, careers, ideas, and everything in between. No microphones. No agenda. Just humans connecting because they care about the same things.

This experience reminded me of something I tend to forget- community doesn’t need perfect planning. It needs intention. It needs genuine people. And it needs a space where everyone feels welcome.

I’m grateful to every single person who showed up. You made this morning unforgettable, and you reminded me why I love building in this space.

Shout-out to Aditya Suresh and Pranam Daga for helping us with putting this together ❤️

There is a quiet moment every November when life slows down just enough for reflection. I never grew up celebrating Thanksgiving. Back home in India, I didn’t even know what it meant. But after moving to the US, watching my friends celebrate, watching sitcoms where families gathered around food and gratitude, I started understanding the deeper meaning behind it.

A moment to pause.
A moment to look around.
A moment to look back.

This year, gratitude feels a little different for me. A little heavier in the best possible way.

I feel grateful for a life where I get to wake up every day and do work I genuinely love. AI is chaotic and intense and fast. Yet it gives me energy. It gives me purpose. And I feel lucky to be part of this ecosystem during a time when everything is shifting.

I feel grateful for the people who hold me steady.
A mom who pushed me when the world expected her to play it safe. A mom who defied every norm to carve space for me to choose ambition without apology.

A husband who is involved in my life in the most beautiful way supportive is honestly an understatement. He is a true partner in every single thing I do. He listens to every idea, every frustration, every new plan I dream up and he backs me with a kind of belief that strengthens my own. Chinnu amma and Chinnu appa who patiently listen to my ten year entrepreneurial plans and cheer for me with the kind of warmth only family can give.

Mentors and colleagues who let me thrive creatively who challenge me in the right ways who make every room I walk into feel lighter and more possible.

I feel grateful for health. For a body I get to take care of. For the time I carve out to move, to eat well, to nurture myself. It took years to realize that taking care of your body is also taking care of your dreams.

And I feel grateful for the little girl I used to be the one who sat with a notebook and wrote her goals on every page the one who kept believing she would get there someday. If I could talk to her today, I would tell her that she is doing great and that her stubborn belief in herself was worth it.

Thanksgiving may not be something I grew up with, but it has become a reminder to pause and appreciate the rarest gift of all the good people around you. People who are genuinely supportive, genuinely positive, and genuinely rooting for you.

I am grateful for all of it.
And grateful for everyone who has been part of my journey so far.

If you are reading this, I hope you take a moment to celebrate the smallest wins, the unexpected joys, and the people who make your life feel expansive.

li_at

JSESSIONID

What an electrifying afternoon! Absolutely delighted to have joined the Mayfield AI List honorary lunch at the beautiful Golden Gate Club in San Francisco.

The room was filled with the brightest minds in the AI VC world. it was a privilege to meet and listen to inspiring leaders like Navin Chaddha, Aparna Chennapragada and Garry Tan. 

I had the opportunity to host a focused roundtable, diving deep into the current state of the industry. We tackled the crucial discussion around Perception vs. Reality of AI, analyzing current hype cycles, and zeroing in on investment opportunities that hold real long-term potential.

It’s moments and small-group settings like these, where leaders can candidly discuss where the real value lies beyond the buzz, that drive the industry forward.

Huge thanks to the Mayfield and Startup Grind team (special shoutout to James Gee and Fadia Frangie Doumat) for hosting and for the award ♥️

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Wrapped up AI Dev 25 hosted by DeepLearning.AI

More than 1400 AI developers gathered in one space, and the energy was something you could feel instantly. Meeting so many builders and hearing what you’re creating reminded me why this community moves the needle the way it does.

Hearing Andrew Ng talk about the future of work was a standout moment. The way he encourages everyone on his team to code, regardless of background, is exactly the kind of mindset shift this era needs. AI literacy is becoming a baseline skill, and he articulated that perfectly.

Our panel with Michael Kearns, Hatice Ozen, Kay Zhu, and Josh Gordon covered how the ecosystem is evolving across the stack, from Groq , Google, and Amazon Web Services (AWS)  to Fireworks AI and how teams like Genspark are using RFT on Fireworks AI in production workflows. We also touched on multi-agent systems, practical best practices, and where developer tooling still needs to mature.

I also loved my conversation with Michael Kearns on responsible AI and how that thinking has evolved from classic ML days to the generative era. His perspective at the intersection of academia and industry always leaves me thinking differently.

Grateful for the conversations and connections today. If we met, thank you for stopping by. If we didn’t, I’m always happy to keep the discussion going. There’s a lot to build, and we’re still early.

Thank you DeepLearning.AI team for having me ❤️

When you’re an AI engineer, the worst bottleneck isn’t writing code. It’s navigating everything around the code.

Last week I tried out Rovo Dev from Atlassian and it really changed how I approach dev work, plus how I see AI agents changing the landscape of software engineering.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗼𝘃𝗼 𝗗𝗲𝘃?
RovoDev is an AI agent built to live inside your dev workflow, including your code, your issues (let’s say in Jira), your docs (let’s say in Confluence), your pull requests. 

It helps you move from idea → code → deployment with fewer context switches.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁:
→ I asked in plain English: “What does this module do? Where are its dependencies?” and got a clear breakdown, no digging through 3 dozen files.
→ I linked my GitHub/Bitbucket + Jira and was able to surface relevant issues, test failures and comments in one context instead of switching tools.
→ The CLI experience (yes, in my terminal) allowed me to trigger agents to draft tests, summarise PRs, or suggest refactoring ideas, without leaving my code. 

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀?
What I realized after using Rovo Dev is that the real time-sink in engineering isn’t writing code, it’s in fact more about re-orienting yourself.

Jumping between Jira, Confluence, and your repo just to understand context.
Trying to figure out why something was built the way it was.
And then losing your train of thought before you can even start solving the problem.

Well, that's where Rovo Dev actually helps close that gap.

If you’ve ever joined a new project and spent hours just piecing together what’s going on, or if you constantly find yourself switching between tools to get one small thing done, you should try this out: fandf.co/42MTjr8 

Thank you Atlassian for partnering with me on this post, and giving me access to Rovo Dev to test it out :)

#AtlassianPartner

This is one of the best resources for aspiring AI engineers, that covers all the basics - advanced in classroom teaching: https://lnkd.in/dgr5zEve

Highly recommend saving this so you can come back to it later.

Goldmine for aspiring AI Engineers

Lately, I’ve noticed something interesting in the AI startup world.

The ones growing the fastest aren’t building the next big model.

They’re building agentic systems.
AI that doesn’t just talk, it does.
Copilots that summarize meetings.

Agents that predict churn or handle internal ops automatically.

These aren’t demos anymore. They’re real products driving revenue.

What separates the ones that scale from the ones that stall comes down to one thing : Data.
Every great agent lives and dies by how it manages memory, retrieval, and reasoning speed.
And that’s exactly why so many founders I work with build on MongoDB Atlas.

It’s not just a database.
It’s the data layer that brings structured, unstructured, and vector data together so your AI can actually reason in real time.

If you’re a startup building AI that acts, not just talks, go check out the MongoDB Startup Program: https://fandf.co/493VsD1

#MongoDBPartner

🚨Context Engineering for GenAI Workshop happening tomorrow!

In this session, we’ll dive deep into:
→ What context really means in LLMs and how it shapes performance

→ The toolkit that helps you engineer it better: Summarization, Pruning, Query Rewrite, RAG, Context Offloading, Isolation, and Memory

→ How to optimize for accuracy, cost, and reliability across use cases

→ A live demo showing how these ideas come together in real time

This workshop is for every engineer who wants to move beyond prompt tuning and start thinking like a system designer.

🧩 Context Engineering
📅 November 8
🕐 1.5 hours
🎙️ with Arvind Narayanamurthy & me

🎟️ Save your spot: https://lnkd.in/dvFmMiRr

If you’re an AI engineer today, you should absolutely get familiar with Model Context Protocol (MCP) and Responses API. These two are becoming the backbone of modern agentic systems.

Let’s unpack why 👇

1️⃣ MCP: The Connector Layer

Model Context Protocol (MCP) standardizes how models connect to external tools, APIs, and live data.

Think of MCP as the wiring system that allows your LLM to talk to the outside world, like tools, databases, vector stores, or your company APIs.

When you build or host your own MCP server, you’re giving your model new abilities to reason, act, and fetch context beyond what’s in its prompt.
This is what turns a plain LLM into an agent.

If you’re working on production-grade AI systems, you’ll often need to:
→ Integrate custom tools or data sources
→ Maintain context across multiple steps
→ Ensure reliable tool invocation and streaming

MCP makes all that possible.

2️⃣ Fireworks AI Responses API

Once your MCP setup is ready, the Responses API is how you connect it to the model.

It’s a next-generation SDK that powers conversational AI. It supports:
→ Stateful conversations (previous_response_id)
→ Real-time streaming for interactive apps
→ Tool calling and safe execution

The Fireworks Responses API follows the same schema and format as OpenAI’s Responses endpoint.

So if you’ve been using OpenAI models, you can switch to open-source models on Fireworks AI with almost zero code change.

You’ll unlock faster inference, lower latency, and broader model choice, including open models like Qwen, Mistral, Llama, and Gemma hosted natively on Fireworks AI.

You can even mix and match:
✅ Use MCP to extend model capabilities
✅ Use Responses API to orchestrate reasoning, tool use, and streaming

That’s the magic combo powering many of today’s most advanced agentic systems.

3️⃣ Why This Matters

Agentic systems aren’t built with just a model call.
They’re built with context, memory, and actions. MCP and the Responses API make that orchestration reliable, stateful, and production-ready.

If you’re serious about building enterprise-grade AI apps, start experimenting with:
→ MCP Examples (connecting to MCP servers and invoking tools)
→ Conversation Chaining (using previous_response_id to continue dialogue)
→ Streaming Output (for responsive real-time interfaces)
→ Ephemeral Mode (for privacy-friendly, non-persistent interactions)

Happy Building 🚀
〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re an AI engineer or an AI leader looking to level up your skills in LangChain and LangGraph, this one is for you.

I’m teaming up with Arvind Narayanamurthy  for a beginner–intermediate, 1.5 hour hands-on workshop where you’ll learn how to move from simple prompts to real AI applications and agentic workflows.

Why this matters
- Most engineers know how to prompt. Few know how to orchestrate.
- LangChain and LangGraph are becoming the backbone of production AI systems.
- If you want to build or lead AI engineering teams, you need these mental models.

What you’ll learn
- Foundations of LLM orchestration and where LangChain fits
- Essential building blocks: chat models, prompting patterns, structured outputs, LCEL
- Tool calling with MCP adapters
- RAG basics end to end
- LangGraph workflows and agentic architectures
- Two live labs: a micro-app and a simple agent
- Quick dive into LangSmith for debugging

The workshop is recorded, and everyone gets the replay.

If you want practical, real-world skills to build modern AI systems, this is the one to sign up for.

Grab your seats: https://lnkd.in/eW2WZdcf

Are you wondering where the AI industry is actually headed? 
Every week brings a new framework, a new toolkit, a new acronym. But beneath all the noise, one truth is becoming clearer: the real moat isn’t the model, it’s the combination of data and AI. 
 
AI is the toolkit. 
Data is the substance. 
AI gives us reasoning power. 
Data gives us understanding. 

That connection came to life at Snowflake BUILD 2025, where the  focus wasn’t on hype, it was on grounding AI in reality. 
 
We saw how Snowflake Intelligence lets teams ask complex questions in natural language and get trusted answers from governed data. How the Cortex Agents API enables developers to build domain-specific agents that reason and collaborate. And how the Managed MCP Server securely bridges external AI systems to enterprise data, turning “agentic AI” from an idea into something production-ready. 
 
Even unstructured data, the 80 % of information hidden in PDFs, chats, and documents, is now first-class and accessible through simple AISQL queries. 
 
This feels like a turning point. 
The next era of AI won’t be defined by bigger models, but by deeper integration between data and reasoning. 
 
AI without data is just potential. 
Data without AI is just storage. 
Together, they become intelligence. 
 
#SnowflakePartner #SnowflakeBUILD

If you’re an AI engineer trying to understand agentic systems, keep reading 👇

A single-agent system relies on one model to do everything: reasoning, planning, execution, and validation. It’s efficient for simple, sequential tasks but quickly breaks down as workflows grow more complex or interdependent.

A multi-agent system, on the other hand, distributes intelligence across specialized agents that collaborate. Each has its own role, memory, & context - and together, they solve problems that a single model can’t handle alone.

But here’s what most people miss: there’s no standard architecture.

A multi-agent system is like Lego blocks, you design it for your use case.

You can build:
→ a supervisor agent managing sub-agents
→ a hierarchical setup with layered control
→ or a hybrid model combining centralized planning and peer coordination
The challenge isn’t in building agents, it's in managing how they talk to each other.

Three reasons multi-agent systems often fail:

❌Memory fragments across agents.
Each agent keeps its own context. When one agent hands work to another, subtle details get lost or duplicated. Without a shared memory layer or context synchronization, agents start “forgetting” what the others already know.

❌Operational costs multiply.
What costs $0.10 for a single agent might cost $1 when ten agents coordinate. Every message, validation step, or context handoff adds latency, token cost, and complexity. At scale, coordination overhead often outweighs the benefit of specialization.

❌ Write conflicts cascade.
If multiple agents write to the same source of truth: a shared database, knowledge base, or document, their changes can collide. One agent updates a schema, another updates logic on top of the old version, and suddenly the system’s state fragments.

TL;DR: Not every workflow needs a multi-agent setup.

When tasks are independent, read-heavy, and parallelizable, multi-agent systems shine. But for sequential logic and tight dependencies, a single-agent pipeline is still faster, cheaper, and easier to maintain.

As always, Galileo has published a new phenomenal free handbook- Mastering Multi-Agent Systems.
 
If you want to go deeper into how these systems work, why they fail, how to evaluate them, explore different architectures, and even build LLMOps around them, this is one of the best reads out there: https://lnkd.in/diptdEC2

#GalileoPartner

Multi-Agent AI System Cheatsheet 

I learned Design Thinking on my first day as a data scientist at IBM, and it quietly became one of the most useful mindsets in my AI career. It taught me to slow down, understand the real problem, and build solutions that actually land with users.

Here’s how I still use it today:

→ Solve the right problem
Most AI teams jump into modeling too early. Design Thinking forces you to reframe, question assumptions, and check if the problem is even defined correctly.  

→ Diverge before you converge
Explore many possibilities, then narrow down. This prevents tunnel vision and leads to better architectures, better experiments, and better product decisions

→ Use the 2x2 grid
Am I exploring or deciding?
Am I thinking about the problem or the solution?
This simple check saves me from going down the wrong path.  

→ Observe behavior, not opinions
The real insights come from watching users, not asking them. The same applies when evaluating agents, prototypes, or internal tools.  

→ Prototype fast, assign ownership
Ideas die when nobody owns them. Execution is where Design Thinking turns into impact.  

This mindset shaped how I build AI systems then, and it still shapes how I build them now.

♻️Share this with your network

Design Thinking Framework

Last week I came home to a little surprise from OpenAI filled with their new books, 100 Chats for Studying, Career, and College Life. 

I flipped through them right away and loved how visual and genuinely interesting they’ve made each section. It feels approachable, practical, and fun in a way most tech books don’t even try for.

If you want to explore the prompts yourself, you can check them out at this link: https://lnkd.in/ddycaDAg

It’s a pretty cool resource to keep on your desk as you build, study, or just experiment with new ideas.

Here is my absolute fav one which I wish I could have used as a student 👇  
I want to understand [concept]. Explain it with an analogy to real-world items.

Let me know in the comments below which ones is your favorite?

If you’re getting started as an AI engineer, this one’s for you 👇

Here are 12 things you need to learn if you want to become a real AI engineer (or level up as one):

1️⃣ LoRA & qLoRA fine-tuning
Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check.
https://lnkd.in/dxAfNJjP
https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF)
Understand how to align model behavior with human expectations using curated reward signals.
https://lnkd.in/da4qf5JV
https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR)
Use simulated or programmatic reward models when human feedback is too expensive.
https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals
Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs.
https://lnkd.in/dFkgD6RQ
https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures
Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases.
https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases
Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs.
https://lnkd.in/d8zeWbmn
https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought
Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context.
https://lnkd.in/djbJD578
https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration
Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs.
https://lnkd.in/dfjvxhvP
https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies
Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures.
https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation
Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference.
https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring
Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues.
https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking
Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale.
https://lnkd.in/dP_NJXg9

〰️〰️〰️〰️
♻️ Share this with your network 
🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re getting started with building AI agents, this Agentic AI Toolkit Cheatsheet will come in handy 👇

You’ll quickly realize it’s not just about choosing a model. You need orchestration, memory, retrieval, evaluation, and a solid deployment stack.

And if you’re not careful, your architecture turns into a mess of disconnected tools.

That’s why I put together this Agentic AI Toolkit Cheatsheet.

It organizes the full stack of what goes into building production-grade AI agents- from model hosting to feedback loops, so you can stop guessing what’s missing in your pipeline and start shipping actual systems.

Here’s what it covers:

🔧 Foundation Models & Hosting
🎛️ Model Customization (SFT, RFT, RLHF)
🤖 Agent Frameworks
🧩 Orchestration & Workflow
📥 Data Ingestion & Preprocessing
🧠 Embedding & Retrieval
🧷 Memory & Knowledge Storage
🔌 Tool Integration
🧪 Evaluation & Monitoring
🖥️ User Interfaces
🚀 Deployment & Hosting


💡 A few tips if you’re working on your first agentic stack:

→ Choose one tool per layer to start, depth over breadth.
→ Build iteratively and introduce evaluation as early as possible.
→ Use event-driven workflows to manage complexity.
→ Think about memory and feedback as first-class components, not afterthoughts.


📌 Bookmark this.
📚 Use it to audit your stack.
🛠️ Build with it.
📤 Share it with a teammate who’s lost in the sea of GenAI tooling.

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This Week of AI is here⚡️
Another packed week in the world of AI- here’s your quick TL;DR of everything that happened across the AI ecosystem this week 👇

- Apple Forges Billion-Dollar AI Alliance with Google for Siri Overhaul
- Anthropic Foils First Documented Large-Scale Autonomous AI Cyberattack
- World Labs Unveils Marble, A Multimodal World Model for Spatial Intelligence
- OpenAI Rolls Out GPT-5.1 with Adaptive Reasoning and Dual Personalities
- Microsoft Creates MAI Superintelligence Team Led by Mustafa Suleyman
- Google Launches ‘Agentic Checkout’ and AI Shopping Tools for Holiday Season
- IBM Joins OpenSearch Software Foundation as Premier Member

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This week, the AI landscape was defined by blockbuster partnerships, a major infrastructure upgrade, and a chilling preview of autonomous cyber warfar

Open article: This Week in AI: Week of 16th November 2025 by Aishwarya Srinivasan • 3 min read

This Week in AI: Week of 16th November 2025

Thanksgiving week, yet a lot happening in this week in AI!
Catch everything you need to know here 👇

Vision-Language Models connect what AI sees with what it reads and reasons.

They’re the foundation of AI systems that can interpret charts, medical images, retail shelves, or product catalogs.

But a generic VLM doesn’t understand your domain’s visual language.
That’s where fine-tuning becomes essential.

𝐖𝐡𝐲 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐚 𝐕𝐋𝐌 𝐦𝐚𝐭𝐭𝐞𝐫𝐬

A pretrained VLM already knows the basics of visual-text reasoning.
Fine-tuning helps it specialize for your domain.

→ In healthcare, it learns to detect anomalies in MRIs and X-rays.
→ In retail, it interprets shelf images and product layouts.
→ In enterprise, it extracts structured data from invoices and reports.

You’re not rebuilding intelligence, you’re refining perception to fit your use case.

𝐇𝐨𝐰 𝐋𝐨𝐑𝐀 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭

Full model fine-tuning is expensive and compute-heavy.
Low-Rank Adaptation (LoRA) keeps the base model frozen and trains only small adapter layers.

That means:
→ Faster training cycles
→ Smaller memory footprint
→ Lower compute costs
→ Domain adapters that are easy to swap in and out

You can maintain one base model and multiple lightweight adapters for each use case such as invoices, medical forms, or retail analytics.

𝐈𝐧𝐬𝐢𝐝𝐞 𝐚 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥

A VLM has three main components:
→ 𝐕𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐜𝐨𝐝𝐞𝐫 converts pixels into visual tokens.
→ 𝐅𝐮𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 combines visual and text context for reasoning.
→ 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐃𝐞𝐜𝐨𝐝𝐞𝐫 generates captions, summaries, or structured responses.

Each layer introduces potential failure modes like poor resolution, misaligned regions, or verbose hallucinations. Fine-tuning improves alignment and reliability across these components.

𝐓𝐡𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞

→ 𝐃𝐚𝐭𝐚 𝐝𝐞𝐬𝐢𝐠𝐧: Collect diverse, high-quality, clearly labeled visuals.
→ 𝐓𝐚𝐬𝐤 𝐝𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 : Choose the right setup: captioning, VQA, extraction, or localization.
→ 𝐋𝐨𝐑𝐀 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Train adapters for each domain efficiently.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Use both quantitative metrics and human review for grounding and accuracy.

Always evaluate across different slices such as document type, lighting, and template to surface hidden biases.

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲

Each domain adapter should have its own dataset lineage, version, and evaluation score.
Reliability requires attention to fairness, privacy, consistency, and uncertainty handling.
Fine-tuning doesn’t just improve accuracy, it strengthens governance and ethical alignment.

LoRA fine-tuning makes VLMs faster to adapt, cheaper to deploy, and more aligned with your real-world data. 

〰️〰️〰️
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Building a Community of AI Agent Builders | Data & AI Tech Leader | Speaker | Insights via talks & podcasts 🎙️

My track record: 18 yrs in Data & AI, architected secure, high-perf data stacks (100TB–500PB), launched Data/MLOps for real-world workloads, and mentored hundreds of engineers from junior to principal.

Scaling reliable, governed data & AI platforms is hard - but repeatable. 
I help tech leaders design, build, and operate Data/ML/GenAI systems that deliver outcomes.

At AWS, I’ve driven E2E platform migrations, solved cost and performance bottlenecks, and built cultures of experimentation, with governance at the core.

 - Designed serverless analytics for latency-sensitive SaaS (30–60% cost reduction)
 - Implemented AI guardrails and data quality frameworks
 - Mentor: global workshops, community builder for serious learners
 - Speaker: 50+ data/AI events, workshop leader, published podcasts
 - Founder: AgentBuild (free 8-week syllabus, live study cohorts)
 - Podcasts to share pragmatic AI adoption lessons. DMs open for thoughtful invites.

I share my field insights through my newsletter:
https://newsletter.agentbuild.ai

Sandipan Bhaumik 🌱

Save this.
This is your map to learn AI Engineering. 
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Here’s the thing after 18+ years in this field, I can tell you…

AI isn’t about memorising algorithms.

It’s about connecting the dots how data becomes patterns, how patterns become predictions, and how those predictions shape real decisions.

When you’re starting out, the landscape can feel… endless.

Neural networks here, gradient descent there, and somewhere in the middle a hundred tutorials that don’t quite fit together.

That’s why I wanted to share this AI Cheatsheet I recently came across.

It distills the fundamentals Machine Learning, Deep Learning, Neural Networks, Big Data into one visual map.

Think of it like a satellite view of the AI world.

You zoom out, see the terrain, then decide where to dive in deeper.

→ 𝐋𝐞𝐚𝐫𝐧 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐭𝐡𝐞 𝐬𝐲𝐧𝐭𝐚𝐱.
→ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐟𝐥𝐨𝐰, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐭𝐡𝐞 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐬.

This is your map.

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PDF Credits: Fjodor van Veen

#AI #DeepLearning #AIEngineering

Tech Leader - Data & AI | Community Founder | Speaker | Podcast Host

Cheatsheets

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐯𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐢𝐧 𝐬𝐢𝐦𝐩𝐥𝐞 𝐭𝐞𝐫𝐦𝐬.

𝟏. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬.
* An AI agent is a single autonomous system. 
* It thinks, decides, and acts on its own using internal reasoning loops. 
* This makes it powerful but also less predictable, since decision making happens inside the LLM and is harder to trace.

𝟐. 𝐇𝐨𝐰 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐞.
* They read memory, use an LLM to reason, pick tools, take action in the external environment, and update memory. 
* This loop keeps running, making real time decisions based on prompts and internal thinking.

𝟑. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬.
* This is a structured, rule based system. 
* It uses multiple agents, tools, and APIs under orchestration. 
* Every step is transparent, logged, controlled, and follows workflow conditions instead of internal freeform reasoning.

𝟒. 𝐇𝐨𝐰 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐞.
* A decider chooses the right path, tasks run in sequence, and LLM evaluation happens only where needed. 
* The workflow follows branches like Path X, Path Y, or Path Z, producing consistent and predictable outcomes.

𝟓. 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞.
* AI agents offer autonomy but less control.
* Agentic workflows offer control, predictability, and visibility.
* Modern systems often combine both for balanced reasoning and reliability.

Which side do you think will dominate the next wave of enterprise AI systems: autonomous agents or structured workflows?

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#AIagents #AgenticAI #AIworkflows #LLMEngineering

You're Losing Output Quality If You Don't Understand These 7 LLM Parameters
Most engineers use AI daily. Few understand what actually controls the output.

Here's the truth: 
LLM parameters are the hidden switches that determine whether your model gives you gold or garbage.
They control creativity, coherence, length, repetition everything that separates a useful response from a useless one.

If you want to engineer outputs instead of just hoping for good results, master these 7 Parameters:

1. max_tokens:
Sets the length limit. More tokens = detailed answers. Fewer = concise responses.

2. temperature:
Controls randomness. High = creative and diverse. Low = predictable and focused.

3. top_p:
Filters token choices by probability. Narrows the pool of possible words the model considers.

4. top_k:
Limits how many likely tokens the model can pick from. Higher = more freedom. Lower = tighter control.

5. frequency_penalty:
Reduces repetition. Increase it to force fresh phrasing and varied language.

6. presence_penalty:
Forces new concepts. Stops the model from rehashing what it already said.

7. stop:
Defines where generation ends. Custom boundaries for clean, controlled outputs.

---

Here's what changes when you dial these in:

Your model stops giving rambling, repetitive, or off-brand responses.
It starts behaving exactly how your application needs it to.

Most people never tune these. That's why most AI outputs feel generic.

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Reference: https://lnkd.in/emRDxKFV


#AI #LLM #GenAI #AIEngineering #AgenticAI #AIAgents #AgentBuildAI

I have curated 24 ready-to-deploy n8n workflows that saves you 100+ hours of development.

These are not beginner exercises, they are real systems solving real problems, and each one teaches you a pattern you can apply to your own work.

𝐒𝐭𝐮𝐝𝐲 𝐭𝐡𝐞𝐬𝐞. 𝐃𝐞𝐩𝐥𝐨𝐲 𝐭𝐡𝐞𝐦. 𝐓𝐡𝐞𝐧 𝐛𝐮𝐢𝐥𝐝 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧.

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬

• Angie, Personal AI Assistant with Telegram Voice and Text
https://lnkd.in/gWKrSE9X

• Multi-Modal Personal AI Assistant with Telegram & Google Gemini
https://lnkd.in/gMuBeFSr

• Multi-Platform AI Sales Agent with RAG, CRM Logging & Appointment Booking
https://lnkd.in/gCjxR82V

• Build a Financial Documents Assistant using Qdrant and Mistral.ai
https://lnkd.in/g4hfXFKE

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

• Automated Stock Analysis Reports with Technical & News Sentiment using GPT-4o
https://lnkd.in/gaZyy_bg

• Smart RSS News Alert System with DeepSeek AI and Slack Notifications
https://lnkd.in/gA57ajE3

• Process Receipt Images with GPT-4o-mini, WhatsApp & Google Sheets Automation
https://lnkd.in/gCVsnuPK

• Modular & Customizable AI Powered Email Routing: Text Classifier for eCommerce
https://lnkd.in/g9taqAZD

𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 & 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧

• Automate Multi-Platform Social Media Content Creation with AI
https://lnkd.in/gyepPfg6

• Automated Social Media Content Publishing Factory + System Prompt Composition
https://lnkd.in/g3B9EYWY

• Generate SEO-Optimized WordPress Content with AI Powered Perplexity Research
https://lnkd.in/gVTkxH5v

• Publish WordPress Posts to Social Media X, Facebook, LinkedIn, Instagram with AI
https://lnkd.in/g2DBTKSi

• Generate Unlimited E-Commerce Ad Creative with Nano Banana Image Generator
https://lnkd.in/gfNb8xkx

• Automated Generation of AI Advertising Photos for Product Marketing
https://lnkd.in/guAgStdG

• Automatically create YouTube short videos using Elevenlabs, Hailuo AI
https://lnkd.in/gAEAg2TW

𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 & 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬

• AI Agent for project management and meetings with Airtable and Fireflies
https://lnkd.in/gqpdwzCe

• Automated LinkedIn Lead Generation, Scoring & Communication with AI-Agent
https://lnkd.in/gnB4Gj5X

• Smart Job Search: Resume Scoring & Tailoring with OpenAI, Apify, and Airtable
https://lnkd.in/gmb9AwFh

• Jira Project Management Automation with Google Gemini & MCP Server
https://lnkd.in/gKAXXzg8

• Personal Finance Assistant: Query Notion Data via Telegram & WhatsApp with Claude AI
https://lnkd.in/gyRsvfAW

𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞

• Building Your First WhatsApp Chatbot
https://lnkd.in/g5nipsz8

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#AI #N8N #AgenticAI

24 Workflows

I stood there, paralysed by a familiar fear: 
𝐖𝐡𝐚𝐭 𝐢𝐟 𝐈'𝐦 𝐧𝐨𝐭 𝐦𝐨𝐯𝐢𝐧𝐠 𝐟𝐚𝐬𝐭 𝐞𝐧𝐨𝐮𝐠𝐡?
Are you feeling the same?
I can feel you.

I have been there, 
felt that stress, 
that anxiety, 
that mind running on a hamster wheel, 
that "𝑛𝑜𝑡 𝑏𝑒𝑖𝑛𝑔 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑚𝑒𝑛𝑡".

Everytime I fall in that trap, I remind myself of what one of my mentors said - "𝐓𝐡𝐞 𝐩𝐚𝐭𝐡 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐜𝐚𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐩𝐚𝐜𝐞. 𝐈𝐭 𝐨𝐧𝐥𝐲 𝐜𝐚𝐫𝐞𝐬 𝐭𝐡𝐚𝐭 𝐲𝐨𝐮'𝐫𝐞 𝐨𝐧 𝐢𝐭."

Progress isn't always visible from where you're standing. 

That conversation you had last month? 
It's germinating. 

That framework you're refining? 
It's deepening. 

That client you're serving with full presence? 
They're noticing.

The oak tree doesn't apologize for taking decades to reach its height. It simply grows, season after season, ring after ring.

I used to think success meant sprinting. 
Now I understand it means 𝐧𝐨𝐭 𝐬𝐭𝐨𝐩𝐩𝐢𝐧𝐠. 
There's a profound difference.

Some days, my progress is a published article. 
Other days, it's finally understanding a concept I've been wrestling with for weeks. Some weeks, it's a new idea. Other weeks, it's the courage to say no to work that doesn't align.

All of it counts. All of it compounds.

The tracks in that photo wind through uncertain terrain. 
You can't see where they end. 
But they're going somewhere. 
And so are you.

If you're building something meaningful - 
whether it's a business, 
a skill, 
a body of work, 
or a new version of yourself

→ Remember this: 𝐅𝐨𝐫𝐰𝐚𝐫𝐝 𝐢𝐬 𝐟𝐨𝐫𝐰𝐚𝐫𝐝. 
Slow is still 𝑚𝑜𝑡𝑖𝑜𝑛. 
And persistence, not speed, is what transforms the landscape.

The journey from here to there isn't measured in how quickly you arrive. It's measured in whether you keep walking when the destination is out of sight.

Keep going. 
𝐘𝐨𝐮𝐫 𝐩𝐚𝐜𝐞 𝐢𝐬 𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐢𝐭'𝐬 𝐲𝐨𝐮𝐫𝐬.

#growth #consistency #development #learning

𝐘𝐨𝐮𝐫 𝐋𝐋𝐌 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐢𝐬 𝐬𝐥𝐨𝐰. 𝐘𝐨𝐮𝐫 𝐭𝐨𝐤𝐞𝐧𝐬 𝐚𝐫𝐞 𝐞𝐱𝐩𝐥𝐨𝐝𝐢𝐧𝐠. 𝐘𝐨𝐮𝐫 𝐜𝐚𝐜𝐡𝐞 𝐢𝐬𝐧'𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠. 
𝐍𝐨𝐛𝐨𝐝𝐲 𝐭𝐞𝐚𝐜𝐡𝐞𝐬 𝐭𝐡𝐢𝐬. 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝟓-𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐭𝐡𝐚𝐭 𝐟𝐢𝐱𝐞𝐬 𝐚𝐥𝐥 𝐨𝐟 𝐢𝐭:

𝟏. 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐁𝐮𝐝𝐠𝐞𝐭𝐬.
Every stage adds delay. Input prep, retrieval, inference, and postprocessing must each stay within tight limits. Set a clear end to end target and split workloads to avoid overhead. Choose model size based on latency needs, not just accuracy.

𝟐. 𝐓𝐨𝐤𝐞𝐧 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠.
Token bloat slows everything down. Use summarization, truncation, or embedding filters before passing content to the model. Optimize prompts, use shorter formats, and enforce model specific token rules to keep prompts lean.

𝟑. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝐋𝐚𝐲𝐞𝐫𝐬.
Caching cuts cost and prevents repeated work. Cache prompts, embeddings, top k retrievals, fixed messages, partial responses, and near duplicate queries. Effective caching makes your system faster and cheaper.

𝟒. 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐬𝐦 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬.
Increase throughput using async and parallel calls. Break big tasks into subtasks, run retrievals in parallel, and use speculative decoding with smaller models. Fan out and aggregate to reduce waiting time.

𝟓. 𝐅𝐚𝐢𝐥𝐮𝐫𝐞 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠.
Production LLM systems need strong fault tolerance. Use retries, logging, tracing, shadow model testing, timeouts, graceful degradation, and fallbacks. Add circuit breakers for unstable services and validate every input and output.

Which of these five areas do you want me to create a deeper guide for next?

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#LLMEngineering #AIagents #AIsystems #ProductionAI

Your customers are screaming for help. Can you hear them?

Here's the uncomfortable truth: 
While you're celebrating new sign-ups, some of your best customers are quietly walking toward the exit. And most teams don't see it coming until it's too late.

But what if AI could tap you on the shoulder before disaster strikes?

I just mapped out an Agentic AI workflow that's basically a customer success team on steroids and it never sleeps.
Check out this powerful Agentic AI workflow that ensures no customer is left behind:

𝟏. 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬:
You need real-time data to start the process. Product analytics, customer support systems, and email engagement tools are the foundational sources that collect risk signals.

𝟐. 𝐀𝐠𝐞𝐧𝐭 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧:
Raw signals are routed to specialized agents who are set up to take action based on risk severity.

𝟑. 𝐀𝐠𝐞𝐧𝐭 𝟏: 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐌𝐨𝐧𝐢𝐭𝐨𝐫:
This agent monitors customer health scores. If scores drop from 88 to 42, indicating usage decline, it triggers further action.

𝟒. 𝐀𝐠𝐞𝐧𝐭 𝟐: 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐳𝐞𝐫:
This agent pulls a 90-day timeline, analyzes tickets, and identifies root causes, giving a complete view of why the customer is at risk.

𝟓. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐢𝐞𝐫𝐬:
Risk severity is categorized into high, medium, and low priorities, ensuring that each case is handled with the appropriate urgency.

𝟔. 𝐀𝐠𝐞𝐧𝐭 𝟑: 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫:
Once the case is identified, this agent pulls predefined playbooks and generates an action plan tailored to address the customer’s unique situation.

𝟕. 𝐀𝐠𝐞𝐧𝐭 𝟒: 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫:
This agent drafts emails, schedules Slack reminders, and triggers CRM notifications to keep everyone aligned and acting promptly.

𝟖. 𝐇𝐮𝐦𝐚𝐧 𝐑𝐞𝐯𝐢𝐞𝐰:
Although automated actions are initiated, human oversight ensures that decisions are backed by the right context and insights.

𝟗. 𝐈𝐧𝐭𝐞𝐫𝐯𝐞𝐧𝐭𝐢𝐨𝐧 𝐄𝐱𝐞𝐜𝐮𝐭𝐞𝐝:
The playbook is executed, and the customer is guided back on track, improving their engagement with the product.

𝟏𝟎. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐎𝐮𝐭𝐜𝐨𝐦𝐞:
With the correct intervention, outcomes like increasing the health score from 42 to 78 and saving $50K ARR become a reality.

𝟏𝟏. 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐒𝐭𝐨𝐫𝐞𝐝:
The results are stored in the MCP server to further improve the inference model and action outcomes in the future.

𝟏𝟐. 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤𝐬 𝐔𝐩𝐝𝐚𝐭𝐞𝐝:
As interventions improve, so do the playbooks. The system learns and updates its strategies to optimize future outcomes.

If you’re not using a workflow like this, you could be letting valuable customers slip away.
This is the future of customer retention and success powered by AI.

Are you currently leveraging any AI-driven workflows to reduce churn in your business?

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Cursor hit $1 billion ARR in 24 months. Meanwhile, research shows AI made experienced developers 19% slower. This gap is where the real money is being made.

I've been talking to CTOs for months about their coding agent deployments.
Most spent $50K+ pcm on enterprise seats. 
Most can't prove ROI.

Here's what I learned from the ones actually making money with this stuff:

The winners aren't using AI to "make developers faster."
They're using it to make customization cheaper.

𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐢𝐧𝐠 𝐟𝐢𝐫𝐦𝐬: 
Same $150K project fee, but finishing in 200 hours instead of 300.

𝐃𝐞𝐯 𝐬𝐡𝐨𝐩𝐬: 
Taking open-source foundations and building customer-specific features in days, not weeks.

𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐭𝐞𝐚𝐦𝐬: 
Customizing tools for different departments without hiring more engineers.

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐈 𝐬𝐞𝐞:
Start with something solid. 
Use AI for the boring customization layer - 
interfaces, integrations, tweaks. 
Keep humans on the unique business logic.

One agency I know built custom Slack integrations for three clients last quarter. 

Same base, different features. 
Used to take 3 weeks per client. 
Now takes 5 days.
That's real ROI.

𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐨 𝐢𝐭:

→ 𝐃𝐨𝐧'𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 "𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲." Measure cost per customization and project delivery time.

→ 𝐃𝐨𝐧'𝐭 𝐝𝐞𝐩𝐥𝐨𝐲 𝐭𝐨 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞. Target teams doing repetitive customization: internal tools, integrations, platform extensions.

→ 𝐓𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐜𝐨𝐬𝐭𝐬: review time (up 91% in some studies), bug increases (up 9%), technical debt from inconsistent AI-generated patterns.

→ 𝐊𝐢𝐥𝐥 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐟𝐚𝐬𝐭. No measurable savings in 30 days? Stop paying for those seats.

If you're struggling to prove value from your coding agent investment, you're not alone. Most companies are.

But the ones who figured it out stopped trying to make everything faster.
They started making 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐡𝐢𝐧𝐠𝐬 cheaper.

The business of coding agents isn't about replacing developers or speeding up code generation. It's about making software customization so cheap that "one size fits all" stops making economic sense.

That's the shift.

I've written a longer article in my newsletter. Link is in comment.

Which companies are getting this right in your experience? 
Drop a comment - I'm genuinely curious what's working in the wild.

I've been watching this shift. The lines between data, AI, and product are blurring. And it's not being discussed much.

Last month, the Head of Data at a Retail company shared: "My team used to get tickets for dashboards. Now they're getting asked to build agents that make decisions. I have no idea how to staff for that."

He's not alone.
Data teams are quietly becoming product teams.

Not because someone decreed it. 
But because the work itself demands it.

When your "data pipeline" starts making decisions, it needs an owner, SLAs, feedback loops, and versioning. 

Guess what? That's product management 101.

I'm watching job descriptions evolve in real time.

AI Product Managers are the hardest to find.
The FDE role is going viral with AI companies.

The companies that get this are restructuring now. 

The ones that don't are still running their AI efforts like IT projects - and wondering why nothing makes it to production.

New roles are emerging:
𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 → 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫𝐬
Less "pick the database," more "design the behavior of a system that runs 24/7 without supervision."

𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 → 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐎𝐰𝐧𝐞𝐫𝐬 
The bottleneck isn't writing code faster. It's building systems you can trust when they make desisions with LLMs connecting to APIs.

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬 → 𝐎𝐮𝐭𝐜𝐨𝐦𝐞 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬
Not just reporting what happened, but shaping how AI systems synthesize information and make recommendations.

𝐏𝐌𝐬 → 𝐑𝐢𝐬𝐤 & 𝐈𝐦𝐩𝐚𝐜𝐭 𝐒𝐭𝐞𝐰𝐚𝐫𝐝𝐬 
Every AI workflow creates measurable impact. Someone needs to monitor it, adjust it, and shut it down when it drifts.

𝐌𝐲 𝐭𝐚𝐤𝐞:
The teams shipping production AI are the ones who figured out that data + workflows + agents need to be treated as products - with ownership, accountability, and iteration baked in from day one.

Most companies are still structured for the old world. They're trying to build autonomous AI systems with org charts designed for BI dashboards.

It's like trying to run a software company with a manufacturing org chart.

𝐒𝐨 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤:
Are you structuring for the org you have... or the org you're about to become?

Because six months from now, your CEO is going to ask you to deploy agents that handle customer support, process claims, or manage inventory.

And the question won't be "do we have GPT-5 access?"

It'll be: "Do we have people who know how to ship this safely, measure if it's working, and iterate when it's not?"

The companies restructuring now will have an answer.

👉 If you're a data, AI, or product leader navigating this shift, tell me in comments or DM me:

What roles are you creating? 
What's breaking in your current structure?

#futureofwork #aiagents #aiinwork #enterpriseai #aiteams

Still confused about RAG?
Here's a simple workflow for you.
Read the post to learn more.

When we ask an LLM a question, it often struggles if it has not seen the right data during training - like business specific data.

That’s where 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) comes in combining search(of the business doucments/data) with generation of accurate and relevant responses.

𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:

𝟏. 𝐔𝐩𝐥𝐨𝐚𝐝 𝐏𝐃𝐅
A user provides a knowledge source, like a PDF or document.

𝟐. 𝐂𝐡𝐮𝐧𝐤, 𝐄𝐦𝐛𝐞𝐝, 𝐒𝐭𝐨𝐫𝐞
The orchestrator breaks it into smaller pieces (chunks), converts them into embeddings (using an LLM), and saves them into a Vector Database.

𝟑. 𝐀𝐬𝐤 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧
The user sends a query to the system.

𝟒. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞 𝐓𝐨𝐩-𝐊
The Vector DB retrieves the most relevant pieces of information (chunks).

𝟓. 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐂𝐡𝐮𝐧𝐤𝐬
The orchestrator receives the matching chunks.

𝟔. 𝐏𝐫𝐨𝐦𝐩𝐭 = 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 + 𝐂𝐡𝐮𝐧𝐤𝐬
The orchestrator combines the user’s query with these relevant chunks and forwards it to the LLM.

𝟕. 𝐋𝐋𝐌 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐬 𝐀𝐧𝐬𝐰𝐞𝐫
The LLM uses both the query and the retrieved knowledge to produce a contextual answer.

𝟖. 𝐅𝐢𝐧𝐚𝐥 𝐀𝐧𝐬𝐰𝐞𝐫 + 𝐂𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬
The orchestrator delivers a refined answer along with source citations for transparency.

In simple terms, RAG makes AI grounded, accurate, and explainable by connecting responses with actual knowledge sources.

𝐃𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐑𝐀𝐆 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐟𝐞𝐰 𝐲𝐞𝐚𝐫𝐬?

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