In software, the word framework means something. It means safety. Predictability. Discipline.
React didn’t become ubiquitous because it rendered HTML. It won because it gave developers a clean mental model for component-based architecture, declarative UI, and state-driven logic.
FastAPI didn’t succeed because it made HTTP easier — Flask did that first. It won because it gave structure: typed endpoints, auto-generated docs, modularity by default.
In contrast, most current agent “frameworks” are still trying to figure out whether they’re tools, libraries, or rough demos. They promise production readiness — but none deliver the agent framework architecture that real systems need.
Most AI Agent Frameworks Aren’t Frameworks at All
The truth is: most agent frameworks are just wrappers.
They wrap around LLMs, tool calls, or prompt formats.
They wrap orchestration logic in DAGs or SDKs.
They wrap demos in scaffolding and call it infrastructure.
But they lack the core characteristics of real developer frameworks:
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Consistent, composable primitives
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Explicit execution flow and state modeling
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Observability, testability, and version control
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Contracts and types that protect you at scale
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Modularity that doesn’t rot under complexity
What Real Frameworks Get Right
Let’s look at real-world, battle-tested developer frameworks:
✅ FastAPI
You define endpoints with type hints.
You get auto-validation, automatic docs, and runtime safety.
It scales from hobby project to enterprise API — without switching tools.
@app.get("/users/{id}")
def get_user(id: int) -> User:
...
✅ React
You describe the what, not the how.
State changes drive rendering.
You get isolation, composability, and testability out of the box.
function Button({ label }) {
return ;
}
These frameworks work because they’re opinionated about structure, but flexible in implementation. They let you build with confidence.
What AI Agent Frameworks Get Wrong
Here’s what today’s most popular agent frameworks try to do — and where they fall flat:
| Framework | Tries to Be | What It Gets Wrong |
|---|---|---|
| LangChain | A high-level abstraction layer | Overabstracted, inconsistent, black-box chaining |
| LangGraph | A declarative orchestration tool | DAG rigidity, fragile dict state, painful to refactor |
| OpenAI SDK | A plug-and-play agent builder | Overreliant on LLMs for logic, no state control or structure |
| AutoGen / CrewAI | Multi-agent orchestration | Complex YAML configs, opaque agent handoffs, brittle behavior |
They often:
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Obscure state handling
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Depend on string prompts with no contract or introspection
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Lack test harnesses or observability tooling
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Treat “workflow” as a DAG instead of as real software logic
Most are more like orchestration demos than frameworks.
The Core Problem: Abstraction Without Architecture
You can’t abstract what you don’t understand.
And most agent frameworks have abstracted away everything before defining anything.
They try to “hide the complexity” of agents — but agents are complexity:
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State
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Memory
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Context
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Ambiguity
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Human interaction
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Tool orchestration
When you hide these, you don’t simplify.
You just move the mess somewhere else — usually into the prompt or the developer’s head.
Real frameworks don’t do this. They give you leverage, not illusion.
What We Can Learn From the Greats
React, FastAPI, Rails — they didn’t succeed by chasing every use case.
They succeeded by doing a few things incredibly well.
React gave us:
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A mental model for UI as a function of state
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Components that were readable, testable, and composable
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The ability to reason about your app
FastAPI gave us:
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Typed contracts
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Runtime validation
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Docs for free
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Production-grade modularity out of the box
Rails gave us:
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Convention over configuration
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Enforced boundaries
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Batteries-included architecture
Agent frameworks aren’t there yet — not even close. But they can get there, if they stop chasing “magic” and start building real software foundations.
What’s Next: Toward Real Agentic Systems
So where do we go from here?
If you want to build agentic systems that last — you don’t need more plugins or prompt wrappers. You need principles.
In our next post, we’ll explore the best foundation we’ve seen so far:
The 12-Factor Agent model — a thoughtful, practical framework that brings software discipline to the world of LLM-powered agents.
But even that isn’t enough. There are deeper layers that still need to be built.
We’re going to walk through them. One by one.
Agents aren’t just loops and tools. They’re systems. And they deserve to be built like real software.
