Cadent-Style Capabilities on Gemini + Vertex AI

This blueprint outlines how to replicate and exceed Cadent-style automation using the existing graph-centric architecture. It emphasizes agentic orchestration, knowledge-first reasoning, closed-loop learning, and operational reliability.

Unified Agent Layer

• Boss (router) agent that assigns work to specialists across media, QA, finance, and support.
• Gemini Enterprise models handle reasoning-heavy steps while Vertex AI endpoints execute deterministic or cost-sensitive tasks.
• Mixture-of-experts (MoE) or multi-level agent (MLA) routing minimizes cost and latency without sacrificing quality.

Knowledge-First Intelligence

• Combine internal and external knowledge into a single graph-backed memory.
• Use GraphRAG for retrieval over the knowledge graph to ground answers and recommendations.
• Store causal links for traceability so decisions can be explained via graph paths and originating signals.

Closed-Loop Automation

• Stream signals → decide → act → capture outcomes → distill nightly.
• Use Vertex Pipelines and AutoML for continuous improvements driven by observed outcomes.
• Apply ADK-style task agents for repeatable automations while keeping humans-in-the-loop for high-impact changes.

Operational Reliability

• Health agents monitor anomalies and trigger auto-remediation playbooks.
• SLO-based alerting targets sub-minute MTTR for critical paths.
• Incorporate cost and performance telemetry into the routing layer to avoid regressions.

Why This Approach Exceeds Cadent

• Deeper intelligence via knowledge graph and causal reasoning rather than workflow-only logic.
• Smarter scaling through MoE/MLA routing to match tasks with the cheapest capable model.
• True autonomy from outcome-based retraining loops.
• Broader coverage across media, RevOps, finance, and support under one orchestration plane.
• Explainable decisions tied directly to graph evidence and live signals.

30-60-90 Day Execution Plan

• 30 days: Deploy boss router, core specialists, GraphRAG, and Vertex endpoints; connect to knowledge graph.
• 60 days: Add closed-loop learning, health agents, and ROAS optimization policies; establish nightly distillation.
• 90 days: Push toward full autonomy, expand to new domains, and benchmark KPI improvements.

Integration Notes

• Keep imports free of try/catch wrappers to match repository guidelines.
• Place screenshots for UI changes using the browser tooling; skip when back-end only.
• Use the make_pr helper after committing changes to keep workflow consistent.