Architecture Summary
Repository-local proof surface for agent runtime reliability and AI workflow orchestration, backed by Python service or lab runtime, Container build surface, Local compose environment.
Free tool-call dataset boundary checklist for fine-tune preparation and eval reviews.
Built for LLM evaluation engineers and dataset reviewers. Use the readiness check below, inspect the implementation, and compare public aggregate demand without submitting project data.
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These notes are derived from this repository's checked-in system architecture, not generic product copy.
Repository-local proof surface for agent runtime reliability and AI workflow orchestration, backed by Python service or lab runtime, Container build surface, Local compose environment.
Primary domain: agent runtime reliability and AI workflow orchestration.
Operating model: stateless runtimes, provider adapters, queue-aware execution, telemetry, and controlled secret boundaries
Containerized runtime path suitable for repeatable local, staging, or managed service deployment Stateless agent gateway with provider abstraction, retries, cost controls, and trace capture
identity boundary and least-privilege service access environment separation for local, staging, and managed runtime paths secret storage outside source and deterministic fallback for missing credentials observability hooks for logs, metrics, traces, and audit events rollback path...
anonymous aggregate tool-call dataset-control interest and checklist usage counts
ads allowed only on public dataset-boundary resources; raw examples, prompts, eval outputs, and dashboards are ad-free
Only four coarse fields are accepted after consent: repository, allowlisted event, public surface, and consent-policy version. Raw inputs, URLs, referrers, identities, files, prompts, and sensitive details are rejected.