Stop Vibes, Start Verifying: Deterministic Guardrails for AI Agents

The author critiques LLM-as-a-Judge as an unreliable way to police AI outputs because it stacks probabilistic judgments. They advocate deterministic, code-based verification—assertions, parsing, and real checks—to block errors regardless of model confidence. Steer, an open-source Python library, implements a verification layer and a "Teach" loop to catch and fix failures without redeployments.

Key Points

• LLM-as-a-Judge creates a circular, probabilistic feedback loop that can rubber-stamp hallucinations; you can’t fix probability with more probability.
• Treat agents like software: use assertions, unit tests, and deterministic checks to block unsafe or incorrect actions.
• Replace vibe checks with verifiable code paths: make real HTTP requests, parse SQL ASTs, and query databases for disambiguation.
• Steer provides a verification layer that enforces hard guardrails around agent functions using simple, composable verifiers.
• A built-in "Teach" loop converts caught failures into targeted rules that patch behavior without prompt rewrites or redeploys.

Sentiment

The overall sentiment is largely in agreement with the article's identification of the 'confident idiot' problem and the need for more reliable LLM outputs. However, there is significant skepticism regarding the effectiveness of the proposed 'Teach' loop as a truly deterministic solution, with many viewing it as a continuation of probabilistic approaches. While the call for deterministic safeguards and traditional software engineering principles is generally well-received, the discussion reflects a nuanced understanding of LLM capabilities and limitations, with both strong critiques and defenses of their inherent nature and utility.

In Agreement

• LLMs are fundamentally statistical text predictors, not fact-storing 'world models,' which intrinsically leads to hallucinations and limits their deductive reasoning.
• 'LLM-as-a-Judge' is problematic because it tries to fix probabilistic errors with more probabilistic judgments, often worsening issues like hallucination and sycophancy.
• LLMs frequently exhibit overly confident, verbose, and sycophantic behavior, which is frustrating in human-like interactions and likely stems from training biases (e.g., RLHF, Reddit data, executive preferences).
• Deterministic safeguards, external validation, and 'hard rules' (like assertions and unit tests) are crucial for making LLM outputs reliable and integrating them into production systems, especially for verifiable sub-tasks.
• The problem is architectural, akin to 'in-band signaling' security vulnerabilities, where data and control paths are not adequately separated, leading to systemic unreliability.
• Practical experience with LLMs for real-world work often necessitates a return to traditional software engineering principles: modular code, robust testing, clear interfaces, and continuous, external validation.
• Google's 'AI Overviews' and other LLM-powered search features frequently hallucinate, misrepresent information, and create a poor user experience, serving as prime examples of the 'confident idiot' problem.

Opposed

• The article's proposed 'Teach' loop, which injects rules into the LLM's context, is itself a probabilistic 'vibe check' rather than a true deterministic safeguard, as LLMs can still ignore or misinterpret these rules.
• Determinism is not the root issue; even fully deterministic LLMs (e.g., with `temperature=0`) would still hallucinate consistently, indicating the problem lies deeper in their architecture (e.g., 'instability') rather than randomness.
• LLMs *can* be guided towards more desirable conversational behaviors (e.g., asking clarifying questions, reducing verbosity) through careful prompt engineering, system messages, or specific model features, suggesting the problem is often user- or tuning-related rather than a fundamental inability.
• Code-based validation solutions, like Steer, are only effective for easily verifiable outputs and won't significantly address complex, judgment-based hallucinations (e.g., diagnosing medical conditions or generating creative content).
• Despite their flaws, LLMs are already immensely useful, and excessively focusing on eliminating their probabilistic nature might degrade their unique capabilities or lead to counterproductive over-regulation.
• The premise that LLMs are uniquely non-deterministic or unreliable compared to humans is questionable; humans are also non-deterministic, prone to error, and often operate with probabilistic understanding, especially on abstract subjects.
• There's an irony or perceived hypocrisy in an article criticizing LLM flaws (and the use of probabilistic fixes) potentially being AI-written itself, and then proposing a solution that some see as another probabilistic 'vibe check'.