Opening — Why this matters now

We keep telling ourselves a comforting story: if an AI explanation isn’t good enough, just refine it. Add another round. Add another chart. Add another paragraph. Surely clarity is a monotonic function of effort.

This paper politely demolishes that belief.

As agentic AI systems—LLMs that reason, generate code, analyze results, and then revise themselves—move from demos into decision‑support tools, explanation quality becomes a first‑order risk. Not model accuracy. Not latency. Explanation quality. Especially when the audience is human, busy, and allergic to verbose nonsense.

The authors introduce Agentic XAI, then show something uncomfortable: explanations improve at first, peak early, and then actively degrade. More thinking does not mean better understanding. Sometimes it means the opposite.

Background — From XAI to agentic refinement

Explainable AI (XAI) was supposed to close the trust gap: SHAP values, feature importance, partial dependence plots—tools that show why a model predicts what it predicts. In practice, these outputs are still written in a dialect only data scientists enjoy.

Large language models changed the game by translating technical artifacts into natural language. But most prior work treated LLMs as static translators: explain once, stop.

This paper asks a sharper question: what if the LLM is an agent?

In their definition, agentic XAI means:

  • Start with a standard XAI output (here, SHAP on a Random Forest).
  • Let an LLM interpret it.
  • Ask the LLM to critique its own explanation.
  • Generate new analyses, plots, and statistics via code.
  • Incorporate those results into a refined explanation.
  • Repeat.

In other words, explanation becomes an iterative optimization problem.

Analysis — How Agentic XAI actually works

The framework has three moving parts:

  1. Model + XAI layer A Random Forest predicts rice yield from soil, weather, and management variables. SHAP provides global feature importance and directionality.

  2. Agentic refinement loop A multimodal LLM (Claude Sonnet 4) receives:

    • The SHAP visualization
    • The dataset schema
    • Its own previous explanation

    At each round, the agent:

    • Identifies analytical gaps
    • Generates Python code to compute new statistics or plots
    • Consumes the outputs
    • Rewrites the recommendation

  3. Evaluation layer Eleven refinement rounds (Round 0–10) are blindly evaluated by:

    • 12 human crop scientists
    • 14 LLMs acting as judges

    Using seven metrics: clarity, conciseness, specificity, practicality, contextual relevance, cost consideration, and scientific credibility.

This setup matters: the paper does not assume improvement. It measures it.

Findings — The inverted‑U nobody wanted

The result is remarkably consistent.

Overall quality

Both human experts and LLM evaluators observe the same pattern:

Phase What happens
Early rounds (0–2) Too shallow, under‑explained
Middle rounds (3–4) Peak quality
Later rounds (5–10) Verbose, abstract, less useful

Average recommendation quality improves by ~30–33% from baseline, then collapses.

This is not noise. Generalized Additive Models confirm a statistically robust inverted U‑shape.

Metric‑level breakdown

Not all qualities decay the same way:

Metric Pattern Interpretation
Specificity Inverted U Too little → optimal → diluted
Practicality Inverted U Actionability erodes with abstraction
Context relevance Inverted U Over‑generalization creeps in
Credibility Inverted U Sophistication without grounding
Conciseness Monotonic decline Each round makes it longer
Cost consideration Monotonic increase Even when data is missing

That last row is the most revealing. The model keeps improving cost analysis—despite the dataset containing no economic variables. This is variance masquerading as insight.

Implications — Explanation has its own bias–variance trade‑off

The paper’s key contribution is conceptual:

Explanation complexity obeys the same bias–variance trade‑off as models themselves.

  • Bias side: early explanations oversimplify and omit key relationships.
  • Variance side: late explanations hallucinate structure, drown signal in detail, and drift away from actionable reality.

Agentic XAI makes this trade‑off visible because it pushes refinement past the comfort zone.

Design lessons for real systems

  1. Early stopping is not optional Optimal explanations emerge quickly. Past that point, iteration is damage.

  2. Metrics cannot all be maximized Conciseness and completeness are enemies. Pick your priorities.

  3. Agentic opacity compounds You’re no longer trusting a model—you’re trusting a model, its explanation, the LLM, the generated code, and the synthesis loop.

  4. Observability matters more than polish Archiving intermediate code, plots, and decisions is not academic hygiene—it’s a trust requirement.

Conclusion — Smarter agents know when to stop

Agentic XAI is powerful precisely because it reveals its own limits.

This study shows that explanation quality is not a function of infinite refinement. It peaks early, then degrades—predictably, measurably, and for structural reasons. The real skill in agentic systems is not thinking longer, but knowing when additional thinking stops helping humans.

In a world racing toward autonomous AI workflows, that restraint may be the most human design choice left.

Cognaptus: Automate the Present, Incubate the Future.