agentbuild.ai

Growing a global Agentic AI builder community from scratch. I design spaces for practical learning, host podcasts with experts, and connect enterprise teams with real-world agent use cases. Passionate about making the “build and learn together” spirit accessible to everyone.

If you are interested visit agentbuild.ai - become an AgentBuilder. It's completely free!

Community Founder

Amazon Web Services (AWS)

Lead Data & AI Specialist SA helping customers scale secure, efficient, and AI-ready data platforms.

Shaped AI and analytics strategies for major enterprises across EMEA, focusing on practical GenAI and data solutions that drive real business results.

Mentor technical teams and lead community learning programs, helping others grow while keeping innovation grounded in real-world needs.

Regularly speak at events and collaborate cross-functionally, translating rapidly evolving tech into actionable outcomes for clients.

Principal Analytics Solutions Architect, EMEA Tech Leader for Analytics

Guided SaaS and enterprise clients through the full data journey—from modern platform builds to smooth GenAI adoption.

Trusted advisor: built long-term relationships by turning complex technical scenarios into understandable, effective solutions.

Launched best-practice initiatives and shared knowledge widely through talks, mentoring, and technical leadership in the AWS community.

Senior Analytics Solutions Architect

Telstra

Orchestrated enterprise data and AI platform transformations, positioning Telstra’s enterprise clients for rapid data & AI innovation.

Advised C-level and data leaders on cloud migration, data maturity, and regulatory governance - unlocking value via modern data stacks and ML automation.

Drove architecture and delivery of secure, ML-ready data lakes, cataloguing, and data quality governance for tier-one clients in telecoms, manufacturing, and public sector.

Championed organizational change: mentored cross-functional teams and evangelised data-driven operating models aligned to strategic business outcomes.

Data & AI Technical Lead

Weetabix

Conducted strategic audit of Weetabix’s enterprise data platform, uncovering root-cause issues impacting analytics and business agility.

Defined and recommended future-state cloud data architecture aligned to “Better Data, Better Decisions” vision.

Developed actionable remediation plan and success roadmap, ensuring reliable, scalable, and regulatory-compliant data environments for operational and AI-driven insights.

Data Architect

NHS South, Central and West

Led data management and analytics strategy for Central & Southern NHS, serving hundreds of CCGs and clinicians.

Architected cloud data platform and BI solutions enabling advanced analytics and care delivery for national and regional datasets.

Led compliance and data governance for NHS Digital, translating GDPR and data-opt out regulations into scalable, secure analytics pipelines.

Built and mentored high-performance technical teams, nurturing culture of innovation and data excellence - recognised by NHS leaders for impact on patient and public outcomes.

Regional Lead, Data Management Services Development

Next Retail Ltd

Designed and automated data pipelines for retail analytics, speeding up business insights.

Built the store analytics datawarehouse migrating data form legacy databases.

Microsoft BI Consultant

iPSL (Intelligent Processing Solutions Limited)

Led high-value data integration projects that improved cheque processing and payment automation for top UK banks.

Switch backend processes (Fraud Detection, Accounts Reconciliation, Analytics and Reporting) to migrate paper-based cheque processing to image-based processing cutting down remittance time from 6 days to 3 days for major UK banks.

Tackled demanding challenges in secure, regulated, high-volume transaction data - ensuring compliance and reliability under pressure.

Served as a key member of the Change Advisory Team, collaborating with banking operations and IT to ensure smooth rollout of system changes - minimising downtime and driving continuous improvement for mission-critical financial processes.

Development and Implementation Engineer

Tata Consultancy Services

Fixed critical data modeling and analytics issues for a major financial client, increasing reliability and speed of business insights.

Delivered hands-on SQL and ETL training to client teams, raising the bar for analytics development across the organization.

Worked closely with business analysts to align data warehouse solutions with regulatory and operational standards in the finance sector.

Data Warehouse Consultant

Led complex BI migrations and global incident management for large banking and finance clients, ensuring business continuity and secure operations.

Directed onshore/offshore teams through high-stakes system upgrades and data transformation projects- meeting strict SLAs and compliance demands.

Collaborated with business and IT stakeholders as part of the Change Advisory Team to evaluate, approve, and deliver major BI system changes with minimal disruption.

Business Intelligence Team Lead

Supported global KPMG teams by developing and maintaining core HR, Audit, and Tax applications and databases.

Built strong client communication skills by collaborating directly with KPMG managers worldwide, helping prioritise needs and deliver practical solutions.

Honed my technical expertise in SQL Server and .NET, while actively working within ITIL standards-gaining hands-on experience in incident, change, and problem management for complex enterprise environments.

Assistant Systems Engineer - Application Development and Maintenance

InfoAxon Technologies Limited

Joined a dynamic engineering team delivering projects for NTL during its transition to Virgin Media.

Applied my SQL Server skills from the classroom to genuine commercial challenges, gaining first-hand experience of real-world tech delivery.

Learned how collaborative teams work, how customer communications drive results, and the value of clear meetings- I even became the go-to note-taker to keep projects on track.

This experience gave me my real start in tech and set the stage for my first full-time role.

Student Intern

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

Everyone’s excited about AI right now. 
But most businesses can’t get past the hype and demos. 
They run into roadblocks with data, integration, and trust.

I help them cut through the noise and actually deliver outcomes. 

At AWS, I work with companies to scale their data and AI platforms. 

And through AgentBuild, I’m building a community where people can learn, share, and grow by creating practical AI agents together.

I’ve spent years bridging strategy with deep tech - leading programs that help both enterprises and builders unlock real value from data and AI. 

So if you’re exploring how to move from hype to real results, I’d love to connect.

From time to time, I join podcasts to share pragmatic AI adoption lessons. DMs open for thoughtful invites.

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

Microsoft is offering a free MCP course for beginners.

And it’s not just fluff it’s 11 lessons packed with everything you need to actually build and deploy MCP-powered AI agents.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮’𝐥𝐥 𝐥𝐞𝐚𝐫𝐧, 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:

1. MCP for Beginners (Full Course) → https://lnkd.in/e4h25ta3
2. Intro to MCP → https://lnkd.in/eSa3jb4z
3. Core Concepts → https://lnkd.in/eRBM_2jQ
4. Security Best Practices → https://lnkd.in/efJpkTN7
5. Build Your First MCP Server → https://lnkd.in/ejP3BbP4
6. Deploy MCP Apps with Real Tools & Workflows → https://lnkd.in/ewwy_XKQ
7. Advanced MCP: Secure, Scalable, Multi-Modal Agents → https://lnkd.in/e-GXtttQ
8. How to Contribute (Tools, Docs, Code & More) → https://lnkd.in/emSDr35u
9. Lessons from Early Adopters → https://lnkd.in/e8qrqG9X
10. Dev Best Practices → https://lnkd.in/e7ABkRj6
11. Case Studies in the Wild → https://lnkd.in/e5cBmpPA
12. Hands-On Labs in VS Code → https://lnkd.in/eChfxDAn

It’s one of the fastest-growing skills in AI engineering today.
And missing out could mean missing the next big wave of AI adoption.

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#MCP #Microsoft #AIAgents #ModelContextProtocol #AIEngineering

Sandipan Bhaumik 🌱

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

Here are the 9 Agentic Workflows for AI Engineers That Solves Real-World Problems

𝟏. 𝐆𝐢𝐭𝐇𝐮𝐛-𝐭𝐨-𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧→ An agent reads infra code on GitHub and generates Terraform scripts. No manual provisioning.
Link: https://lnkd.in/e5ubAhJ9

𝟐. 𝐃𝐚𝐭𝐚𝐎𝐩𝐬 (𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐏𝐡𝐚𝐬𝐞) → Agents monitor, validate, and heal production data pipelines. Think automated quality checks + alerts.
Link: https://lnkd.in/eSicc335

𝟑. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐋𝐨𝐨𝐩 → Collects customer reviews, clusters them into themes, and even suggests improvements. That’s automated product strategy.
Link: https://lnkd.in/erj85Mis

𝟒. 𝐋𝐞𝐚𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 → Agents research prospects, enrich profiles, and score leads before sales ever sees them.
Link: https://lnkd.in/e578Mt5N

𝟓. 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 → Agents parse EHRs, connect with diagnostic models, and provide initial clinical decision support.

Link: https://lnkd.in/eAAsKX7T

𝟔. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 → Orchestration agents route queries to retrieval + reasoning agents. The result? Faster, context-aware responses.
Link: https://lnkd.in/ekS8BYEj

𝟕. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 → Agents research, draft, edit, and optimize content. Not just writing full pipeline automation.
Link: https://lnkd.in/eEvE89jj

𝟖. 𝐈𝐧𝐬𝐢𝐝𝐞𝐫 𝐓𝐡𝐫𝐞𝐚𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 → Detecting suspicious activity, flagging anomalies, and escalating threats before they cause damage.
Link: https://lnkd.in/eNsD3YUw

𝟗. 𝐃𝐚𝐭𝐚𝐎𝐩𝐬 (𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐏𝐡𝐚𝐬𝐞) → CI/CD for data workflows. Agents validate schema changes, run tests, and maintain pipeline integrity.
Link: https://lnkd.in/exKcM4gM

Notice the pattern?
It’s not “AI replacing humans.” It’s AI agents stitching workflows together across data, infra, healthcare, sales, and security.

This is why “agentic AI” matters. It shifts AI from answering prompts to executing multi-step, high-stakes tasks.

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#AI #AIAgents #AgenticAI

Microsoft is offering a free 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐜𝐨𝐮𝐫𝐬𝐞 𝐟𝐨𝐫 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 and it’s seriously good.
And it’s not just fluff it’s 𝟏𝟒 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 packed with everything you need to actually build and deploy Production-ready AI Agents.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐥𝐞𝐚𝐫𝐧, 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:

1. AI Agents for Beginners (Full Course) → https://lnkd.in/gxCXp4yH
2. What are AI Agents → https://lnkd.in/gxCXp4yH
3. Which AI Agent Framework to Use → https://lnkd.in/gBdGunhS
4. How to Design Good AI Agents → https://lnkd.in/g3k3mfpv
5. What Is the Agent Tool Use Design Pattern? → https://lnkd.in/gF3dSRb5
6. What Is agentic RAG? → https://lnkd.in/geZmpmpq
7. How to build effective AI agents → https://lnkd.in/gJJaxYaf
8. What Is the AI Agent Planning Design Pattern? → https://lnkd.in/gR_zd6-p
9. How to use a multi-AI agent system → https://lnkd.in/gS_BjiTm
10. How can AI agents improve? (Metacognition) → https://lnkd.in/gGmRKF3X
11. How to deploy AI agents into production → https://lnkd.in/gWBPBsCU
12. Using Agentic Protocols (MCP, A2A, and NLWeb) → https://lnkd.in/gq3x54Gb
13. Context engineering for AI agents → https://lnkd.in/gkQURFhN
14. AI Agent Memory: Building Self-Improving Agents → https://lnkd.in/gnknd2hY


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𝐓𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐌𝐂𝐏 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐢𝐧 𝐨𝐧𝐞 𝐩𝐢𝐜𝐭𝐮𝐫𝐞

When you chat with an AI agent, it feels instant.
You ask a question → it gives an answer.

𝐁𝐮𝐭 𝐡𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐭𝐫𝐮𝐭𝐡:
Behind that smooth reply is MCP (Model Context Protocol) the invisible system that makes sure your query is understood, processed, and answered reliably.

𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐌𝐂𝐏 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤𝐬 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:

1. You send a query
2. The MCP Client initializes and sets up the environment
3. It shares capabilities → what tools and resources are available
4. Your message (with metadata) is sent to the model
5. The model may trigger a tool call (with name + arguments)
6. The tool runs and returns a result
7. If external context is needed, MCP fetches additional resources
8. Resource data flows back into the system
9. Results are organized
10. The model prepares the final draft
11. The answer is delivered back to you

Looks simple on the surface. But under the hood, MCP is orchestrating every step so the AI agent doesn’t collapse into chaos.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:
Without MCP → Tool calling becomes a mess.
With MCP → AI agents can reason, fetch data, call tools, and scale into real-world automation.

The next frontier isn’t just better prompts.
It’s about mastering the protocols like MCP.

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#MCP #AI #Agents #Automation #AgenticAI #AgentBuildAI

I loved watching this robot pulling off this prank.
Then I thought what if bots prank with our calendars, bank accounts, may be order 18K water cups from your credit card?

While I understand the LLM Agents space, 
I got curious on how such interactions are built into physical robots.

And how do we actually make such pranks safe?

I came across this beautiful paper that draws out some guiding principles on Human-Robot interaction.
https://lnkd.in/edw-xz6m

Here a simple mental model to follow while designing such interations:

𝐆𝐫𝐞𝐞𝐧-𝐳𝐨𝐧𝐞 𝐩𝐫𝐚𝐧𝐤𝐬 = 𝐞𝐭𝐡𝐢𝐜𝐚𝐥 𝐛𝐲 𝐝𝐞𝐬𝐢𝐠𝐧

→ Opt-in “Fun Mode,” default OFF
    Think of it like “surprise me” on Netflix. 
     It’s OFF unless you turn it ON. 
    The robot only jokes around if you’ve said “yes.”

→ Show what it’ll do (transparency)
    Before the prank, the robot gives a quick heads-up: 
    “I’m about to do a popcorn trick - okay?” 
    No mystery moves, no sneaky stuff.
    (This surely is not going to be funny though)

→ Predictable micro-scripts
     It follows a tiny, fixed routine: 
     offer → pull back → offer → hand over. 
     Max two teases, under a few seconds. 
     No freestyle, no dragging it out.

→ Big, obvious Stop
    You can stop it instantly with a word, button, or gesture. 
    Say “Stop,” and it stops - right now.
   (Reminds me of a movie where this didn't work)

→ No real impact
     It can’t touch your money, 
    change records, 
    or affect safety. 
    It’s just a playful moment. 
    And everything is logged so, if needed, 
    you can replay what happened and why.

𝐘𝐞𝐥𝐥𝐨𝐰-𝐳𝐨𝐧𝐞 = 𝐫𝐚𝐢𝐥𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝

→ Consent refresh in sensitive contexts; 
→ don't fake it to “sell the joke.” 
→ If a human is driving, disclose it.

→ Smooth handoff to immediate compliance 
     when a user says “stop” or looks uncomfortable.

𝐑𝐞𝐝-𝐳𝐨𝐧𝐞 = 𝐝𝐨𝐧’𝐭 𝐝𝐨 𝐢𝐭

→ Any prank that risks dignity (embarrassment/harm), 
    privacy, or physical/mental integrity; 
→ any prank without clear opt-out/kill switch. 

So much to learn in this space - without even getting technical.

Enjoy the video!

AI’s biggest wins in 2026 won’t come from new models.
They’ll come from the discipline of evaluating, testing, measuring, and proving what actually works for the user.

Behind the scenes, something is shifting: contracts, budgets, even compliance are all starting to demand evidence, not demos.

I just wrote about why this makes 𝟐𝟎𝟐𝟔 𝐭𝐡𝐞 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐄𝐯𝐚𝐥𝐬 - and how it will quietly decide who leads and who lags.

Do you think most companies are ready for this shift?

Read the article, share your opinion. 
Tell me why my prediction is not accurate.

We’ve lived through three fast years: 2023: The LLM rush (everyone worshipped the models). 2024: The POC flood (everyone “tried” AI). 2025: The Agent 

Open article: 2026 Will Be the Year of AI Evals by Sandipan Bhaumik 🌱 • 6 min read

I used to think the hardest part of enterprise AI was the code. I don’t believe that anymore.
Here's why...


These days, I hear a lot of people asking:

→ Which model should we use?
→ How do we chain agents together?
→ Can we optimize latency?

All important questions.

But I’ve seen projects fail for quieter reasons:

→ No one could audit what the AI just decided.

→ No one knew who was accountable when it went rogue.

→ The system couldn’t explain its choices in plain English.


In my experience, enterprises don’t stumble because the code breaks.

They stumble because they struggle to prove their solutions are safe, compliant, and accountable.

This matters most because,

→ Regulators aren’t waiting for us to figure it out.

→ Customers are less forgiving when AI gets it wrong.

→ And competitors who bake governance in early seem to move faster, not slower.


It’s changed how I think about governance.

Not as a hurdle, but as the thing that actually lets you scale with confidence.

A few things I’ve found helpful:

1️⃣ Log every agent decision in human-readable form.

2️⃣ Make a business owner accountable, not just the engineers.

3️⃣ Test continuously for bias, reliability, and compliance - not just at launch.

4️⃣ Culture Shift - Move governance out of “checkbox compliance” and into daily design decisions.

I don’t see governance as a brake anymore.
Done right, it actually creates trust.

And in enterprise AI, trust travels further than any demo ever will.
 
👉 How are you building trust into your AI systems?

𝐖𝐡𝐚𝐭 𝐫𝐞𝐚𝐥𝐥𝐲 𝐬𝐭𝐨𝐩𝐬 𝐀𝐈 𝐟𝐫𝐨𝐦 𝐥𝐞𝐚𝐤𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐬𝐞𝐜𝐫𝐞𝐭𝐬?

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐭𝐚𝐥𝐤𝐬 𝐚𝐛𝐨𝐮𝐭 𝐰𝐡𝐚𝐭 𝐀𝐈 𝐜𝐚𝐧 𝐝𝐨.
𝐁𝐮𝐭 𝐡𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐧𝐨𝐛𝐨𝐝𝐲 𝐚𝐬𝐤𝐬:
𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐬𝐮𝐫𝐞 𝐀𝐈 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐬𝐡𝐚𝐫𝐞 𝐭𝐡𝐞 𝐰𝐫𝐨𝐧𝐠 𝐭𝐡𝐢𝐧𝐠?

That is where AI Guardrails come in.
Think of them as the safety layer that keeps AI systems ethical, accurate, and secure.

𝐖𝐡𝐲 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 𝐌𝐚𝐭𝐭𝐞𝐫
- Protect sensitive data from being exposed
- Ensure AI systems follow ethical and legal rules
- Prevent misuse of information

𝐓𝐡𝐞 𝐑𝐢𝐬𝐤𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬
- Privacy violations and data leakage
- Unsafe or harmful content
- Spreading biased or misleading outputs

𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬
- Regular monitoring and audits
- Multi-layered verification processes
- Involving ethical experts in design

𝐇𝐨𝐰 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 𝐖𝐨𝐫𝐤 𝐢𝐧 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐒𝐲𝐬𝐭𝐞𝐦
1. Input Validation → checks incoming data
2. Safety Filter → screens risky inputs
3. PLL Detector → spots sensitive or private data
4. Ethical Validator → applies ethical rules
5. Content Verifier → ensures format and accuracy
6. Monitoring System → tracks agent performance
7. Specialized Agents (Research, Analysis, Validation) → provide expert checks

𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭?
AI that not only answers but does so safely, responsibly, and without leaking your secrets.

The next frontier of AI adoption is not just about better models.
It is about trustworthy AI systems built on strong guardrails.

𝐒𝐨 𝐡𝐞𝐫𝐞 𝐢𝐬 𝐦𝐲 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧:
𝐀𝐫𝐞 𝐲𝐨𝐮 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤𝐬… 𝐨𝐫 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐛𝐞 𝐭𝐫𝐮𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝?

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#AI #Guardrails #DataPrivacy #EthicalAI #AgentBuildAI

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Want to drive more opportunities from LinkedIn?

Sandipan Bhaumik 🌱's Linkedin Analytics

's Best Posts (last 30 days)