AI Workflows Became Useless the Moment We Started Calling Them Workflows

The Problem With Naming Things

The term "AI workflow" emerged around 2023 when companies realized they needed to sound strategic about using ChatGPT. Marketing teams started diagramming boxes and arrows showing how prompts flow through different models. Engineering teams built orchestration layers to chain API calls together. Everyone agreed these were "workflows" because the alternative—admitting we were just gluing API calls together with duct tape—sounded less impressive on roadmaps.

Two years later, the companies that went all-in on "AI workflow platforms" are discovering what should have been obvious: workflows are what you build when you don't understand the actual problem. The abstraction became the product, and now we're stuck maintaining infrastructure for a metaphor that never quite fit.

What We're Actually Building

Most "AI workflows" fall into three categories, and none of them are actually workflows in any meaningful sense.

The first category is sequential API calls dressed up with retry logic. You send a prompt to GPT-4, take the output, send it to Claude for refinement, maybe hit a specialized model for fact-checking. Call it a "workflow" if you want, but it's just a for-loop with better marketing. The complexity isn't in orchestrating the sequence—it's in handling the 47 ways these calls can fail, and no workflow platform solves that.

The second category is human-in-the-loop approval gates. The AI generates something, a human reviews it, maybe makes edits, then it moves to the next step. This is literally just a content management system with AI features bolted on. We built this exact infrastructure in 2010 for managing blog posts, and renaming it an "AI workflow" doesn't make it novel.

The third category is the only interesting one: systems where AI agents make decisions that affect other AI agents. But here's the problem—these aren't workflows either. They're distributed systems with all the coordination problems that implies. Calling them workflows is like calling Kubernetes a "server workflow platform." Technically true, completely missing the point.

Why Workflow Thinking Fails

Workflows assume predictable state transitions. Step A completes, you move to step B. The output of one stage becomes the input to the next. This mental model works great for invoice processing or expense approvals because the state space is constrained and the transitions are deterministic.

AI operations break every assumption workflows depend on. The same prompt with the same model produces different outputs. Intermediate states aren't clean—you get partial completions, hallucinations, refusals. The "workflow" needs to handle cases where step 3 decides step 1 was wrong and needs redoing. Traditional workflow engines have no vocabulary for this.

The companies succeeding with AI aren't building workflow platforms. They're building systems that treat AI operations as unreliable components in a resilient architecture. The difference matters. Workflows optimize for orchestration; resilient systems optimize for recovery. When your "workflow step" fails 15% of the time, recovery is the entire product.

The Tools That Actually Work

LangChain became the poster child for AI workflows, and its evolution tells the whole story. The initial pitch was beautiful: chain LLM calls together with a clean abstraction. Developers loved the idea. Then they tried using it in production and discovered the abstraction leaked everywhere. The real code wasn't the chains—it was the error handling, the retry logic, the fallbacks, the monitoring.

The LangChain that survived isn't really about chaining anymore. It's about providing utilities for the messy parts: prompt templates, output parsing, integration adapters. The "workflow" abstraction quietly became optional. The developers who stuck with it are the ones who stopped thinking in workflows and started thinking in components.

Meanwhile, the teams building actual AI products mostly abandoned these frameworks entirely. They write direct API calls with custom retry logic, store intermediate states in databases, and handle coordination in application code. It's less elegant, more boilerplate, and actually works in production. The workflow abstraction promised to hide complexity but just moved it around.

What Comes After Workflows

The next generation of AI tooling is already here, and it looks nothing like workflow platforms. It looks like observability tools that treat AI calls as distributed traces. It looks like feature stores that version prompts the way we version code. It looks like testing frameworks that handle non-deterministic outputs.

The pattern is clear: treat AI operations as infrastructure primitives, not workflow steps. Build the same reliability patterns we built for microservices—circuit breakers, bulkheads, timeouts, retries. Stop trying to orchestrate AI calls and start building systems that survive AI failures.

The companies still selling "AI workflow platforms" are optimizing for the wrong metric. They're measuring how easily you can diagram your AI operations, not how reliably you can run them. The market is figuring this out. The workflow platforms that survive will do so by quietly becoming something else entirely—probably infrastructure platforms that happen to have a workflow UI for the sales demo.

The Real Competition

The competition isn't better workflow orchestration. It's making workflows obsolete by building AI capabilities directly into products where the orchestration becomes invisible. The best AI products don't expose "workflows" to users at all. They expose outcomes.

Notion AI doesn't ask you to configure a workflow for document generation. It just generates the document. Midjourney doesn't make you orchestrate image generation steps. It produces images. The workflow exists, obviously, but it's an implementation detail, not the product surface.

This is where the market is heading: AI capabilities that disappear into purpose-built interfaces. The "workflow" becomes internal plumbing, not a feature to sell. The platforms that win will be the ones that make building this plumbing easier, not the ones that make diagramming workflows prettier.

We're two years into the AI tooling cycle, and the pattern is familiar. The first wave of tools optimized for adoption—make it easy to get started, make it look like existing mental models, make it feel safe. The second wave optimizes for production—handle the edge cases, survive the failures, scale past the demo. Workflow platforms were the first wave. We're overdue for the second.