Why AI Agents are Unpredictable

Welcome to Day 2 of #30DaysOfTrust.

Why AI Agents are Unpredictable (And why traditional code can't tame them). 🤖

For the last 40 years, software has been built like a train track. 🛤️

Developers wrote strict, line-by-line instructions. If X happens, do Y. The software was the train—fast, efficient, but it could only travel exactly where we laid the tracks. It was 100% deterministic and predictable. If something unexpected happened, the app crashed.

Agentic AI changes everything. We are no longer building train tracks. We are building off-road autonomous rovers. 🚙

In an Agentic Architecture, you don't write the exact path. Instead, you give the AI two things:

1. A Prompt: The final destination or goal (e.g., "Resolve this customer's billing ticket").
2. Tools: The capabilities it can use (e.g., Access to the CRM, a calculator, an email sender).

The Reasoning Engine 🧠

The AI acts as a "reasoning engine." It looks at the goal, looks at its available tools, and makes a real-time decision on how to connect the dots.

It hasn't been programmed with a step-by-step recipe. It is figuring it out on the fly. If it tries to use the CRM and gets an error, it doesn't just crash. It "thinks" about the error, course-corrects, and tries another tool.

The Security Catch 🛡️

Because the AI is making probabilistic guesses in real-time, it is inherently unpredictable. You no longer control the exact path the code takes.

If an AI has the "power" to write to your database, and it hallucinates or misunderstands its goal... it will confidently march off-road and execute that hallucination.

Traditional security assumes a human is clicking the buttons. But when the software itself is deciding which buttons to click, we need a fundamentally new approach to trust.

Thoughts on the train vs. rover analogy? Let me know below! 👇

#AgenticEra #30DaysOfTrust #BuildInPublic #AASB #SecuriX #Cybersecurity #AI