Traditional software does what you program it to do. If you write “when the user clicks this, send that request,” the program does exactly that — no more, no less. AI agents are different: they use models that can interpret natural language, make decisions, and take multi-step actions in the world. That shift from deterministic scripts to adaptive behaviour is what separates agents from the software we’re used to. Here’s how to think about the difference.
Determinism vs Behaviour
Traditional software is deterministic. Same input, same output. You can trace execution, write unit tests, and reason about every path. AI agents rely on models that produce probabilistic outputs. Same prompt might yield different responses; the agent might choose a different tool or step than last time. That doesn’t mean agents are random — they’re often highly reliable for well-scoped tasks — but you can’t enumerate every path. You test for behaviour and outcomes, not for exact code paths.
Instructions vs Code
With traditional software, you specify behaviour in code: conditionals, loops, API calls. With agents, you often specify behaviour in natural language (prompts) plus a set of tools the agent can call. The “program” is the prompt and the tool definitions; the model figures out when and how to use them. That makes agents flexible and fast to iterate on — change the prompt, get different behaviour — but also harder to audit. You’re optimising for “does it do the right thing?” rather than “does this line of code run?”
Scope and Boundaries
Traditional software has a fixed scope. It does the tasks you implemented. An agent can tackle open-ended goals: “summarise these emails and suggest replies” or “find the best flight and book it.” The agent decides which tools to use and in what order. That flexibility is powerful but also risky — the agent might do something you didn’t anticipate. So agent design is as much about boundaries (what the agent is allowed to do, what it can access) as it is about capability. Traditional software has implicit boundaries (the code); agents need explicit ones (permissions, tool limits, guardrails).
When to Use Which
Use traditional software when the task is well-defined, repeatable, and you need guarantees (e.g. payments, compliance, safety-critical paths). Use agents when the task involves interpretation, choice, or natural language, and when a bit of variability is acceptable. Many systems will be hybrids: traditional pipelines for the critical path, agents for the flexible parts (drafting, classification, search). The difference isn’t “agents replace software” — it’s “agents are a new kind of component” that you plug in where behaviour matters more than determinism.
Debugging and Observability
When traditional software fails, you have stack traces, logs, and reproducible steps. When an agent fails, you might see “it chose the wrong tool” or “it gave a bad answer” — and the same prompt might work next time. So observability for agents is different: you need to log prompts, tool calls, and outputs, and to analyse behaviour statistically rather than line-by-line. Good agent design includes clear logging, evaluation datasets, and the ability to replay and compare runs. You’re debugging behaviour, not code.
Summary
Traditional software: deterministic, code-defined, fixed scope, fully auditable. AI agents: behaviour-based, prompt- and tool-defined, flexible scope, outcome-tested. Understanding the difference helps you choose the right tool and design systems that use both effectively.
