It made me think: what if there’s a “Snakes & Ladders for AI Agents”?

I’ve always believed complex systems need visual maps you can feel. Back in 2014, I created the Data Science Metro Map. A dozen years later, the same instinct: AI agents need their own visual map.

While agentic AI is a new frontier, the traps are ancient

I’m borrowing from 20+ years shipping advanced AI systems across hundreds of deployments—the full stack, from data foundations to enterprise integration. I’ve watched multi-million-dollar efforts unravel from the same causes:

• Inadequate data foundations
• Context poisoning at scale
• Tool boundaries breached; agents become liability machines
• Runaway costs that kill momentum overnight
• Governance and trust blind spots that invite regulatory shutdown
• Rushing to benefits without building fundamentals

When you’ve seen the same failure bite five enterprises the same way, pattern recognition becomes prediction.

Introducing The Agentic Systems Ladder

81 cells from “should this agent exist?” to sustained business value. Each cell encodes a condition or milestone an agent system must meet to progress. Colors separate lifecycle stages; the questions on the left define the governing concern of that stage.

• Ladders compound advantage through sound structure.
• Snakes surface the failure modes that undo progress.

There are dozens of snakes and ladders in the real agent lifecycle. I surfaced the ones that bite most often:

• Orchestration Snake (Cell 37): Your multi-agent system splits a complex task beautifully. Agent A researches. Agent B analyzes. Agent C recommends. Agent D executes. The output is catastrophically wrong. Which agent failed? All of them contributed. None of them owned the outcome. Debugging becomes archaeology. Accountability vanishes into the orchestration layer.
• Adoption Snake (Cell 14): You built a technical marvel. No one uses it. Why? You automated the workflow instead of reimagining it. The agent mimics the human process—including the parts humans hate. Users route around it within a week.
• Tool Boundary Snake (Cell 32): You gave the agent broad tool access to “move fast.” It can read, write, delete, execute. Then it emails 10,000 customers with hallucinated data. Or drops production tables. Liability arrived faster than value.
• Sticker Shock Snake (Cell 64): Unit economics worked in the pilot with 10 queries/day. Your agent now runs autonomously, calling the LLM 847 times to complete a single task because you didn’t constrain the reasoning loop. Monthly bill: $340K. Budget: $12K.
• Agent Boss Snake (Cell 58): The agent is live and making decisions 24/7. Who owns it? Engineering says it’s a business process. Business says it’s a tech asset. When it makes a consequential error at 2am on Sunday, no one gets paged. Accountability evaporates while autonomous decisions continue.
• Governance Gap Snake (Cell 54): You moved fast. Trust and compliance were skipped. 43 of your 47 agents get shut down because no one documented decision logic or data lineage. Just autonomous black boxes making binding decisions.
• Drift Snake (Cell 72): Your golden dataset and goal instructions were perfect in Q1: dual-source critical components for resilience. Then geopolitical tensions made one source a regulatory risk overnight. Your agent is still confidently splitting orders 50/50—including to the now-sanctioned supplier. The real world changed. No one’s monitoring goal alignment. It’s optimizing beautifully for the wrong thing.

Day 2 is the hardest with agents

Building the agent is Day 1. Operating it is Day 2—and that’s where most teams stumble. They’re deploying agents without a clear view of what comes next, climbing structures that don’t compound, overlooking regressions they’ve seen before.

Will you encounter these failure modes firsthand—or recognize them in time?

Progress is never linear. It never has been.
Every ascent carries the possibility of reversal.

The Agentic Systems Ladder exists to make those reversals visible—before they become scars.

Your move. Which snakes will bite your enterprise first? Are you prepared?

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When I wrote my blog post Becoming a Data Scientist — Curriculum via Metromap, little did I know that it will receive a rousing feedback. So a big THANK YOU first of all! Over years a lot of people reached out to me with very kind words and how they use it as a guide in their data scientist journey. Also, many who sought permission to use the Metromap picture in their presentations as well as a few universities that also reached out to use it as part of their syllabus. Writing that post made me realize two things,

• Taking a step back, presenting a complex topic using a big-picture metaphor in a consumable and aesthetic fashion has value + use.
• Scared the pants off me to write my next post.

Now here I’m after almost 4 years to write on a topic that is very close to my heart and yet again see a lot of confusing fluff floating around — Artificial Intelligence (A.I). I’m pretty sure many of you including me would say yes to the following,

1. Everyone in your LinkedIn connections list has AI in their title.
2. Are getting flooded with articles that talk about A.I transforming industries and doomsday articles that go hand in hand.
3. See articles that are not only confusing and misleading but also don’t tend to be comprehensive.
4. Hear from someone is working on “AI for X”, where X can be anywhere from treating cancer to ordering lunch.

Pardon me for overgeneralizing, but I also see of folks who very loosely use the word A.I and have absolutely no clue/idea about what they are talking about. If you try to avoid them and try to seek the answer for “What is AI?”, you are bound to get flooded with conflicting views and very obfuscated terms and definitions. Just because someone is using a deep learning library/package, that doesn’t mean their system is intelligent. There is more to it. So here is my yet another modest attempt to convey via a picture — “Demystifying AI”.

If I have to pick a great starting definition for AI, I would vote for John McCarthy’s. He probably gave the most profound and yet a simple definition of A.I,

“science and engineering of making intelligent machines, especially intelligent computer programs”

AI is a fascinating area and I personally feel it will not do justice to explain it without looking at it from multiple dimensions. I have provided my point of view on AI in the following dimensions,

1. Guardrails for AI (starting with, “just because you can doesn’t mean you should”)
2. Core & essential building blocks
3. Types of data AI systems work on
4. Primary characteristics of an AI system
5. Different types of AI (yawn!)
6. Types of approaches to train / teach AI systems
7. Top Algorithms
8. Most common AI workloads/tasks
9. Common examples of AI systems at work
10. Dev Ops for AI — how are AI systems built?
11. Popular Platform, API’s, Libraries & Frameworks
12. Some of the absolute concepts and topics you need to take time in knowing
13. What’s next for AI?

My goal with this visual is to provide you all with an ability to look at the big picture of AI and yet look at it from various dimensions. I have consciously not gone into great depth and detail, but stuck to a fairly high-level to convey the concepts clearly. I could easily take each of these dimensions and blow it up in multiple levels of detail. I may try to do that in the future or might try to write a book. Feel free to leave your comments and constructive feedback.

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Given how critical visualization is for data science, ironically I was not able to find (except for a few), pragmatic and yet visual representation of what it takes to become a data scientist. So here is my modest attempt at creating a curriculum, a learning plan that one can use in this becoming a data scientist journey. I took inspiration from the metro maps and used it to depict the learning path. I organized the overall plan progressively into the following areas / domains,

1. Fundamentals
2. Statistics
3. Programming
4. Machine Learning
5. Text Mining / Natural Language Processing
6. Data Visualization
7. Big Data
8. Data Ingestion
9. Data Munging
10. Toolbox

Each area  / domain is represented as a “metro line”, with the stations depicting the topics you must learn / master / understand in a progressive fashion. The idea is you pick a line, catch a train and go thru all the stations (topics) till you reach the final destination (or) switch to the next line. I have progressively marked each station (line) 1 thru 10 to indicate the order in which you travel. You can use this as an individual learning plan to identify the areas you most want to develop and the acquire skills. By no means this is the end; but a solid start. Feel free to leave your comments and constructive feedback.

PS: I did not want to impose the use of any commercial tools in this plan. I have based this plan on tools/libraries available as open source for the most part. If you have access to a commercial software such as IBM SPSS or SAS Enterprise Miner, by all means go for it. The plan still holds good.

PS: I originally wanted to create an interactive visualization using D3.js or InfoVis. But wanted to get this out quickly. Maybe I will do an interactive map in the next iteration.

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