Then there’s Michael Fauscette, chief analyst at Arion Research LLC, who says the key decision framework for CIOs is to use deterministic code wherever outcomes must be predictable, auditable, and repeatable, and reserve agentic and probabilistic approaches for tasks that require reasoning, judgment, or handling ambiguity at scale. “In practice, that means letting agents handle the messy middle of workflows, like interpretation, summarization, and decision support,” he says, “while traditional code owns data validation, transaction processing, compliance logic, and structured output generation.”
However, Sangeet Paul Choudary, a C-level advisor on AI strategy and author, believes it really depends on the tolerance for failure versus the upside of innovation. “Agents can help come up with novel solutions to problems coders wouldn’t have thought through, so where that’s valuable, I’d design with agents at the core, and code as checks and balances,” he says. “In scenarios with low tolerance for failure, though, I’d flip it.”
If you’re working on the agentic side first, as part of a new software development project, it’s also important to optimize your agentic code and outputs first. You generally want to get this as accurate and repeatable as possible before deciding when and where to bring in the guardrails of traditional code. As an example, poor prompting or less than optimal LLMs for a specific use case, can shift the boundaries and might even make you under-utilize the power of your agents in a search for the safety of traditional code.