5 min read Engineering

Building Systems That Think

Reflections on designing distributed systems with principles borrowed from cognitive science.

The microservice at 3am was hallucinating again.

Not in the AGI sense—in the medical sense. After 72 hours of intermittent network partitions, our order processing service was making decisions based on stale data, convinced it was operating normally. The logs showed perfect internal consistency. The business showed declining revenue.

This is when I started thinking about distributed systems as cognitive architectures.

The Perception Problem

A service only knows what its sensors tell it. When you call an API, read from a database, or check a message queue, you’re sampling reality through an interface. Those interfaces lie—not maliciously, but inevitably.

Networks partition. Clocks drift. Caches stale. The CAP theorem isn’t just about databases; it’s about the fundamental impossibility of perfect knowledge in distributed environments.

This is also the problem consciousness solves. Your brain doesn’t have direct access to reality—only to electrical signals from sensory organs. Vision, hearing, proprioception: all models, all approximations, all potentially wrong.

Internal Models

The solution in both cases: maintain an internal model of the world, update it based on incoming evidence, and act on that model even knowing it’s incomplete.

In cognitive science, this is predictive processing. Your brain constantly predicts what should happen next, then reconciles predictions with actual sensory input. Consciousness is the error signal.

In distributed systems, we call this eventual consistency. Services maintain local state, propagate changes asynchronously, and reconcile conflicts when they’re detected. The application is the error signal.

Consensus as Cognition

Getting multiple nodes to agree on shared state is the distributed systems version of inter-hemispheric communication. Raft and Paxos are protocols for collective decision-making under uncertainty—just like neural voting mechanisms in perceptual binding.

Both face the same fundamental tradeoffs:

Speed vs Accuracy: Fast consensus means less validation. Slow consensus means better decisions but higher latency. The brain solves this with a hierarchy: fast subcortical reactions, slower cortical deliberation.

Centralization vs Resilience: Single leader systems (Raft) are simple but fragile. Leaderless systems (Cassandra) are robust but complex. Biology uses both: centralized coordination for whole-organism behaviors, distributed autonomy for cellular functions.

Memory and State

In systems design, we debate stateless vs stateful services. In neuroscience, we study working memory vs long-term consolidation. Same questions, different vocabularies:

The hippocampus is literally a write-through cache for the neocortex. Sleep is a batch processing job that consolidates working memory into durable storage. We’ve been reinventing these patterns in every database architecture.

Failure Modes

Systems fail like minds fail:

Network Partition = Sensory Deprivation: Cut off from input, both systems confabulate. They generate internally consistent stories that diverge from external reality.

Cascade Failure = Panic: One service crashes, overwhelming its dependents, which overwhelm theirs. This is computationally identical to anxiety spirals—positive feedback loops in error signaling.

Thundering Herd = Overstimulation: Too many requests at once, and the system locks up trying to serve everyone. Cognitive overload follows the same pattern: attention thrashes, performance craters.

Designing for Bounded Rationality

Herbert Simon coined “bounded rationality” to describe how humans make decisions with limited computational resources. Every distributed system operates under the same constraints:

The solution isn’t to build perfect systems. It’s to build systems that fail gracefully under these constraints, just as cognition evolved to be “good enough” rather than optimal.

What This Means in Practice

When I design systems now, I ask cognitive questions:

What does this service “believe” about the world? Make beliefs explicit. Model them as state. Test what happens when beliefs diverge from reality.

How does it update its beliefs? Define clear learning rules. Specify reconciliation strategies. Know the latency of your feedback loops.

What’s its attention mechanism? You can’t process everything. What gets priority? What gets dropped? What’s your circuit breaker?

How does it explain itself? Observability isn’t just logs and metrics. It’s the ability for a system to articulate its internal state, its decisions, its uncertainties.

The Meta Pattern

The deeper parallel: both brains and distributed systems are solutions to the same problem—coordinating computation across unreliable components to achieve global objectives.

Evolution found one solution through natural selection. We’re finding another through engineering. The convergence is striking:

We’re not building conscious systems. But we are building systems that solve consciousness-class problems: perception, memory, decision-making, and action under uncertainty.

Next time your service is misbehaving, don’t just read the logs. Ask what it believes, why it believes it, and what would change its mind. You might find you’re debugging not just a system, but a tiny, specialized mind.

And that’s the best kind of engineering problem.