From a single plain-language message in Slack — across intelligence, governance, and execution — to a merged PR with a full audit trail.
From a single plain-language message in Slack — across intelligence, governance, and execution — to a merged PR with a full audit trail.
Signals queries its knowledge graph — built from GitHub, Jira, and Confluence — and identifies 7 downstream repos that consume the auth service. It flags 3 as high-traffic based on deploy frequency. This context is injected into the work order before a single agent fires.
The request hits Maestro's control surface. Policy says: auth changes require a SAFE-tier pipeline (adversarial spec review + cross-model validation + human approval gate before merge). Maestro routes to SAFE, bills the client org, and dispatches to Shipyard. A Harbor ticket is created automatically.
Shipyard spawns 8 worktree-isolated agents — one per affected repo. Each agent reads the spec, builds the migration, runs tests, and submits a PR. Cross-model validation (Claude + OpenAI o3) reviews each diff. The developer gets 8 PR links in their Slack thread with a cost summary.
Post-deploy, Signals monitors error rates across all 8 repos. If a regression surfaces within 2 hours, it fires a telemetry alert that Maestro can route to an automatic revert. The drift score from spec to final code is logged for future planning accuracy.
Thrasoz is live today across multiple enterprise clients. The platform is in active development with new capabilities shipping weekly. We're selectively onboarding strategic partners who want to move AI from experiment to operating layer.