Importing the whole shared drive is the most common way to start an AI knowledge base, and the most common mistake. When old and current versions are retrieved together, the model blends them: in one study, even with the current information retrieved, adding a single outdated passage cut the overall score by more than 24%. First build a source inventory recording each source's owner, update frequency, access restrictions and handling decision; collect only one workflow's well-governed sources in the first batch; and keep only the current version of each document, labeling any old version you must keep with its version and expiry date. After importing, check the file counts, because some connectors import nothing and report no error when they lack permissions.
Looplane's prompt is now `m3-exact-edit-v4`: the version persists into artifacts; core/tool/interaction/runtime/instructions/skills/workspace/memory are composed as stable or dynamic sections; and positive/negative examples cover replace_text, unified diffs, and direct replies. Unit tests pin the structure, while live-eval coverage still needs expansion.
Flow Runtime is the heart of Agent Platform: a Flow becomes immutable when published, each Run is bound to a specific version and preset, Steps move through a DAG according to edge conditions, every boundary saves a checkpoint, and resume/retry-step preserves the complete trace history.
A Skill is a versioned, installable, and auditable capability package. Its dual-file architecture separates metadata from instructions, explicit binding replaces model-driven routing, and every invocation is recorded. The Learning Loop turns run signals into proposals, sandbox evaluations, human review, and publication while enforcing the principle: agents propose, humans review, and evals serve as the gate.