As a Senior Customer Success Manager & AI Architect, I help clients unlock the potential of Generative AI to drive business transformation and achieve measurable outcomes. My role encompasses the entire lifecycle of AI adoption, from identifying high-impact use cases and crafting tailored strategies to designing solutions and developing proof of concept models that validate potential ROI.

I collaborate closely with technical teams and business stakeholders to ensure the successful implementation and deployment of Generative AI solutions, seamlessly integrating them into existing systems and workflows. My focus is on translating complex AI technologies into actionable business strategies that drive efficiency, boost productivity, and foster innovation across various industries.

In addition, I work to build strong client relationships, providing ongoing support and guidance throughout the AI implementation journey. This includes training teams on best practices, ensuring alignment with business objectives. My goal is to empower organizations to thrive in a competitive landscape by leveraging the latest advancements in Generative AI.

Senior Customer Success Manager - AI Architect | Specializing in Gen AI and Agentic AI Solutions

Responsible for delivering scalable and highly available Data Fabric solutions with advanced
analytics, data governance, data pipelines, data repositories and master Data Management. Conduct customer demos to showcase Data Fabric capabilities of IBM Cloud Pak for Data. Collaborate with customer and IBM technical teams to understand current data topologies/workloads and develop and recommend modern data architecture to meet customer’s data platform requirements. Prove the architecture through a 4-6 week MVP for a given use case.

Sr Enterprise Architect - Data Elite Team

Led solution architecture for Health Insights modernized data Platform. Designed an event-driven Data Quality Framework to automate data quality checks. Led security and compliance process to get the data platform HIPAA certified. Designed and Implemented BI solutions for Data scientist and Data Managers using Superset and Plotly Dash. Implemented authentication and authorization using AppID and Keycloak. The overall project Reduced time to Insights by 96.6 % with a continuous update paradigm with near Zero downtime. 

Led solution architecture and successful implementation of Watson Change detection on Watson’s payer platform. This solution is a comprehensive and scalable machine learning system that understands healthcare data in context and identifies emerging cost drivers early on, empowering health care payers to take action to improve performance sooner. Reduced time for analysts to identify top cost drivers by 87.5 %

Led solution architecture and development team towards a successful implementation of Watson Analytic Assistant. This solution brings AI to Watson Health’s payer platform to surface Insights using easy to understand natural language dialog to recommend the best visualizations. Reduced time for end user to generate health Insights charts by 96.5 %

Led solution architecture for Cognitive Benefits Abstractor and several successful pilots of the solution. This solution uses Watson AI and healthcare specific Natural Language Processing (NLP) to interpret lengthy, unstructured data. Filed three patents. Time acceleration: 40x to 50x. Cost savings: 50% - 80%. F1 score: of 98%.

Lead Solution Architect - Watson Health

Solution architect and Client lead for IBM Flexible Analytics: This solution is designed to provide analytic insights to healthcare customers to make successful business decisions. I led several client projects to identify business and technical requirements, support to RFP process, architect and present solutions to meet client requirements, proof of concept work, work hands on in IBM cloud platform, product demonstrations, coordinate technical resources, and create customer facing documents. 

Solution architect and Implementation lead for IBM Unified Data Model for Healthcare which is an industry-standard healthcare model to help customers quickly develop business applications. I led the effort by working with different business units within IBM to migrate the data from a legacy platform into UDMH using IBM Data Stage and extended the model using Info Sphere Data Architect.

Sr Technical Solution Architect - Watson Health

GSDC - Global Skill Development Council

Appointed to the Advisory Board at GSDC – Global Skill Development Council in a pro bono capacity. I’ll be supporting knowledge exchange across the global tech community through occasional webinars. My focus area is agentic agents—intelligent systems capable of autonomous decision-making, dynamic tool use, and multi-step task execution.

Pro Bono Advisory Board Member

Groundspeed, LLC

Groundspeed is a data science and artificial intelligence (AI) company serving insurance carriers. Their solutions unlock the value in unstructured information to provide powerful predictive risk analytics. Architected the OLTP and Dimensional DWH, build a deployment process using Flyway to automate the deployment to a PostgreSQL database on AWS.

Sr Data Architect Consultant

Johnson & Johnson Health and Wellness Solutions, Inc.

Spearheaded a large-scale data migration improvement process for one of JJHWS largest customer and optimized data migration time from 6 weeks to fit into a 48 hr. maintenance window (95% improvement). This was a key accomplishment for the organization as it led to reduction in operation cost and key customers signing multi-year contract generating additional revenue.

Spearheaded a Big Data project to migrate JJHWS data to JNJ Data Lake on Cloudera eco system. Query performance improved by 94%. Conducted training sessions for employees on Big Data Solutions. 

As a Scrum Master managed and led a team of DBA’s, App/BI developers to implement a new platform using cutting end technology to replace a classic system. Responsible for estimation of work effort, project planning, design, architecture, implementation and test plan. 

Led Data Warehouse development team to design, architect, and implement the migration of data from classic system to new Refusion system, including ETL, Data Warehouse, DataMart, generating XML extracts.
Led a data centre migration project and reduced operational costs by consolidating systems and improving system availability to meet customer SLA.

Sr. Data Architect

CS Manager-Agentic AI Architect@IBM| #35 Favikon Top Creator🔥| 240K+ FB |Thought Leader| Keynote Speaker| Board Member| Podcast Host| Publisher| IBM ATE Top 0.05%| IBM Tech Top 1%| Multiple Patents| Multiple OTA Awards

A recognized thought leader in Generative AI and Agentic AI, Jothi Moorthy is an IBM Senior Customer Success Manager and AI Architect. She helps organizations deploy generative and agentic AI solutions, including building AI-powered coding assistants to accelerate developer productivity, using generative AI to access and summarize insights from legacy data sources, and connecting legacy enterprise systems with modern generative and agentic AI ecosystems. She also designs multi-agent workflows that automate manual, time-consuming processes, freeing employees to focus on higher-value tasks and significantly improving enterprise productivity.

She is part of the elite top 0.05% in IBM’s Advanced Technical Eminence program and among the top 1% of IBM technologists recognized at IBM Tech 2024. She holds multiple patents and has been awarded multiple Outstanding Technical Achievement Awards (OTAs) for her innovative solutions.

Ranked #35 on Favikon’s Top 200 Creators and in the Top 5% of LinkedIn voices, Jothi has built a strong global following, including 240K+ Facebook followers. She is a sought-after keynote speaker and serves as a Board Member for the Global Skill Development Council (GSDC), shaping conversations at the intersection of AI, leadership, and enterprise transformation.

She is the creator of Agentic AI News & Use Cases, one of the fastest-growing newsletters on Generative AI and Agentic AI with 3,000+ subscribers, delivering weekly news and in-depth analysis.

Her Generative AI Revolution Podcast brings together industry experts to highlight cutting-edge implementations and future possibilities. It is available on Spotify, YouTube, and Apple Podcasts.

As Publisher and Editor-in-Chief of Women in Tech Chronicles Magazine (non commercial), she amplifies the voices of women leaders in technology, celebrating authentic journeys and inspiring the next generation.

Views expressed are my own & don't represent my employer.

Just discovered I'm featured as a 𝐑𝐢𝐬𝐢𝐧𝐠 𝐒𝐭𝐚𝐫, ranked 𝐍𝐨.𝟑𝟓 in the 𝐓𝐨𝐩 𝟐𝟎𝟎 𝐂𝐫𝐞𝐚𝐭𝐨𝐫𝐬 for 𝐀𝐈 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 on LinkedIn by Favikon

🌟 "When you have that 𝐢𝐧𝐧𝐞𝐫 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 to 𝐛𝐞𝐭 𝐨𝐧 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟 – knowing that no matter what you do, you can do it 𝐰𝐞𝐥𝐥." – Aravind Srinivas, Perplexity CEO

𝐓𝐡𝐞 𝐮𝐧𝐢𝐯𝐞𝐫𝐬𝐞 𝐫𝐞𝐬𝐩𝐨𝐧𝐝𝐬 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝 𝐭𝐚𝐤𝐞𝐬 𝐧𝐨𝐭𝐢𝐜𝐞.

But here's what I know for sure:
This isn't about a ranking.
𝐓𝐡𝐢𝐬 𝐢𝐬 𝐚𝐛𝐨𝐮𝐭 𝐰𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐠𝐨 𝐀𝐋𝐋 𝐈𝐍 𝐨𝐧 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟.

𝐋𝐢𝐯𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐭𝐫𝐮𝐭𝐡 means betting on yourself completely:
→ 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐩𝐚𝐭𝐡.
→ Turning 𝐨𝐛𝐬𝐭𝐚𝐜𝐥𝐞𝐬 𝐢𝐧𝐭𝐨 𝐟𝐮𝐞𝐥.
→ Embracing the 𝐮𝐧𝐤𝐧𝐨𝐰𝐧.
→ Staying 𝐚𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜.
→ 𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐢𝐧𝐠 every moment.
→ Refusing to die with your 𝐠𝐢𝐟𝐭𝐬 still inside you.

When you choose to:
→ 𝐒𝐡𝐚𝐫𝐞 knowledge instead of hoarding it
→ 𝐋𝐞𝐚𝐫𝐧 in public, even when it felt uncomfortable
→ 𝐋𝐢𝐟𝐭 others as you climb
→ 𝐂𝐫𝐞𝐚𝐭𝐞 value for the community first
→ 𝐁𝐞𝐥𝐢𝐞𝐯𝐞 that helping others multiplies your own success

The obsession with 𝐀𝐈 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 isn't just about the technology.
→ It's about 𝐝𝐞𝐦𝐨𝐜𝐫𝐚𝐭𝐢𝐳𝐢𝐧𝐠 access to knowledge that can transform how we work and live.
→ Every post, every insight shared, every conversation started... it all compounds.

𝐓𝐡𝐢𝐬 𝐨𝐛𝐬𝐞𝐬𝐬𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐰𝐡𝐨 𝐈 𝐰𝐚𝐬 𝐦𝐞𝐚𝐧𝐭 𝐭𝐨 𝐛𝐞, 𝟐𝟒/𝟕, 𝐧𝐨 𝐞𝐱𝐜𝐞𝐩𝐭𝐢𝐨𝐧𝐬, 𝐧𝐨 𝐞𝐱𝐜𝐮𝐬𝐞𝐬, 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐦𝐲 𝐜𝐨𝐧𝐭𝐞𝐧𝐭.
→ 𝐈𝐭'𝐬 𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐦𝐲 𝐞𝐧𝐭𝐢𝐫𝐞 𝐞𝐱𝐢𝐬𝐭𝐞𝐧𝐜𝐞.

To everyone who's engaged with my content, challenged my thinking, or simply hit "like" on a post — thank you.
→ You're the reason this community grows.

To everyone on their own journey of becoming:

𝐘𝐨𝐮𝐫 𝐡𝐢𝐠𝐡𝐞𝐬𝐭 𝐬𝐞𝐥𝐟 𝐢𝐬 𝐰𝐚𝐢𝐭𝐢𝐧𝐠.

𝐓𝐡𝐞 𝐰𝐨𝐫𝐥𝐝 𝐧𝐞𝐞𝐝𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮'𝐫𝐞 𝐦𝐞𝐚𝐧𝐭 𝐭𝐨 𝐜𝐫𝐞𝐚𝐭𝐞.
𝐒𝐭𝐨𝐩 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐬𝐦𝐚𝐥𝐥.
𝐘𝐨𝐮𝐫 𝐭𝐢𝐦𝐞 𝐢𝐬 𝐧𝐨𝐰.

What version of yourself are you becoming? 👇

Jothi Moorthy

𝐌𝐨𝐬𝐭 𝐌𝐋 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐭𝐞𝐚𝐜𝐡 𝐲𝐨𝐮 𝐡𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐦𝐨𝐝𝐞𝐥𝐬.
𝐀𝐥𝐦𝐨𝐬𝐭 𝐧𝐨𝐧𝐞 𝐭𝐞𝐚𝐜𝐡 𝐲𝐨𝐮 𝐡𝐨𝐰 𝐭𝐨 𝐬𝐡𝐢𝐩 𝐭𝐡𝐞𝐦.

And that is the real bottleneck in AI today.
Because training a model in a notebook is one thing deploying it into a production system without waking up at night when it breaks? That is a completely different skill set.

𝐓𝐡𝐞 𝐜𝐨𝐮𝐫𝐬𝐞 𝐭𝐚𝐤𝐞𝐬 𝐲𝐨𝐮 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐨𝐟 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐚 𝐦𝐨𝐝𝐞𝐥 𝐥𝐢𝐯𝐞, 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:
1. Introduction to MLOps: https://lnkd.in/gGRZvSBD
2. Developing in Databricks: https://lnkd.in/gJGhxPKk
3. Getting started with MLflow: https://lnkd.in/gXiUH6aK
4. Log and register models with MLflow: https://lnkd.in/gFMvPZ83
5. Model serving architectures: https://lnkd.in/gHTuxfdB
6. Deploying a model serving endpoint: https://lnkd.in/gTRGhjNG
7. Databricks Asset Bundles: https://lnkd.in/gbWJuyGx
8. CI/CD and deployment strategies: https://lnkd.in/gDpyUreK
9. Intro to monitoring: https://lnkd.in/gcj3WVHY
10. Implementing Model Monitoring in Databricks: https://lnkd.in/ghyS7Hpv

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐧𝐨𝐰 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫:
1. Knowing how to fine-tune a model is not enough anymore.
2. Teams need people who can deploy and maintain them.
3. And those who can do both? They’re rare and valuable.

𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐩𝐚𝐫𝐭?
You will walk away with hands-on notebooks, practical workflows, and an understanding of how AI systems actually make it into production.

Stop being just a model builder.
Start becoming a full-stack AI engineer.

Huge shout out to the creators behind it.

♻️ Repost this if you want more engineers to learn how real-world AI gets deployed.
➕ Follow Jothi Moorthy for more

𝐌𝐈𝐓 𝐣𝐮𝐬𝐭 𝐝𝐫𝐨𝐩𝐩𝐞𝐝 𝐚 𝐠𝐨𝐥𝐝𝐦𝐢𝐧𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬.
Most people jumping into AI never really learn how deep learning actually works.

They know how to use models.
They know how to call APIs.
But ask them how backpropagation optimizes a network or why attention changed everything… and it gets quiet.

That is why this new free resource from MIT is such a big deal.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐟𝐢𝐧𝐝 𝐢𝐧𝐬𝐢𝐝𝐞:
* The math behind deep learning (in plain language)
* How training really works from gradient descent to optimization tricks
* CNNs, downsampling, and upsampling explained visually
* Graph neural networks and why they matter beyond social networks
* Transformers and attention the ideas powering today’s AI models
* Even advanced topics like GANs, diffusion models, and adversarial attacks

𝐀𝐧𝐝 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐩𝐚𝐫𝐭?
Every chapter comes with code notebooks and slides you can run instantly. No fluff. No marketing speak. Just the fundamentals the stuff that actually makes you a better AI engineer.

If you have ever felt like deep learning was a black box, this is how you open it.

📘 Check it out here: https://lnkd.in/gcG9iU79

♻️ Repost this to help your network avoid common pitfalls
➕ Follow Jothi Moorthy for more

#DeepLearning #AI #Learning #Resource

🔥 From 𝟱𝟬+ 𝗶𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝘀 to 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝗜𝗕𝗠’𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, Jessica Nahulan is redefining what it means to build with 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 and lift others along the way.

🚨 The September edition of Women in Tech Chronicles is here

👩‍💻 Meet Jessica Nahulan
𝗦𝘁𝗮𝗳𝗳 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗜𝗕𝗠 | 𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿 𝗡𝗼𝗺𝗶𝗻𝗲𝗲

🔹 Filed 𝟱𝟬+ 𝗽𝗮𝘁𝗲𝗻𝘁𝘀 across AI, IoT, 3D/4D printing, cybersecurity, and automation
🔹 Drives 𝗺𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽 across IBM, guiding first-time inventors and ideation sessions
🔹 Shapes 𝗜𝗣 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 as a member of 𝟯 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗯𝗼𝗮𝗿𝗱𝘀
🔹 Builds 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝘀𝗰𝗮𝗹𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 and publishes research in generative systems
🔹 Leads the 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗱𝘃𝗼𝗰𝗮𝗰𝘆 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 for IBM Data & AI

Her talent has been recognized globally:

🏆 𝗪𝗮𝘁𝘀𝗼𝗻𝘅 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 – Winner
🏆 𝗧𝗲𝗰𝗵𝗖𝗼𝗻𝗻𝗲𝗰𝘁 – Winner
🏆 𝗜𝗕𝗠 𝗪𝗼𝗿𝗹𝗱 𝗪𝗶𝗱𝗲 𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗖𝗼𝗱𝗲 – Winner
🏆 𝗜𝗕𝗠 𝗧𝗲𝗰𝗵 𝗔𝘄𝗮𝗿𝗱 – Recognized for 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 and 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗰𝗹𝗶𝗲𝗻𝘁 𝗶𝗺𝗽𝗮𝗰𝘁

💬 “The future should not just be intelligent. It should be inclusive, efficient, and meaningful.”

Jessica reminds us that true growth isn’t about climbing ladders — it’s about creating 𝗿𝗶𝗽𝗽𝗹𝗲𝘀 𝗼𝗳 𝗶𝗺𝗽𝗮𝗰𝘁 by lifting others.

💬 Know someone I should feature next? Drop a name.
♻️ Feel inspired? Repost

Jessica Nahulan- Women In Tech Chronicles Magazine

Most people think building with AI is about calling an LLM API and chaining a few prompts together. 

But, the real challenge starts when you leave the playground and step into production where your agents need to remember, recover, coordinate, and not burn the house down when 10 of them run in parallel.

And that is where this new open guide hits different.
📘 Agentic Design Patterns a 424-page handbook by Antonio Gulli (Director at Google) is probably the most practical resource I’ve seen on building real-world AI systems that scale.

𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐣𝐮𝐬𝐭 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐢𝐧𝐠 𝐰𝐡𝐚𝐭 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞, 𝐢𝐭 𝐰𝐚𝐥𝐤𝐬 𝐲𝐨𝐮 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞𝐦 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐭𝐡𝐞 𝐰𝐢𝐥𝐝:
* How to chain prompts and route tasks with structure
* How to manage memory across long-running sessions
* How to recover gracefully when agents get stuck mid-task
* How to build safety and guardrails into your workflows
* How to get multiple agents talking without chaos
* And how to integrate tools and context using MCP

𝐈𝐭 𝐢𝐬 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 𝐢𝐧𝐭𝐨 𝟐𝟏 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝟒 𝐛𝐢𝐠 𝐚𝐫𝐞𝐚𝐬:
1. Foundational: prompt chaining, routing, planning
2. Advanced systems: memory, learning, monitoring
3. Production concerns: error handling, evaluation, safety
4. Multi-agent architectures: coordination, reasoning, optimization

𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐧 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐚𝐬𝐤𝐢𝐧𝐠:
* “How do I persist memory across workflows?”
* “What happens when 10+ agents run at once?”
* “How do I keep things from breaking in production?”

This book has the answers. And it does not stop at theory it is packed with real code examples you can apply right now.

𝐑𝐞𝐚𝐝 𝐢𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/gabQDnDN

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𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 𝐢𝐭 𝐢𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞𝐲 𝐟𝐚𝐢𝐥.

And here is the part most engineers underestimate:
90% of AI agent issues don’t happen because the model is “bad.” They happen because of predictable, fixable errors in how the system is designed and orchestrated.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐝𝐨𝐧’𝐭 𝐛𝐫𝐞𝐚𝐤 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧, 𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝟓 𝐟𝐚𝐢𝐥𝐮𝐫𝐞 𝐩𝐨𝐢𝐧𝐭𝐬 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐦𝐚𝐬𝐭𝐞𝐫 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐭𝐨 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐞𝐦:

1. Input Errors: Garbage In, Garbage Out
If the input is unclear, incomplete, or wrongly formatted, the agent fails before it even starts.

* Always validate and sanitize inputs before they reach the model.
* Add guardrails to prevent nonsense responses.
* Example: A malformed query can confuse the agent and derail the task.

2. Observability & Logging: You Can’t Fix What You Can’t See
Most teams don’t track how often errors happen, so they stay invisible until it’s too late.

* Implement logging and monitoring from day one.
* Capture retries, edge cases, and response quality metrics.
* Over time, this data helps you anticipate and fix recurring issues faster.

3. Reasoning Errors: The Wrong Path Problem
Sometimes the model “thinks” in the wrong direction, leading to hallucinations or irrelevant actions.

* Add self-checks: make the agent review and validate its own output.
* If it’s unsure, let it re-query or seek new data before responding.
* Example: If a retrieval step fails, the agent should attempt a fallback query.

4. Execution Errors: When the Agent Gets Stuck
Agents can loop endlessly, forget context, or leave tasks incomplete.

* Set hard execution limits and fail-safes.
* If the task still doesn’t finish, hand it over to a human for resolution.
* Example: A travel planner bot might re-check the same flight repeatedly unless you set a stopping condition.

5. Tool/Action Errors: The Weakest Link
External APIs or tools often break or return bad data.

* Always keep backup tools ready.
* Implement graceful fallbacks e.g., switch weather APIs if one fails.
* Never assume external dependencies will behave perfectly.

Here is the mindset shift: AI agents do not fail randomly they fail predictably. And when you understand these 5 failure modes, you stop firefighting and start designing resilient systems.

𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐞𝐫𝐫𝐨𝐫𝐬 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮 𝐟𝐚𝐜𝐞𝐝 𝐰𝐡𝐢𝐥𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈?

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🔥 𝐒𝐚𝐧𝐝𝐛𝐨𝐱 𝟑.𝟎 𝐚𝐭 IBM 𝐓𝐞𝐜𝐡𝐗𝐜𝐡𝐚𝐧𝐠𝐞 𝟐𝟎𝟐𝟓.

The ultimate expo experience is back.
A dynamic hub where developers and technologists connect, collaborate, and get inspired.

🎉 We kicked off 𝐈𝐁𝐌 𝐓𝐞𝐜𝐡𝐗𝐜𝐡𝐚𝐧𝐠𝐞 𝟐𝟎𝟐𝟓 in style with the 𝐎𝐩𝐞𝐧𝐢𝐧𝐠 𝐍𝐢𝐠𝐡𝐭 𝐁𝐥𝐨𝐜𝐤 𝐏𝐚𝐫𝐭𝐲.
Great food. Interactive games. Refreshing drinks.
And an energy that set the tone for the entire week.

This isn’t just an expo.
It’s where 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐜𝐨𝐦𝐞𝐬 𝐚𝐥𝐢𝐯𝐞.
Where 𝐢𝐝𝐞𝐚𝐬 𝐭𝐮𝐫𝐧 𝐢𝐧𝐭𝐨 𝐚𝐜𝐭𝐢𝐨𝐧.
And where the 𝐈𝐁𝐌 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐛𝐮𝐢𝐥𝐝𝐬 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭.

If you missed the kickoff, here’s a glimpse of the vibe 👇
#IBMSandbox3 #IBMTechXchange

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