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.

Fully autonomous agents are not yet reliable for complex production use cases because accuracy collapses when chaining multiple probabilistic steps. Zapier's CEO recommends a hybrid "agentic workflow" approach: embed a single, decisive agent within an otherwise deterministic, structured workflow to ensure reliability while still leveraging LLM intelligence.

An effective AI agent isn't a single, all-knowing model. It's custom software that uses LLMs for inference only when necessary, relying on cheaper, deterministic code for most tasks. The goal is to maximize outcomes, not token usage, by blending AI with traditional software.

The key to creating effective and reliable AI workflows is distinguishing between tasks AI excels at (mechanical, repetitive actions) and those it struggles with (judgment, nuanced decisions). Focus on automating the mechanical parts first to build a valuable and trustworthy product.

Wade Foster argues that for most business automation, delegating tasks to an AI agent is inefficient. Instead, an AI should be used to *build* a deterministic workflow (i.e., traditional code) which is cheaper and more reliable for execution. AI's reasoning is best reserved only for parts that truly require it.

Poetic's architecture offers a hybrid approach to overcome the limitations of pure code or pure AI agents. Workflows execute as reliable, deterministic code. However, if the underlying application changes, an AI layer intervenes to "heal" the process, providing adaptability without sacrificing precision.

Separate AI's role. Use an AI assistant to write reliable, deterministic code for structuring data (e.g., pulling Slack messages via API). Then, apply a live AI model only for the subjective task, like categorizing message urgency. This hybrid approach creates a more robust and controllable system.

Relying solely on natural language prompts like 'always do this' is unreliable for enterprise AI. LLMs struggle with deterministic logic. Salesforce developed 'AgentForce Script,' a dedicated language to enforce rules and ensure consistent, repeatable performance for critical business workflows, blending it with LLM reasoning.