Non-Deterministic Systems

• Non-deterministic systems produce different outputs from the same inputs across runs, making traditional testing, debugging, and compliance fundamentally harder than with deterministic software.
• LLMs are non-deterministic even at temperature=0 due to floating-point arithmetic, GPU parallelism, Mixture-of-Experts routing, and batch-level dependencies — no major provider guarantees fully deterministic outputs.
• Non-determinism enables the flexibility and adaptability that make AI agents useful, but introduces risks around reproducibility, auditability, and trust that engineering teams must actively manage.
• Traditional QA breaks down because it assumes input X always produces output Y — testing non-deterministic AI agents requires probabilistic validation, behavioral bounds, and goal-driven evaluation.
• Only 21.1% of LLM-based code generation research papers account for non-determinism in their experiments, exposing a widespread validity gap in how the industry evaluates AI systems.
• Governance, observability, and structured guardrails are essential for deploying non-deterministic systems in production without exposing organizations to uncontrolled risk.

A non-deterministic system is a system that can produce different outputs from the same input under identical conditions, meaning its behavior is not fully prescribed by its specification and cannot be exactly repeated across runs. Non-deterministic software, by its very nature, can produce different outputs for the same input under seemingly identical conditions.

In classical computing, non-determinism has roots in concurrency, distributed systems, and theoretical computer science — as Wikipedia's entry on nondeterministic algorithms documents. But the term has taken on urgent new meaning with the rise of LLM-powered AI agents. Generative AI has sparked a significant shift in the technological landscape by introducing a new non-deterministic element to modern computing.

Think of it this way: a deterministic system is like a vending machine — insert the same coins, press the same button, get the same product every time. A non-deterministic system is more like asking a skilled engineer to solve a problem. They'll produce a valid solution, but it might differ each time in approach, phrasing, or structure. The answer might be equally correct, but it won't be identical.

As Salesforce explains in their guide to agentic AI design, in the world of agentic systems, behavior falls along a spectrum from deterministic AI to non-deterministic AI. Non-deterministic AI behavior equals flexibility — probabilistic, adaptive, and context-aware. The engineering challenge is knowing where on that spectrum your system should live.

Sources of Non-Determinism in AI

Non-determinism in modern AI systems doesn't come from a single source. It emerges from multiple layers of the stack simultaneously.

Token sampling. LLMs predict probability distributions over tokens and sample from them. LLMs predict the probability of a word or token given the context. The randomness typically comes from the sampling methods used during text generation, such as top-k sampling or nucleus sampling. As a result, identical instructions or prompts can yield completely different responses.

Floating-point non-associativity. Even with temperature set to zero, GPU operations introduce variability. Deep learning frameworks often prioritize performance, sometimes at the expense of bit-for-bit reproducibility. Unless explicitly using deterministic algorithms, operations like matrix multiplication, convolution, or reduction can have nondeterministic implementations. Using atomic operations or multi-pass algorithms on GPU can yield slightly different results run-to-run.

Mixture-of-Experts routing. As explored in research covered by Thinking Machines Lab, batch composition affects expert routing. When groups contain tokens from different sequences or inputs, these tokens often compete against each other for available spots in expert buffers. As a consequence, the model is no longer deterministic at the sequence-level, but only at the batch-level. Even if you submit the exact same input multiple times, you may receive different outputs depending on what other inputs are processed in the same batch.

Infrastructure variability. The precision used (FP32, FP16, BF16, etc.) also affects reproducibility. Lower precision has more rounding error and thus more potential variability. If an LLM service switches hardware or uses a mix of hardware, the floating-point behavior might not be identical.

The Temperature=0 Misconception

Many engineers assume setting temperature to zero makes their LLM calls deterministic. It doesn't. OpenAI states that their API can only be "mostly deterministic" irrespective of the value of the temperature parameter. They've added a seed parameter that improves reproducibility but still doesn't guarantee identical outputs due to system updates and load balancing across different hardware. It's a "best effort" kind of situation.

With the shift to LLM-first AI agents, that predictability disappears. Large Language Models introduce non-determinism, meaning the same input can generate an infinite number of responses, many of which could be valid (even with temperature set to 0). This makes AI agents far more natural and capable — but it also breaks the assumptions that traditional testing and evaluation rely on.

Research published on arXiv quantified this directly: they demonstrated an alarming degree of variation across equivalent input runs with a varied collection of high performing LLMs under presumed deterministic settings. Their findings suggest there is far too much uncertainty in a realm where robust engineering is the expectation.

Non-determinism isn't a bug — it's a fundamental property of the systems engineering teams are now building on. The dual nature of non-determinism in generative AI unleashes creativity and adaptability in complex environments while navigating the challenges of unpredictability and consistency.

The scale of adoption makes this urgent. According to Stanford's 2025 AI Index Report, 78% of organizations reported using AI in 2024, up from 55% the year before. That means non-deterministic behavior is now embedded in production systems across most enterprises — whether they've accounted for it or not.

For engineering teams deploying AI agents, non-determinism touches every operational concern:

• Debugging becomes probabilistic. When an LLM produces inconsistent outputs, it becomes nearly impossible to debug problems effectively. If a model generates an incorrect or harmful response, engineers cannot reliably reproduce the issue. This makes it extraordinarily difficult to identify whether the problem stems from the model itself, the prompt engineering, the data, or some other factor. Debugging becomes a game of chance rather than a systematic process of elimination.
• Compliance requires auditability. Beyond debugging, reproducibility is critical for auditing and verification. Regulatory bodies, compliance officers, and security teams need to understand how AI systems make decisions.
• Benchmarks lose meaning. Non-determinism introduces noise into benchmarks, making it difficult to determine whether performance differences are real or artifacts of randomness.

The industry's response? As a Comet.com guide to agentic systems puts it, there's a tension in software engineering between the desire for predictable, deterministic systems and the powerful but unreliable non-determinism of LLM-driven intelligence. That tension defines the engineering challenge of this era.

AI Agents in Production

The core of production challenges stems from the agent's autonomous and non-deterministic nature, which can deviate from original intent in unpredictable ways. This is "agentic drift" — the inevitable divergence between what you designed the agent to do and what it actually does in production.

Consider a code review agent triggered on pull requests. Run it twice on the same diff, and it might flag different issues, suggest different refactors, or phrase feedback differently. The reviews might both be valid — but your CI pipeline needs to produce consistent pass/fail signals.

Testing Non-Deterministic AI

Traditional QA assumes deterministic behavior. Traditional quality assurance assumes deterministic behavior — given input X, you always get output Y. That model breaks completely with AI agents.

As Datagrid's testing framework guide explains, modern AI testing requires five key shifts: embrace probabilistic validation instead of exact output matching, monitor behavior over time rather than single-point verification, measure behavioral bounds instead of deterministic correctness, incorporate human judgment where automated testing reaches its limits, and validate reasoning processes alongside functional outcomes.

Hybrid Deterministic/Non-Deterministic Architectures

The smartest production architectures don't try to eliminate non-determinism — they contain it. You want the best of both worlds — an agent that's helpful and conversational up front, but transitions to predictable processes to finish. A deployment agent might use an LLM to interpret intent (non-deterministic) but execute the actual deployment through a deterministic pipeline with strict guardrails.

Reproducibility Is Not Guaranteed

No major provider currently promises fully deterministic outputs for their generative models. It's generally understood as a limitation of today's LLM technology. Engineers should design systems to tolerate minor output variations rather than expecting bit-perfect determinism.

Testing Requires New Frameworks

Chaos engineering can be applied to testing non-deterministic systems. You can deliberately introduce failures and perturbations into a system to test its resilience and identify potential issues before they occur in production — randomly terminate instances, simulate network latency, or inject errors into data streams. By systematically introducing chaos in a controlled environment, you uncover weaknesses that might not be apparent under normal testing conditions.

Non-Determinism Creates Security Exposure

Self-directed behavior in advanced agents introduces risks of uncontrolled operations, explainability, and auditability, and makes it difficult to maintain predictable security boundaries. These capabilities transform security from a boundary problem to a continuous monitoring and control challenge. An agent that behaves differently on every run is harder to threat-model than a deterministic workflow.

The Validity Gap in Research

To understand how the literature handles the non-determinism threat, researchers collected 76 LLM-based code generation papers that appeared in the last 2 years. Only 21.1% of these papers consider the non-determinism threat in their experiments. These results highlight that there is currently a significant threat to the validity of scientific conclusions. If you're evaluating agents based on published benchmarks, factor in this uncertainty.

Over-Reliance on Deterministic Assumptions

For enterprise, unpredictability is a major drawback. Non-deterministic AI tends to produce varying outcomes for the same input, making it unreliable for sensitive or customer-facing tasks. Teams must explicitly map which parts of their system require deterministic guarantees and which can tolerate variation — then architect accordingly.

Non-deterministic systems demand infrastructure built for variability — observable, governed, and auditable by default. Guild.ai provides the runtime and control plane where AI agents run with full session traceability, permissioned actions, and behavioral monitoring, so teams can deploy non-deterministic agents with the confidence of deterministic oversight. Learn more and join the waitlist at guild.ai.