For core business automations, agentic AIs that "guess" are expensive and unreliable. A superior approach uses tools that convert natural language into deterministic, code-like workflows, which run consistently and use AI only when necessary.

Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.

Unlike predictable automation technologies, AI is stochastic and can produce unexpected results, making it unsuitable for unsupervised, autonomous tasks. Its primary strength lies in augmenting human experts who can guide, filter, and interpret its output in a collaborative process.

The intelligence layer of AI is advancing rapidly, but enterprise adoption lags because a crucial control layer is underdeveloped. The next wave of AI development will focus on providing observability, control, and traceability, allowing businesses to audit and course-correct an AI agent's decisions.

AI will not replace enterprise software because AI models are non-deterministic (probabilistic), while enterprise systems require deterministic (100% reliable) execution for critical functions. Enterprise software will act as the execution layer that harnesses AI's "thinking" capabilities within safe, predictable workflows.

The focus in AI engineering has shifted from the agent itself to the surrounding system or 'harness.' This includes managing workflows, context, permissions, and tools. Engineering these reliable systems is now seen as more critical for delivering value than simply prompting a more powerful model.

Traditional systems can be controlled with simple, deterministic rules. Because modern AI agents are inherently unpredictable, effective governance requires using another layer of AI. A specialized AI must monitor, interpret, and block the actions of other agents in real-time.

To control costs, security, and governance, enterprises are moving from interactive, ad-hoc agent use to a 'software factory' model. This approach systematizes the entire work lifecycle, automating processes and minimizing the risks associated with inconsistent human operation of powerful AI tools.

Fully autonomous AI agents are not yet viable in enterprises. Alloy Automation builds "semi-deterministic" agents that combine AI's reasoning with deterministic workflows, escalating to a human when confidence is low to ensure safety and compliance.