# kaal:claim:5245185-032

**Claim.** The adaptive learning touted as the strength of centralized AI monitoring is insufficient, because it relies on internal data loops that cannot match the external evolution of AI agents.

**Type.** failure  **Support.** evidenced

**Holds when.**

- centralized feedback and recalibration confined to internal data

**Source quote.**

> The centralized AI adaptive learning touted as a strength is insufficient, as it relies on internal data loops that cannot match AI agents' external evolution.

**From.** Wulf A. Kaal, *How can we Best Monitor AI Agents* (2025), 6.3.4. Adaptive Learning: Centralized Constraints Limit Evolution, page 14

**Cite as.** Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185

**Verify.** sha256 of source PDF `4d7adba83ec722480e97bde6528cbe9ce98c709e45cb18794f157a64b8fe7da2` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20How%20can%20we%20Best%20Monitor%20AI%20Agents.pdf

**Failure mode.** internal-loop-recalibration  (family: ai-oversight-and-alignment-gap)

**Topics.** decentralization, ai-and-agents

**Keywords.** adaptive-learning, internal-loops, ecosystem-inputs, centralized-ai

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.