LLMs are amazing at pattern recognition and generating human-like responses. But when you’re dealing with something as sensitive as querying customer data or diagnosing production systems, “close enough” won’t cut it.

A timely example of this for us is investigating customer logs: the AI agent must pinpoint an endpoint for the current customer - never pulling data from somewhere else. Similarly, if you’re looking up account and transaction details, there’s zero room for mixing up two customers’ data.

That’s where deterministic behavior - actual guarantees - must come in.

At Markprompt, we’re tackling this problem with capabilities and rules:

• Capabilities define what your AI agent can do: run a workflow, call an external API, fetch customer data, etc.
• Rules specify how to do it safely: which parameters are allowed, which are off-limits, and which must be pulled from an approved, validated set.

The key thing here is building a way to eject from “LLM zone” into “strict code land”. That might mean a rule that says: “Account IDs must come from an internal list, never from user or AI-generated text.” This ensures you don’t accidentally query the wrong account or mix customers’ data. It’s no longer just about prompt engineering. It’s about structuring code-based guardrails so your agent can’t even attempt an invalid query.

In short, building reliable LLM-powered systems inevitably requires evolving from “prompting” to “coding”.