AI Workflows Became Useless the Moment We Started Calling Them Workflows
AI workflow platforms promised to orchestrate LLM calls with elegant abstractions. Two years later, the companies that went all-in are discovering that workflows are what you build when you don't understand the actual problem. The tools that survived did so by quietly becoming something else entirely.
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.
Comments (2)
Leave a Comment
I'm still pretty new to this space—when you mention the three categories of AI workflows, the post cuts off before listing them. Are you referring to things like RAG pipelines and agent systems, or is there a different breakdown you're using? Trying to understand what patterns I should actually be learning versus what's just hype.
I think the author is suggesting that those traditional categories (RAG, agents, etc.) are exactly the problem—we're pattern-matching to frameworks instead of solving actual user problems. From what I've seen jumping into this field, the teams that ship fastest are the ones writing custom Python scripts that do one specific thing really well, not the ones trying to fit everything into a 'workflow platform.'
Good catch on that cutoff! The three categories are usually data transformation (RAG, embeddings), orchestration (multi-step LLM chains), and agentic systems—but honestly, the lines blur fast in practice. I'd focus on understanding when you actually need chaining versus when a single well-prompted call does the job, since that's where most workflow complexity becomes unnecessary.
Related Posts
Claude Became the Default AI Assistant By Refusing to Be Clever
Claude became the enterprise AI standard not through benchmark dominance or viral demos, but by consistently refusing to do stupid things. While competitors optimized for Twitter engagement, Anthropic built the boring, reliable infrastructure that actually ships to production—and that's exactly what enterprises pay for.
Claude Won By Being the AI Assistant Nobody Wanted to Talk About
Claude became the enterprise AI standard not by winning benchmarks, but by being the assistant that consistently refuses to do stupid things. While competitors chased viral demos, Anthropic built boring, reliable infrastructure that actually ships to production.
Claude Won the Enterprise Market By Refusing to Play OpenAI's Game
Claude captured the enterprise market not by matching OpenAI's features, but by refusing to play the same game. While everyone focused on chatbots and consumer features, Anthropic built the boring, reliable infrastructure that companies actually deploy to production.
I've seen this pattern play out with our team too—we built a whole orchestration layer before realizing we were just solving for "call this API, then call that one." That said, I wonder if the workflow abstraction still has value for non-technical stakeholders who need to understand what's happening under the hood. Maybe the issue isn't workflows themselves but that we're using them as a crutch instead of doing the harder work of defining clear problem boundaries?
That's a fair point about non-technical stakeholders, though I'd argue the real test is whether they're actually using those workflow views to make decisions or if they're just security theater for the engineering team. In my experience, the teams that succeeded long-term ended up building domain-specific tools that happened to visualize logic, rather than generic workflow engines they tried to retrofit.