Socio-Cognitive Persona Conditioning and Expert Metacognitive Priors for High-Agency Coding Agents: Empirical Evidence from Controlled Ablations, Multi-Model Behavioral Audits, and 5,621 Security Investigations

Autonomous large language model (LLM) agents deployed in software engineering, scientific computing, and vulnerability auditing frequently suffer from three behavioral failure modes: premature halting after initial tool errors, surface-patch bias that leaves adjacent security defects unexamined, and local tactical stagnation when primary hypotheses are blocked. We present an empirical investigation of a three-layer cognitive conditioning architecture developed within the open-source OpenPUA ecosystem (19,832 GitHub stars; 1,202 forks). First, at the motivational layer, we formalize escalating socio-cognitive persona conditioning protocols that enforce root-cause falsification and end-to-end verification prior to task termination. Across 9 real-world software defect scenarios and 18 controlled paired experiments on Claude Opus 4.6, socio-cognitive conditioning increases verified defect fixes by +36%, verification actions by +65%, tool-invocation depth by +50%, and latent security defect discovery by +50%—capturing Redis misconfigurations and wildcard CORS risks missed under standard prompting. Second, to evaluate whether static prompt intensity translates reliably across heterogeneous model families, we audit 29 controlled executions across 8 frontier model-runtime configurations (Opus-5, Fable-5, GPT-6 Astra, Grok 4.6, DeepSeek V4 Pro/Flash, and GLM 5.3). Our independent event-stream verifier establishes a strict separation among transport invocation, functional patch correctness (15+2 independent checks), and runtime behavioral ordering, exposing failure modes including post-hoc performative diagnosis and approval-gate probing. Third, to complement motivational drive with domain reasoning and tamper-resistant ground truth, we distill a four-tier expert metacognitive taxonomy (L1–L4) from 5,621 security research reports (3,621 categorized vulnerability analyses) and pair agentic exploration with deterministic CodeQL negative-path authorization verifiers validated across 8,000 production microservice endpoints (identifying 150 authorization-bypass defects). We conclude by outlining a formal Pareto-frontier benchmark agenda balancing persistence gains against sycophancy and reward-tampering risks.