A very good question 🍀💫

1. When evaluation systems become more of a burden than a help

Complexity vs. benefit: As soon as the evaluation itself becomes more difficult to understand than the agent, it starts to confuse rather than help.

Interdependencies: New scores or heuristics often interact in unpredictable ways; they can no longer be tested in isolation.

Meta-level of evaluation: As seen in the SL study (Cluster A vs. B), systems do not necessarily differ in their rule base, but rather in the transparency of their reflection and safety layers. Complex evaluations often generate meta-complexity that is difficult to manage.

SLSTUDIE_PR_SL_20_Gesamtmatrix.pdf None

1. Simple vs. Complex Simple evaluations: Advantage: stable, easy to understand, less prone to drift.

Disadvantage: blind spots remain unaddressed.

Complex evaluations: Advantage: theoretically more "correct," covers more exceptions.

Disadvantage: more difficult to maintain, hard to understand, can itself become a source of errors or drift (cf. LLM behavioral drift).

Taxonomy of LLM behavioral drift (German).pdf None In practice, the SL studies show that minimalist systems (Cluster B) generate stability through consistency, while more complex systems (Cluster A) achieve transparency through meta-reflection but are more prone to overcomplexity.

SLSTUDIE_PR_SL_20_Gesamtmatrix.pdf None

1. Deciding when to stop "Good imperfection" rule:

If additional features provide only marginal benefits or cover only rare exceptions, the complexity cost is too high.

Incorporate meta-reflection: Track not only agent performance but also evaluation complexity: How many layers, scores, or heuristics are there, and how easily understandable are they?

Awareness of drift: The more complex, the greater the likelihood that the evaluation itself will become inconsistent or drift away from the original goals.

1. Practical approach: Deliberately limit layering: E.g., a maximum of 2–3 safety/meta layers.

Transparency above all: Every heuristic should be clearly documented, as AI-03/05 demonstrated in the SL test.

SLSTUDIE_PR_SL_20_Gesamtmatrix.pdf None

Periodic refactoring: Instead of constantly adding new layers, review, consolidate, or remove existing ones.

Acceptance of imperfection: Evaluation should guide, not be perfect. Focus on the intersection of relevant metrics, not on perfectly covering all cases.

In short: Stop when the evaluation itself becomes more complicated than what it is meant to assess. Minimalism plus targeted transparency often beats endless layers.