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

Every Diwali, I find myself hitting a quiet reset button.

It’s more than just lights, sweets, and fireworks - it’s that feeling of renewal. A reminder that we can begin again, no matter how the year has unfolded.

Each year, this festival grounds me in gratitude and hope. It reminds me that even when things don’t go as planned, we can still create moments of meaning, sometimes in the most unexpected places.

Last year, I celebrated Diwali surrounded by my entire family, laughter echoing through our home.
This year looked different. We found ourselves celebrating in the middle of the mountains, just calm skies, fresh air, and quiet reflection.

And somehow, it was equally beautiful.

For me, Diwali isn’t just about where you are, but the light you carry with you- in your work, your relationships, and your own growth journey.

Here’s to new beginnings, to chasing what lights you up, and to carrying that energy through the rest of the year.

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It still feels a little unreal, I just crossed 1 million across all platforms where I create and share⭐️

When I started posting on LinkedIn, it wasn’t about becoming a creator. It was about curiosity - connecting with other ML researchers, sharing what I was learning, and building a community that learned together.

Over the years, that curiosity turned into a mission - to learn, teach, and grow alongside this community. And to help others make sense of a world that’s moving faster than ever, especially now with AI changing how we work, learn, and even think.

There are so many people who feel lost right now - unsure where to start, what to learn next, or how to stay relevant.

If my content has helped even a few of you feel a little more confident, a little more inspired, or a little more seen - that’s what makes it all worth it.

I’m deeply grateful for every person who’s been part of this journey, whether you discovered my work through a post, a reel, a workshop, or a random video. You’ve shaped this path more than you know.

The goal hasn’t changed - to keep learning, to keep teaching, and to help others navigate this ever-evolving AI world with curiosity and courage.

To the next million ❤️

My friend Pranjal Daga and I are hosting a super casual AI meetup in Central Park on November 15th!

No slides, no talks, no agenda- just a bunch of AI nerds hanging out, chatting about cool ideas, and enjoying some coffee and snacks in the park.

If you’re into tech, curious about AI, or just want to meet fun people who get way too excited about the future, come join us! 

📍 Central Park, New York City (Exact location after event confirmation)
🗓️ November 15th
🎟️ RSVP on Luma: https://luma.com/ii79gav1

Bring your curiosity, grab a muffin, and let’s geek out under the fall sunshine.

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 ♥️

🚨BREAKING NEWS: OpenAI just launched their new AI browser, ChatGPT Atlas, and I got early access to it.

After having testing if for several hours- first take is that I love it. I can instantly see all my copy-paste and jumping around tabs go away. 

When I was replying to emails, I didn’t have to switch tabs to ChatGPT. I just asked Atlas, “make this reply sound more concise,” and it edited inline, inside Gmail. Then I was reviewing a document, and instead of copying text over, I asked it for a summary right there. It understood the page context instantly.

Next, I tried something more complex. I asked Atlas to plan a one-week trip to Alaska over the holidays. It pulled up flight options, hotels, and even built a full itinerary, all in one flow, no spreadsheets or extra tabs. I also asked it to help me meal prep and order the groceries for me on instacart- did a pretty good job with just a little feedback (checkout the screenshots below)👇 

Atlas integrates contextual grounding (it knows the page you’re on), browser memory (it remembers what matters and can resurface past work), and inline inference (you can write, edit, or ask questions directly on the page). It’s powered by the same ChatGPT stack but natively optimized for browsing.

Then there’s Agent Mode which currently in preview for Plus and Pro users. This is where it starts to act. You can say, “book the hotel from my Alaska search” or “compare these two articles,” and it handles it for you. Early days, but this is what agentic workflows will look like once embedded directly in everyday tools.

What I also like: OpenAI has made memory optional. You can clear browsing data, use incognito mode, or control which sites it can see. And yes, your ChatGPT memory syncs across the browser. That means the context you’ve built in ChatGPT carries into your browsing session.

If you’ve built Custom GPTs, they’re now accessible inside Atlas too. I can connect it to my AishGPT! Basically your content writer, research assistant, or code reviewer or any other customGPT you use can live in your browser - not as a separate chat window, but as a co-pilot that interacts with whatever’s on your screen.

📺 You can test it out for yourself: https://lnkd.in/d2heMDuR

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li_at

Peter Burjanec - Reddit, Inc. | LinkedIn

OpenAI

Arsen Yeremeichuk

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

If you want to become an AI engineer, this is probably the best course out there!
Not exaggerating, ByteByteGo’s team has created something truly exceptional with their 6-week cohort-based ByteByteAI program, and the next cohort starts November 8th (only two days left!).

I’ve been following their work for years, I first read System Design Interview when I was a student, and later their Machine Learning System Design and Generative AI System Design books. They’ve become the go-to textbooks for anyone serious about building real-world AI systems.

Now they’ve taken that same clarity and depth and turned it into a hands-on course that’s completely project-based learning-  which, if you know me, is my favorite way to learn.

Here’s what you’ll actually build:
 1️⃣ An LLM Playground
 2️⃣ A Customer Support Chatbot using RAG + prompt engineering
 3️⃣ An Ask-the-Web Agent (like Perplexity) with tool calling
 4️⃣ A Deep Research Agent combining web search + reasoning
 5️⃣ A Multi-modal Generation Agent
 6️⃣ Your own capstone project, from idea to prototype

It’s taught by Ali Aminian, one of the best-selling authors in AI system design, who’s built large-scale AI systems at Google and Adobe, and contributed to AI education at Stanford.

If you’re serious about building, not just learning AI systems, this is the course to take. I highly, highly recommend it: https://lnkd.in/dhi5ycdn

A lot of incredibly talented people at Amazon have been impacted by the recent layoffs, and it’s really heartbreaking to see.

If you’ve been affected and are figuring out what’s next, I’d love to help in whatever way I can.
Whether you need general guidance, want to pivot into AI, or just need a sounding board. Feel free to DM me, and I’ll try my best to get on a call with as many people as possible.

Also, Fireworks AI is hiring across several roles. If something aligns with your background, please apply and drop me a DM once you’ve done so.

I’m opening weekly Friday Office Hours for AI builders!

If you’re experimenting with open-source models, building GenAI pipelines, or exploring fine-tuning for your use cases, I’d love to chat.

This will be a casual, technical session to connect with startups and enterprise teams who are building LLM systems, discuss your challenges, and just brainstorm ideas together.

At Fireworks AI, we’ve been powering some of the most exciting use cases in the ecosystem- from Cursor’s fast-apply feature to AI workflows at Upwork, Notion AI, Cresta, Sentient, and Sourcegraph. It’s been fascinating to see how small improvements at the inference layer can completely change the end-user experience.

We’ve also open-sourced Eval Protocol, a toolkit to help you evaluate large language models and understand their performance beyond surface-level metrics.

My goal with the office-hours is simple: connect with more practitioners, hear what you’re building, exchange ideas, and share learnings about optimizing inference and fine-tuning with open-source models.

If you’d like to join one of the Friday sessions, fill out the form below, would love to connect and chat: https://lnkd.in/dW8Z7uHK
(Please register using your business email.)

P.S. These office hours are meant for professional discussions specific to Fireworks AI and ongoing work around LLM systems. They’re not personal consultation sessions.

I came across Canva’s case study recently and thought it was pretty interesting. 

When your product reaches hundreds of millions of users, feedback starts coming in from every direction - support tickets, surveys, app store reviews, social media, and community forums.

But here’s the real challenge: even though there’s more feedback than ever, it gets harder to actually hear what users are saying. Manual tagging doesn’t scale, insights get buried in noise, and PMs end up relying on anecdotes or chasing spreadsheets instead of real patterns.

That’s what made Canva’s approach stand out to me. They used Enterpret’s customer intelligence AI to turn scattered feedback into something teams could act on.

Here’s how they did it 👇

1. Unified Knowledge Graph
All structured and unstructured feedback from 30+ channels centralized in real time.

2. Feedback Modeling
Mapped sentiment, bugs, and feature requests across cohorts, plan types, and product surfaces.

3. Natural Language Querying
PMs could simply ask, “What are new users on mobile frustrated with this week?” and get an instant, structured answer.

4. Automated Detection
AI agents flagged anomalies like spikes in complaints or sudden sentiment drops before metrics started showing the impact.

The outcome wasn’t just faster analysis, it changed how teams think about feedback - from something reactive to something operational.

If you’re scaling a product and struggling to make sense of feedback at scale, Enterpret 2.0 (just launched) is worth checking out. It brings deeper AI capabilities and a new UI that makes listening and acting a lot easier.

I highly recommend reaching the case study as well, link in comments :)
#EnterpretPartner

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

AI agents are only as powerful as the context they operate in.

When context is static, agents plateau. But when context evolves- when it’s refreshed, connected, and accessible, that’s when things start to get interesting.

Take a customer support agent, for example. The most effective ones don’t just respond; they learn. They automatically pull the latest documentation, adapt to new policies, and refine their knowledge base the moment a new feature or update goes live, no manual retraining, no complex setup.

Or look at what’s happening with MCP-connected agents. These systems can browse, search, and act across the web- handling tasks like building a travel plan, managing e-commerce operations, or reacting to real-time data streams to make decisions on their own.

That’s the direction the industry is heading, not toward bigger models, but toward smarter context.
The real progress in AI adoption is happening in how well agents can access, interpret, and act on live information.

Static workflows are becoming the bottleneck. Adaptive, context-aware systems are becoming the differentiator.

They’re transforming how we think about automation, customer experience, and operational intelligence, moving from rule-based processes to systems that continuously learn and evolve.

If you want to explore how this is being applied in practice, Zapier and Apify are hosting a live session on TOMORROW (October 23) showcasing how real-time web data can make agents more dynamic and self-updating.

It’s free to attend, and you’ll get the recording afterward: https://bit.ly/47dwvn2

Happy learning 🚀

#ZapierPartner

When evaluating AI agents, accuracy alone is a poor proxy for performance.

An agent’s goal isn’t to produce a correct answer, it’s to complete a task. And how reliably it does that depends on more than just model precision.

Three metrics matter most:

1. Task Success Rate (TSR)
Measures the percentage of end-to-end tasks completed correctly.
This captures real-world reliability – can the agent consistently finish what it starts?

2. First-Try Success (FTS)
Tracks how often the agent succeeds on its first attempt.
This reflects reasoning quality and prompt grounding – whether it understands the task context accurately before acting.

3. Recovery Speed
Captures how quickly, or in how many steps, the agent self-corrects after a mistake.
This is the best signal of adaptability and robustness, which are critical for agents operating in dynamic environments.

In complex, multi-step workflows, these metrics often tell a more complete story than accuracy or BLEU scores.

An agent that can self-correct and adapt is far more valuable than one that only performs well under static test conditions.

〰️〰️〰️
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

🚨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

Stop asking “Can I?”
Start asking “How can I?”

Lately, I’ve been mentoring many mid to senior-level professionals who feel stuck watching the AI wave rise around them.

They’re great at what they do, but I keep hearing the same thing:

“Can I really pivot into AI?”
“What if I don’t have the right background?”

Here’s what I tell them.
The question isn’t if you can. It’s how you can.

Because everyone’s “how” looks different.

Step 1: Find your general direction

Not your forever plan.
Not your ten-year goal.

Just a direction that excites you enough to start moving.

Maybe it’s:
• Operations to AI-driven automation
• Marketing to data-driven personalization
• Product management to AI-powered products

Pick something broad enough to explore.
You’re not locking yourself in. You’re simply choosing a starting point.

Step 2: Take small, consistent steps

This is where the growth happens.

You don’t need to quit your job or enroll in another degree. You just need to move.

• Start a small project using AI tools.
• Join a nonprofit AI initiative.
• Collaborate on an internal experiment that uses data or automation.

Each small step builds confidence.
Each small win builds momentum.

And the more you experiment, the clearer your next step becomes.

Think T-shaped 📚

Before you go deep, go wide.

Learn the fundamentals.
Understand how AI systems work, who the key players are, and what workflows look like.

Then identify:
• What excites you
• What aligns with your strengths
• What transferable skills give you a head start

That’s your edge.
That’s where you go deeper.

It’s not about reinventing yourself.
It’s about repurposing your superpower.

Here’s the truth, even those of us already in AI are constantly relearning.

The tools we used three months ago may already be outdated. The people who thrive here aren’t the ones who know the most, they’re the ones who adapt the fastest.

This field rewards curiosity, not perfection.

So whether you’re just starting or shifting mid-career, be brave enough to jump in, experiment, and keep learning.

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

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

If you’re an aspiring AI engineer trying to understand how the industry is moving beyond LLMs, here’s a quick eagle’s-eye view of one of the most fascinating frontiers in AI today: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀.

We’ve reached a point where large language models can generate text, summarize papers, write code, and even reason, but that’s not enough anymore.

The next leap isn’t about bigger models. It’s about autonomy, with systems that can not only generate but also decide, act, and adapt in the real world.
That’s where Agentic AI Systems come in.

These are goal-driven, adaptive platforms capable of orchestrating complex workflows, making independent decisions, and using memory to 𝗥𝗲𝗮𝘀𝗼𝗻 → 𝗔𝗰𝘁 → 𝗔𝗱𝗮𝗽𝘁.

 Instead of just prompting a model for a single response, you’re designing a network of intelligent components that:
→ Understand goals and constraints
→ Plan actions through orchestration frameworks
→ Execute via tools, APIs, or other agents
→ Observe results, learn, and improve over time

This shift, from intelligence to autonomous intelligence, is why agentic systems have become one of the most important topics for modern AI engineers.

𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀
→ For AI Engineers:
Agentic architectures are redefining how applications are built- from RAG pipelines and copilots to autonomous research or data systems. Understanding gateways, planners, orchestrators, memory layers, and evaluation loops will become a must-have skill set.

→ For Tech Leaders:
If you’re leading teams or evaluating where AI fits into your business, this is your blueprint for understanding how next-gen systems will operate- safely, scalably, and with clear policy and observability layers.

Happy learning & Happy Building 🚀

Mohammad Chowdhury

#ad Productivity isn’t about working harder, it’s about reducing mental clutter. We make countless micro-decisions daily, chasing context, switching tools, and staying on top of endless threads. But that’s not productivity, it’s burnout.

Balancing a 9-5 and 5-9, I’ve learned real productivity is about thinking less about what’s next. That’s why I’m diving into Slack’s latest AI updates to help streamline my work.

Slack has evolved from being just a communication layer to becoming what they call the Agentic Enterprise, a workspace where humans and AI agents collaborate inside the same conversation.

If you want to see what that looks like in action, check it out here: https://bit.ly/4qcF09y

The new Slack AI innovations:
→ summarize key updates from multiple channels,
→ surface blockers automatically,
→ assign actions to the right people, and
→ integrate your favorite AI apps directly into your workflow.

This means your workspace doesn’t just tell you what’s happening, it acts on it.

For anyone building or leading in AI, this is a glimpse into the next era of intelligent systems, where agents and humans operate as co-workers, not separate layers.

The future of productivity won’t come from adding more tools, but from systems that understand how you work.

That’s not an upgrade, that’s a mindset shift, from communication to collaboration with intelligence.

#DF25

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