The pull of mechanistic interpretability
The term mechanistic interpretability evokes physical “mechanisms” or simple clockwork systems, which scientists can analyze step-by-step and thoroughly understand. Yet attempting to apply this reductionist approach to AI might be misguided.
It’s a seductive idea: that if we could just see inside the model or agent framework deeply enough, we’d uncover neat causal chains and reliable gears of thought, and perhaps even be able to fix them so they always yield truth and insight.
But as Dan Hendrycks and Laura Hiscott argue in their recent AI Frontiers piece, this metaphor is fundamentally flawed. Today's AI systems are less like clocks and even computers, and more like weather: nonlinear, emergent, adaptive. They don’t yield simple explanations, not because they are broken, but because they are complex. And as we introduce agents, tools, personas, and skill-based composition into the mix, the system’s inner workings grow even harder to untangle.
As a computational cognitive scientist, I’ve long understood the appeal of these metaphors. We've loved the clock. We’ve loved the computer. Each offered us a way to explain ourselves and our minds in an age of growing complexity. But maybe, with AI, we’ve finally built a system whose metaphor matches us best: not because we can granularize it into smaller and smaller agents to understand each part, but because we can't.
What complex systems demand instead
In most complex systems, understanding emerges not from analyzing the parts, but from observing the behavior of the system as a whole.
-Melanie Mitchell, Complexity 2019
Complex systems defy mechanistic analysis in ways that are both frustrating and revealing. They resist explanation not because they are flawed, but because they operate on different principles:
- Emergence: The system exhibits behavior beyond the behavior of individual parts. In agentic frameworks, we often see pipelined agents deviate from their procedure instructions. This is a feature, not a bug: unlike traditional RPA--where every possible path had to be encoded--these agents improvise. And are therefore difficult to predict.
- Unpredictability: Agentic systems encounter new material constantly. Sometimes their past experiences creates new output paths we can't anticipate. Saying an agent "behaved incorrectly" and looking for root causes often oversimplifies a nonlinear interaction.
- Layered interactions: As teams stack agents, tools, skills, and personas, the system's internal route becomes less relevant to its outcome. Did the agent pull the wrong document but still produce a correct answer? In complex systems, it's the result, not the exact path, that often matters most.
Many organizations want to apply the familiar development tooling they've used for deterministic systems to these agentic frameworks. But the closer parallel often lies in how they already evaluate their human teams: through observed outcomes, task suitability, and reliability under pressure.
And that's exactly what we do at Reins AI.
We don't evaluate agents like machines, or clocks, or even computer algorithms. We evaluate them like systems embedded in human workflows: measuring observable behavior, uncertainty, and adaptability where it actually matters.
What Reins AI evaluations actually do
At Reins AI, we don’t try to decode neurons. We help teams decide what to do next.
In complex systems, the value doesn’t come from internal inspection. It comes from observing behavior in context. We identify patterns, evaluate behavior, and enable targeted adaptation. Our focus is on helping teams make real decisions about performance, risk, and improvement.
We structure our evaluations to support teams wherever they are:
In all three cases, we focus on measuring the behaviors that matter:
- Suitability: Is the system useful in the way your users need it to be?
- Quality: Is it doing what it's supposed to do, and how often is it failing?
- Uncertainty: Are there hidden risks or gray zones we need to triage?
A simple triage loop
We don’t treat every change as a crisis.
At Reins AI, we use statistical controls to help teams distinguish between natural variation and meaningful shifts, ensuring you only act when it matters and adapt when it counts.
Here’s how our approach works in practice:
- Observe: We monitor the system’s real behavior using structured quality and suitability signals
- Detect: When an issue occurs, we determine whether it’s a known failure, a novel edge case, or a signal of drift
- Triage: We help classify the issue: can it be fixed with prompts? Memory? Workflow redesign? Should it be accepted?
- Adapt: We feed that back into system design: not just as a patch, but as a guide for intentional improvement
This loop turns evaluation from a report into a lever for system evolution.
You don't need interpretability
You need direction. Want to evaluate your own system like this? Reach out.
