Agentics has determined that automatic memorization and retrieval of AI session transcripts provide no measurable performance benefit for software engineering tasks, and may actively degrade model accuracy. The findings, derived from extensive internal testing of its Nori AI coding agent, challenge a prevailing industry assumption that historical conversational data serves as a valuable long-term memory foundation for autonomous agents. Initially, the development team hypothesized that session transcripts would yield critical context, including discarded approaches, user intent, and rationale behind existing code. Despite initially building products around this premise, prolonged experimentation revealed zero performance gains when agents were granted search access to prior conversation logs. When agents already operate with access to structured code artifacts, detailed documentation, and comprehensive pull request metadata, transcript retrieval merely duplicates known information while introducing unstructured noise. This forces language models to consume excessive tokens on irrelevant details, ultimately reducing processing efficiency and output quality. A primary technical limitation identified is the inability of current agent architectures to effectively curate or discard outdated context. Without native state management, models treat all input tokens as valid, causing previously generated or unreviewed code and memory fragments to accumulate as factual ground truth. This phenomenon, described as intent drift, compounds errors over time and exacerbates context window bloat. Furthermore, standard alignment mechanisms do not permit agents to autonomously delete erroneous context without risking unintended modifications to the codebase. To validate these conclusions, Agentics implemented a human-in-the-loop system where weekly summaries of pull requests, communications, and system updates were automatically proposed as agent memory updates. Only twenty percent of these proposals were accepted after manual review, indicating that eighty percent of automated memory additions would have degraded agent performance. The results underscore that autonomous memorization remains unsustainable for production environments, whereas structured, human-verified metadata provides a more reliable foundation for agent reasoning. While session indexing retains utility for team observability and audit trails, the research signals a strategic pivot in AI agent design away from raw conversational history toward engineered, artifact-driven context management. Developers prioritizing autonomous coding assistance are now advised to invest in comprehensive code documentation, structured change logs, and verified memory pipelines rather than relying on unstructured transcript retrieval.