Teams building agentic systems wrestle with growing technical debt around their “harness” code. Production harnesses must lock down behavior for safety, while training harnesses need flexibility for exploration. Treating one harness as a permanent solution backfires as model capabilities evolve. Clear separation of roles, regular refactoring and an eye on obsolescence can keep AI pipelines maintainable. Elden Ring’s NPCs don’t use fancy behavior trees. Instead, each actor pushes and pops simple “Goals” on a stack—randomized when needed, hierarchical when the situation calls for it. That mix of structure and chance delivers surprisingly rich combat encounters with minimal code. A former Meta L8 engineer took similar simplicity into a solo setup: a voice-driven planner, custom scripts and parallel agent workflows cut down busywork. He swapped endless meetings for clear task lists and let agents handle boilerplate, freeing him to focus on design and strategy. On the tool front, Flint AI provides a CLI for scanning agent-generated code and catching runtime failures; Hunk offers a Git-integrated diff viewer with inline AI comments; and Slack’s new Claude Tag automates task handoffs inside team chats. In security, OpenAI rolled out Codex Security plus GPT-5.5-Cyber for automated vulnerability fixes, while a Mythos benchmark questioned the tool’s hype by comparing it against other models on real bugs. Mistral OCR 4 sets a new standard in document intelligence with bounding-box outputs and confidence scoring. Meanwhile, local-AI fans can run GLM-5.2 via Unsloth Studio’s web UI, tweaking parameters and downloading models for fast inference. On the people side, a Google engineer got fired after his grassroots Workspace CLI went viral—highlighting how corporate caution can clash with bottom-up innovation. Finally, proofs of concept show post-training agents like Claude Fable 5 and Opus 4.8 boosting base models through self-evaluation loops, with Fable 5 leading in trace quality and calibration.