1. Observation: AI Feeding on AI
Across the industry, a familiar pattern is emerging. Systems built with AI components increasingly rely on other AI systems to supervise, evaluate, optimize, or explain them. Agents review the output of other agents. Prompt optimizers correct prompts generated by earlier prompts. Observability platforms attempt to monitor probabilistic behavior with more probabilistic analysis layered on top.
This pattern is often framed as progress. As systems become more complex, the story goes, they naturally require more sophisticated tooling to manage them. AI supervising AI is presented as the inevitable next stage of maturity.
But the tell is not the presence of multiple AI components. It is the direction of control flow.
When a system’s correctness, safety, or reliability depends on downstream inference correcting or interpreting upstream inference, something has already shifted. Control is no longer exercised through structure and constraint. It is deferred to interpretation after the fact.
What looks like sophistication is often compensation.
2. Constraint Erosion
In established engineering practice, constraints exist to preserve legibility and control. They are not arbitrary rules; they are mechanisms for keeping systems understandable by the people responsible for them. Limits on pull request size, review standards, explicit invariants, and named ownership all serve the same purpose: they bound complexity so judgment can still be exercised.
AI-generated output changes the economics of producing artifacts, but it does not change the role constraints play. What it changes is the pressure to suspend them.
Large changes are accepted because “the model wrote it.” Reviews are abbreviated because “we can always regenerate,” assuming the next generation will somehow inherit constraints never imposed on the first. Intermediate artifacts are discarded because “the agent handles it.” What would be treated as recklessness if done by a human is reframed as efficiency when mediated by automation.
This erosion is rarely explicit. Teams do not decide to abandon constraints; they quietly stop enforcing them. The rationale is usually pragmatic—speed, experimentation, competitive pressure—but the effect is structural. Once constraints are relaxed in systems that depend on inference, they are difficult to reassert, because the system adapts to higher throughput by replacing judgment with approximation.
The critical shift is that constraints move from being preconditions to being afterthoughts. Instead of bounding what the system is allowed to do, teams attempt to evaluate outcomes after the fact. Review becomes sampling. Accountability becomes statistical. Correctness shifts from something established through intent and constraint to something inferred from aggregate behavior across opaque systems.
Over time, this produces a system where output continues to increase while confidence quietly degrades. Engineers may still feel productive, but their ability to say why the system behaves as it does weakens. Constraint erosion does not fail loudly. It succeeds by normalizing the loss of knowability before its consequences are visible.
3. Compensation Disguised as Discipline
When quality begins to degrade, the response is rarely to reimpose constraints. Instead, organizations compensate.
AI reviewers are introduced to review AI-generated code. Evaluation systems are layered on top of prompt pipelines. Observability platforms attempt to monitor agent behavior. Optimization agents are tasked with tuning other agents. Each addition is justified as discipline, rigor, or maturity.
But what is being compensated for is not lack of intelligence. It is absence of constraint.
Invariant-based constraints do not merely limit behavior; they define impossibility. By collapsing a broad solution space into a provably valid one, they make correctness knowable. When those invariants are missing, organizations attempt to detect problems they could have made structurally impossible.
This creates the appearance of control without its substance. Systems become increasingly elaborate, while responsibility diffuses across layers of automation. Failures are delayed, not prevented. Confidence is managed, not earned.
The system grows more complex, but less governable.
4. Structural Instability
This instability is not incidental—it is inherent.
Inference systems work by collapsing many possible states into fewer inferred ones. This is not a limitation of current models or tooling; it is how inference operates by design. If a system did not discard information, it would not be performing inference at all.
When inference is used to mediate control flow, information is irreversibly lost at each step. Context is removed, ambiguity is resolved probabilistically, and alternative states are discarded. No downstream layer can recover what was never preserved.
This means that stacking inference on inference does not merely add uncertainty. It multiplies it. Epistemic decay is non-linear. Each additional layer amplifies the loss introduced by the previous one.
This is not a matter of model quality or sophistication—it is inherent to inference itself. More capable models do not change the underlying constraint. They only collapse state more efficiently.
The result is a system that may function, but cannot be fully known. Failures do not arise because the system is broken, but because it violates a basic constraint on knowability.
5. Observability vs Post-Hoc Rationalization
Classical observability works because deterministic systems lose information in predictable ways as they execute. Logs, metrics, and traces are projections of real system state. They may be incomplete or noisy, but they are not inferred. When something goes wrong, observability allows engineers to reconstruct causality, identify failure modes, and intervene based on what can still be known about the system.
Systems whose control flow depends on inference break this model. By the time observability is applied, critical information has already been discarded by design. Each inference step collapses possible system states into inferred ones, removing context that cannot be preserved or recovered later. What remains is not execution history, but a sequence of decisions detached from the conditions that produced them.
AI observability platforms attempt to reuse the tools and language of classical observability in this new context. They track prompts, scores, confidence levels, agent actions, and summaries of behavior. But these artifacts are not projections of state in the same sense. They are inferred reconstructions layered on top of missing information, often derived from logged inputs and outputs that cannot restore replayability or causal traceability.
The result is not observation, but rationalization.
These systems explain what happened only after it has happened, using narratives constructed from partial signals. They offer coherence where causality no longer exists. The dashboards may look familiar, but the epistemic ground has shifted: engineers are no longer inspecting a system’s behavior; they are interpreting stories about it.
This distinction matters because observability implies control. In deterministic systems, visibility enables intervention. In inference-mediated systems, visibility often arrives too late and with too little fidelity to change outcomes meaningfully. The system can be monitored, but not governed.
Crucially, this is not a failure of tooling maturity. No amount of additional instrumentation can recover information that was never preserved. When observability is applied to systems whose behavior emerges from stacked inference, it functions as reassurance rather than control. It reduces anxiety, not uncertainty.
This is why observability expands in parallel with instability in inference-mediated systems. As knowability erodes, explanation layers multiply. But each new layer reinforces the same inversion: instead of designing systems that remain knowable, organizations invest in mechanisms that make unknowability feel managed.
What remains is not observability, but rationalization. Control has already been lost.