Thinking in Systems

Patterns and reflections from long practice.

The AI Agentic Systems Ladder

There’s something meditative about board games. Growing up, I loved Snakes & Ladders. Roll the dice, climb, fall, repeat. Years later, I learned its origins: Moksha Patam, an ancient Indian game where ladders were virtues lifting you toward enlightenment, snakes were vices dragging you back.

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.

The Agentic Systems Ladder: 81-cell board game showing AI agent lifecycle from inception to sustained value, with ladders representing accelerators and snakes representing failure modes
The Agentic Systems Ladder

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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