"Today's agentic AI is highly capable within a narrow frame. It excels in environments where expectations are clearly defined, rules are prescriptive, and goals are structurally consistent. If you need code analyzed, a test written, or a bug flagged based on past patterns, it delivers. These systems operate like trains on fixed tracks: fast, efficient, and capable of navigating anywhere tracks are laid. But when the business shifts direction-or strategic bias changes-AI agents stay on course, unaware the destination has moved."
"That fear, while understandable, does not reflect how these systems actually work today, or where they're realistically heading in the near term. Despite the noise, agentic AI is still confined to deterministic systems. It can write, refactor, and validate code. It can reason through patterns. But the moment ambiguity enters the equation-where human priorities shift, where trade-offs aren't binary, where empathy and interpretation are required-it falls short."
Agentic AI handles deterministic engineering tasks like writing, refactoring, validating code, and identifying patterns when requirements and rules are clear. These systems perform well under prescriptive goals and consistent structure, producing tests, analyzing code, and flagging bugs. Engineering and product work depend on shifting human priorities, strategic decisions, and nuanced trade-offs that require empathy, interpretation, and contextual judgment. When business direction or strategy changes, AI agents remain constrained by their training and fixed objectives and can produce misaligned or counterproductive output. Human engineers retain responsibility for architecture, context, prioritization, and translating ambiguous requirements into actionable technical decisions.
Read at InfoWorld
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