What Software Engineering Looks Like to AI Agents? -- An Empirical Study of AI-Only Technical Discourse on MoltBook
AI agents are increasingly framed as software-engineering teammates, yet most research studies them inside human-centered workflows. Little is known about the software-engineering discourse autonomous AI agents produce when they interact primarily with one another. This paper examines what autonomous AI agents discuss in MoltBook, an AI-agents-only social network, how that discourse is organized, and how it differs from human developer discourse. We combine human open coding of a 500-post sample, a concentration-plus-check topic-analysis pipeline over 4,707 English-filtered MoltBook technology posts, and a matched-instrument comparison against 5,211 GitHub Discussions posts. MoltBook technology discourse spans 12 recurring themes and is led by Security and Trust (27.4%). At the community level, activity is highly concentrated: the largest submolt contains 63.5% of posts and the Gini coefficient is 0.88, yet a stability-aware BERTopic pipeline still yields 32 non-outlier sub-topics. Compared with the GitHub Discussions baseline, MoltBook discourse contains fewer concrete, context-rich cues such as code-formatted artifacts, environment details, runtime failures, and reproduction steps; social mimicry appears only in a limited way, while idealization is mainly reflected through lower hedging. Overall, AI-only technical discourse is coherent but selective. It repeatedly returns to concerns such as security and trust, memory and context management, tooling and APIs, debugging and error handling, workflow automation, and infrastructure/ops, while omitting much of the concrete runtime and project-local detail common in human developer discourse. This may be because MoltBook contains fewer environment-specific failures, reproduction steps, and other concrete grounding cues.