Objectives

  • Automate recurring reviews and experiments that leverage the full AI coaching dataset (multi-AI-coach debates, weekly digests, cohort insights).
  • Strengthen observability, cost management, compliance, and deployment pipelines for production readiness.
  • Explore optional pathways for human coach collaboration, marketplace features, and monetization readiness without compromising the AI-first model.
  • Finalize documentation, runbooks, and governance so the system can scale beyond proof of concept.

Functional Scope

Automation & Advanced Coaching Experiments

  • Implement scheduled digests that summarize progress across wealth areas and deliver actionable next steps to students and the AI oversight team (with optional human mentors).
  • Launch multi-AI-coach debate mode where personas provide independent takes before an ensemble agent synthesizes the final advice.
  • Introduce experimentation framework to test prompt variations, task recommendations, and nudge cadences with statistical rigor.
  • If human oversight is introduced, pilot collaboration workflows: assign vetted human coaches to students, provide shared session notes, and manage permissions.

Operations & Compliance

  • Build full observability stack: centralized logging, distributed tracing, metrics dashboards, and alert routing.
  • Implement cost monitoring for AI usage (per user, per session) with automated caps and downgrade paths to cheaper models when thresholds exceed budgets.
  • Conduct security review: penetration testing, secrets rotation, dependency scanning, and incident response drills.
  • Formalize data retention and deletion policies, including legal holds and export tooling for users.

Deployment & Release Management

  • Create CI/CD pipelines with automated testing, linting, preview deployments, and phase-specific feature flags.
  • Support blue/green or canary releases for high-risk features (e.g., new AI prompts, automation workflows).
  • Document rollback procedures, migration strategies, and post-deploy verification checklists.
  • Establish sandbox environments for experimentation agents separate from production traffic.

Documentation & Enablement

  • Produce comprehensive runbooks for each agent/service, including escalation paths and contact points.
  • Update onboarding materials for new developers and optional human mentors, covering architecture, data schemas, and operational policies.
  • Catalog APIs (REST/GraphQL) and MCP tool contracts with versioning and changelog management.
  • Define governance committee cadence to review metrics, approve experiments, and prioritize backlog.

Technical Considerations

  • Adopt infrastructure-as-code (Terraform, Pulumi) to manage databases, secrets, and observability tooling.
  • Evaluate data warehouse options (BigQuery, Snowflake, Supabase) for long-term analytics and ML experimentation.
  • Ensure privacy compliance (GDPR/CCPA): consent tracking, data residency considerations, and DSR workflows.
  • Build guardrails for any optional human coach involvement, including audit trails, session access controls, and compensation tracking.

Multi-Agent Workstream

| Agent | Responsibilities | Deliverables | | --- | --- | --- | | DevOps Steward | Lead observability, deployment automation, and incident management. | CI/CD pipelines, monitoring stack, incident playbooks. | | Experimentation Lead | Design and execute multi-AI-coach debates, AB tests, and weekly digest automations. | Experiment scripts, evaluation reports. | | Compliance Officer | Oversee privacy, security, and policy documentation. | Compliance checklist, audit artifacts, retention policy. | | Marketplace Architect | Explore optional human coach collaboration workflows and monetization hooks. | Permission model, payment integration plan. | | Knowledge Curator | Maintain ongoing content ingestion, retraining schedules, and knowledge governance. | Content roadmap, retraining logs. | | Analytics Lead | Extend cost and performance dashboards, integrate with finance tooling. | Cost monitoring dashboards, budget alerts. |

Exit Criteria

  • Weekly digests and multi-AI-coach debate workflows run automatically with monitoring and rollback plans.
  • Observability stack covers logs, metrics, traces, and cost dashboards with on-call rotation defined.
  • Security and compliance reviews completed with documented remediation plans and recurring audits scheduled.
  • CI/CD pipeline supports safe releases with automated verification and rollback paths tested.
  • Documentation hub published covering architecture, data schemas, agent contracts, runbooks, and governance policies.

Risks & Mitigations

| Risk | Mitigation | | --- | --- | | Automation drift causing unexpected behavior. | Implement feature flags, staged rollouts, and automated tests simulating agent workflows. | | AI cost overruns despite monitoring. | Enforce budget limits, auto-switch to cheaper models, cache frequent prompts, and negotiate vendor discounts. | | Optional human coach marketplace introduces liability. | Require background checks, clear terms of service, and insurance coverage before launch. | | Operational complexity overwhelms team. | Invest in training, modular runbooks, and tool consolidation; evaluate managed services when appropriate. |

Dependencies & Notes

  • Requires maturity from Phases 1–4 with stable data flows and analytics foundation.
  • Coordinate with finance/legal for monetization, compliance, and vendor contracts.
  • Engage user research to validate appetite for optional human coach collaboration before full build-out.
  • Maintain backlog of stretch experiments for future phases (e.g., mobile app, wearables integration).