November 26, 2025 6 minute read
For the past few decades, Engineering meant one thing: removing ambiguity. It meant defining strict interfaces, enforcing type safety, and ensuring that Input A \+ Code B \= Output C .
Traditional software engineering is Deterministic. We play the role of Traffic Controllers; we own the roads, the lights, and the laws. We decide exactly where data goes and when. Agent Engineering is Probabilistic. We are Dispatchers. We give instructions to a driver (an LLM) who might take a shortcut, get lost, or decide to drive on the sidewalk because it "seemed faster."
It is a paradox that junior engineers often ship functional agents faster than seniors. Why? The more senior the engineer, the less they tend to trust the reasoning and instruction-following capabilities of the Agent. We fight the model and try to "code away" the probabilistic nature.
Here are 5 examples where traditional engineering habits clash with the new reality of Agent Engineering.
1. Text is the New State
In traditional engineering, we model the world with data structures. We define schemas, interfaces, and strict types. It feels safe because it is predictable. We instinctively try to force Agents into this box.
The Trap: Real-world intents, preferences, or configurations are rarely binary/structured. User inputs are continuous (natural language) rather than discrete (structured fields).
Text is the new State. We must abandon the comfort of booleans in favor of semantic meaning.
Imagine a Deep Research plan approval use case where the user says, "This plan looks good, but please focus on the US market." A deterministic system forces this into is_approved: true/false and we lobotomize the context.
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