sage.studio designs and delivers a PR intelligence workflow on AWS Bedrock — VPC-isolated, with CloudWatch audit trails satisfying compliance requirements. A sequencer orchestrates three agents: code quality, security analysis, and standards compliance. All review rubrics live in a versioned prompt layer — knowledge compounds with each review cycle without model retraining. RBAC controls which teams access which agents. Delivered with full documentation and a handover runbook for the internal platform team.
sage.studio builds a custom incident triage workflow directly on the Anthropic Claude API. When an alert fires, a context-gathering agent pulls logs, metrics, and recent deployment history. A diagnosis agent — using a carefully engineered chain-of-thought prompt — surfaces probable root causes ranked by confidence. A runbook agent retrieves the most relevant resolution steps from a versioned prompt library the team controls. The on-call engineer receives a structured brief within two minutes. All prompts are versioned and owned by the engineering team — not locked inside a vendor’s platform.
A sage.resident joins the team. They attend the standups, pick up the sprint tickets, and start building. The first workflows go into production. The team’s own engineers pair on every build — so they understand what was built and why. When architectural decisions arise, the SDLCAgents.ai leadership team is available as a sounding board. By the end of the engagement the team has running workflows, an engineer who has upskilled alongside the resident, and no vendor dependency to maintain.
A sage.resident picks up where studio left off. They know the codebase because they have the handover documentation. They add the incident triage workflow in the first sprint, extend the prompt layer, and wire in the new integrations. The startup’s engineers are involved in every build — not as observers, but as co-builders. When the resident finishes, the team has three additional workflows in production and the confidence to keep extending without outside help.
You know the outcome. You know the stack. You need someone to scope it, build it, and hand it over production-ready — on a fixed scope and a clear timeline.
You have a roadmap and a stack. You need an AI engineer embedded in the team — building sprint by sprint, with architect-level oversight behind them.
Scoped and priced per engagement. Milestone-based delivery. No retainer required after handover. You own everything that gets built.
Monthly engagement. Minimum three months. Includes architect and leadership oversight from the SDLCAgents.ai team. Can follow a studio engagement or stand alone.