User stories
User stories captured from human stakeholders. Each story has a GitHub issue (github_issue:) and lists its research docs and proposal via frontmatter. Open any story to see the full Related panel.
- User story: self-auditing rig — automated stuck-issue detection and memory loop As a rig operator I want the rig to detect stuck issues, investigate, file them, capture fixes into memory, and recognise recurrences — replacing manual triage.
- User story: safety foundation — block the unrecoverable before higher-trust tiers Phase-0 runtime guards that sit between agent reasoning and tool execution: dangerous-command blocklist, git worktrees per task, default-deny egress NetworkPolicy, GitHub App tokens with 1h TTL. Prerequisite for autonomy tier promotion past T1.
- User story: nightly quality gate — golden suite as regression blocker Nightly harness runs the rig against a fixed set of 10 internal tasks + weekly SWE-bench Pro subset + per-incident regression cases. Fails the pipeline on >10% metric regression. ~$3–8/night (~$1.1–2.9k/year). Prerequisite for autonomy tier promotion past T1.
- User story: hard cost ceiling — LiteLLM proxy with per-agent virtual keys Layer-3 enforcement that returns 429 at the proxy before the request reaches the LLM provider. No trust-based limiter; no override flag. A compromised or looping agent cannot exceed its budget because the proxy fails the call.
- User story: agent observability — one env var + a trace store Turn on CLAUDE_CODE_ENABLE_TELEMETRY=1 in agent pods and route OTLP spans to a dual-backend stack: Grafana Cloud (startup credit) for infra+trace waterfalls, Langfuse Cloud (startup discount) for LLM-specific UX. Phoenix stays as the dev-inner-loop store. OpenObserve self-host is the documented fallback if Grafana credit is denied.
- User story: two-layer brain for multi-project support Split the brain into an invariant rig brain (how agents work) and per-project brains (what each project is) so the same agents serve multiple product portfolios (Dashecorp iOS today; future tenants later).
- User story: memory-to-docs promotion pipeline Memory accumulates agent experience; brain holds canonical truth. Close the loop: a weekly lint promotes high-value memories into docs PRs so the brain gets smarter over time.
- User story: how should the rig handle docs vs memory (2026-04-18) Parent user story tying together the three research docs (DASHE-11/12/13) for the docs/memory strategy refinement.