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

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

Sandipan Bhaumik 🌱

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

Cheatsheets

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐮𝐬𝐞 𝐯𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐝𝐚𝐢𝐥𝐲. 

Here is how Vector Databases store and retrieve information behind the scenes:

𝟏. 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧:
* You define a collection (like a table) that holds all related data.
* Each record has properties (like title, description, release year, genres, etc.).

𝟐. 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧:
* Each collection includes metadata defining data schema (like name, data type, and index settings).
* Integrations like vectorizers, compressors, and generative indexes are linked here.

𝟑. 𝐑𝐞𝐪𝐮𝐞𝐬𝐭 𝐕𝐞𝐜𝐭𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
* When you send a request (say, “Top Gun: Maverick”), the text is converted into numerical form using an embedding models.
* This vector represents the semantic meaning of the text, not just keywords.

𝟒. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐭𝐨𝐫𝐚𝐠𝐞:
* The vector (e.g., `[0.0183, 0.8153, -0.3462, 0.5852...]`) is stored in the vector database.
* Each stored object gets a unique ID (UUID) for retrieval.

𝟓. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠:
* Data is divided into smaller “shards” for scalability and faster lookups.
* The inverted index maps data points for quick similarity searches.

𝟔. 𝐒𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥:
* When a new query comes in, it is vectorized again.
* The system compares it with stored vectors using similarity algorithms (like cosine similarity).
* The most semantically similar items are retrieved and returned.

𝟕. 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲:
* The vector database responds with the closest matching data (e.g., movie recommendations, document retrieval, etc.).

𝐈𝐧 𝐬𝐡𝐨𝐫𝐭:
Vector Databases don’t just store text. They store meaning enabling LLMs and AI systems to find conceptually related data, not just literal matches.

So next time someone says “it is just like a database,” you will know it is much more than that.

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

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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