Not all “AI” is the same. The term covers everything from simple automation to autonomous agents, and conflating them causes real problems.
I’ve watched teams apply agent-level thinking to automation-level problems, then wonder why their “AI initiative” feels over-engineered. Or worse: build deterministic workflows when they actually need an agent, then blame the technology when it can’t adapt.
Understanding where your work falls on this spectrum helps you choose the right tools and set realistic expectations.
The key distinction: Deterministic vs. Probabilistic
Deterministic: If your processes are pre-defined with fixed rules, you’re in deterministic territory. Same input, same output, every time. Think: spreadsheet formulas, trigger-based workflows, if/then logic.
Probabilistic: If AI has discretion to adapt its approach based on context, you’re in probabilistic territory. Same goal, different paths to get there. Think: “research these companies,” “write a personalized email,” “monitor for significant changes.”
Here’s how it breaks down:
Level 0: Workflow Automation
“You design it, tools execute it”
AI coding assistants and automation platforms help you build traditional software and workflows faster. These are “if this, then that” configurations. Workflows are trigger-based and execution is completely predictable.
You’re the architect. AI just speeds up the building.
Examples:
• Zapier: New Typeform submission → Add to Google Sheets → Send Slack notification
• Claude Code builds your dashboard from API data
• Automated report generation from database queries
Common tools: Claude Code, Cursor, Copilot, Zapier, n8n, Make
Level 1: AI-Enabled Workflow
“AI contributes, you orchestrate”
Somewhere in the workflow, AI generates content or makes a decision. The structure is still rigid and pre-defined, but AI adds intelligence at specific steps you specify.
Inputs and outputs are predictable even if the exact content varies.
Examples:
• Lead enrichment: Email arrives → AI researches company → AI writes personalized intro → You approve → Send
• Content pipeline: Topic input → AI drafts post → Human edits → AI generates social variants → Schedule
• Support routing: Ticket arrives → AI analyzes sentiment → Routes to appropriate team
Common tools: Zapier AI, Make, Lindy, API integrations with Claude/GPT
Level 2: Agent
“The agent decides how to accomplish your goal”
You give the agent a goal, tools, instructions, and knowledge. The agent determines the best approach, adapts to unexpected inputs, and handles diverse scenarios without predefined paths.
It has discretion over how to use its tools and when to ask for help.
Examples:
• “Research these 20 companies and build a target list” — Agent decides: search strategy, data sources, enrichment depth, formatting
• “Monitor competitors and alert me to significant changes” — Agent decides: what’s significant, how to verify, synthesis format
• Customer success agent: Monitors account health, proactively reaches out, answers questions, escalates when needed
Common tools: Relevance AI, Lindy, custom builds, LangChain, CrewAI, AutoGPT
The spectrum isn’t about which level is “better”
It’s about matching the right approach to your problem.
Start with the simplest level that solves your need. Move up only when the deterministic approach can’t handle the variability you’re facing.
A workflow automation that runs perfectly every time beats an over-engineered agent that introduces unnecessary complexity. And an agent that adapts to unpredictable scenarios beats a brittle workflow that breaks on edge cases.
Know where you are on the spectrum. Build accordingly.