In the pantheon of programming languages, few have had such a profound impact yet remain so misunderstood as Smalltalk. Created in the 1970s at Xerox PARC by Alan Kay, Dan Ingalls, and Adele Goldberg, Smalltalk didn’t just introduce object-oriented programming to the world — it created an entire vision of computing that we’re only now beginning to fully appreciate in the age of AI agents.
The Rise and Fall of a Revolutionary Idea
Smalltalk emerged from a radical question: what if computers could be as dynamic and interactive as human thought itself? The language embodied Kay’s vision of the “Dynabook” — a personal computing device that would be as accessible as a book but as powerful as any computing system. Everything in Smalltalk was an object, including classes, methods, and even the environment itself. The system was reflexive, introspective, and alive in ways that most languages today still struggle to achieve.
But Smalltalk’s commercial trajectory tells a different story. By the late 1990s and early 2000s, the language had largely faded from mainstream use. Several factors contributed to this decline:
Performance perception plagued early implementations. While the later HotSpot JVM for Java borrowed heavily from Smalltalk VM optimization techniques, the initial performance gap created a narrative that Smalltalk was “too slow” for serious work. This perception persisted even after implementations like VisualWorks proved otherwise.
The proprietary model backfired spectacularly. Companies like ParcPlace and Digitalk tried to monetize their Smalltalk implementations at a time when the industry was moving toward open-source models. By the time Squeak and Pharo emerged as truly open implementations, Java had captured the enterprise mindset with Sun’s backing and the promise of “write once, run anywhere.”
Cultural momentum shifted to C-syntax languages. The C family — C++, Java, C#, JavaScript — created a linguistic continuity that made it easier for developers to move between them. Smalltalk’s distinctive syntax, with its message-passing notation and lack of traditional control structures, required a cognitive shift that many organizations weren’t willing to make.
Static typing won the enterprise. The 1990s and 2000s saw large enterprises gravitating toward statically-typed languages that promised early error detection and better tooling for large teams. Smalltalk’s dynamic nature, while powerful, was seen as risky for mission-critical systems.
Why Smalltalk Matters More Than Ever for AI Agents
Here’s where the story takes an unexpected turn. The very features that led to Smalltalk’s commercial decline are exactly what make it relevant — even essential — for understanding modern AI agent architectures.
Live Programming and Continuous Adaptation
Smalltalk pioneered the concept of a “live” programming environment where you could modify running code without stopping the system. You didn’t compile-and-run; you shaped a living system. This is precisely what AI agents need to do — adapt their behavior in real-time based on new information without complete restarts.
Modern reinforcement learning systems struggle with this. They require training cycles, deployment pipelines, and careful orchestration. Smalltalk solved this in the 1970s by making the runtime environment itself programmable and introspectable. An AI agent built with Smalltalk principles could modify its own behavior patterns, inspect its decision-making processes, and evolve without external recompilation.
Message Passing as First-Class Semantics
Smalltalk’s fundamental abstraction wasn’t the function call — it was the message. When you write account deposit: 100, you're sending a message to an object, not invoking a method. The receiving object decides how to handle that message. This indirection, this late binding, creates a semantic flexibility that's crucial for AI agents operating in uncertain environments.
Consider how this maps to agent architectures. An AI agent doesn’t execute deterministic functions; it interprets intentions and context. The message-passing paradigm naturally models this. Agent communication languages like FIPA ACL tried to formalize this in the 2000s, but they built complex protocols on top of languages that didn’t natively understand message semantics. Smalltalk had it from day one.
Reflexive Introspection
Every object in Smalltalk can answer questions about itself. What class am I? What methods do I understand? Who created me? This capability — introspection and reflection — is fundamental to explainable AI. If an AI agent makes a decision, we need to trace its reasoning. In Smalltalk, the system can interrogate itself at any level of abstraction.
Modern machine learning systems are notoriously opaque. We’re building elaborate interpretation frameworks — SHAP values, attention visualization, feature attribution — to understand what our models are doing. Smalltalk’s approach suggests a different path: build the explainability into the fundamental architecture. Make every decision object capable of explaining itself.
Image-Based Persistence
Smalltalk doesn’t save code to text files. It saves the entire running state of the system — the “image” — which can be suspended and resumed. This sounds archaic until you consider AI agent memory. How do you persist an agent’s learned experiences, its accumulated context, its evolving understanding? Not as log files or database records, but as a living state that can be hibernated and awakened.
The Smalltalk image model prefigures what we now call “agent checkpointing” or “episodic memory systems.” You’re not serializing data structures; you’re capturing the entire cognitive state.
Objects All the Way Down
In Smalltalk, there are no primitive types, no special cases, no escape hatches to “the metal.” Everything is an object responding to messages. This creates a uniform semantic layer that’s ideal for knowledge representation. Modern knowledge graphs struggle with the impedance mismatch between different data models. Smalltalk’s radical uniformity suggests that the solution isn’t more abstraction layers, but a simpler, more consistent foundational model.
The Smalltalk Pattern in Modern AI Systems
If you look carefully at cutting-edge AI agent frameworks, you’ll see Smalltalk’s patterns re-emerging:
Actor model systems like Erlang and Akka are essentially distributed message-passing systems — Smalltalk scaled across nodes.
Autonomous agent frameworks are rediscovering that agents need to be introspectable, adaptable, and capable of meta-reasoning about their own processes.
Live coding environments for AI development are recreating Smalltalk’s interactive development model because it maps better to iterative agent refinement than traditional compile-test-deploy cycles.
Self-modifying code in agent systems is exploring what Smalltalk developers took for granted — that the boundary between code and data should be fluid.
What We Lost and What We’re Rebuilding
The tragedy of Smalltalk isn’t that it failed commercially. Many brilliant technologies fail commercially. The tragedy is that we spent three decades rebuilding its insights piecemeal in languages that fight against them. We added reflection to Java through elaborate APIs. We created message queues to simulate message passing. We built hot-reload systems to approximate live coding. We developed aspect-oriented programming to recover some of Smalltalk’s flexibility.
For AI agents specifically, we’re now building elaborate architectures on top of Python — a language never designed for the kind of reflexive, adaptive, message-oriented computing that agents require. We’re creating frameworks, libraries, and patterns to compensate for foundational mismatches.
Smalltalk had these capabilities as first-class language features from the beginning.
The Path Forward
I’m not suggesting we all switch to Smalltalk tomorrow. Languages exist in ecosystems, and those ecosystems have enormous inertia. But as we architect the next generation of AI agent systems, we should study Smalltalk not as a historical curiosity but as a design pattern language for adaptive, introspectable, message-oriented computing.
The insights matter:
Design for introspection from the ground up, not as an afterthought
Make message passing the fundamental abstraction, not procedure calls
Build live, mutable systems that can adapt without restarts
Create uniform semantic models that don’t require constant translation between layers
Treat the agent’s entire state as a first-class entity that can be inspected, persisted, and reasoned about
Smalltalk died commercially because it was too radical for the enterprise computing of the 1990s. But its core insights — about objects, messages, introspection, and live systems — are exactly what we need for AI agents that must operate in uncertain, dynamic environments while remaining explainable and adaptable.
The language may have faded, but its vision of computing is more relevant than ever. In the age of AI agents, perhaps it’s time to revisit what Alan Kay and his colleagues at PARC knew all along: that computation is fundamentally about objects sending messages in a live, introspectable environment. Everything else is just implementation details.