Cadent-Style Gemini + Vertex AI Execution Blueprint

This playbook maps Cadent-style campaign operations to the existing boss-agent and graph-centric architecture while targeting higher ROAS, faster resolution, and lower MTTR.

Capability Mapping

Unified Agent Layer: Boss Agent router (services/boss_agent and orchestrator profiles) delegates to specialist agents for media, QA, finance, and support. Gemini models handle reasoning, while Vertex AI endpoints execute actions and tool calls.
Knowledge-First Intelligence: Internal + external knowledge graphs back GraphRAG retrieval with causal links for traceable decisions and explanations.
Closed-Loop Automation: Signals → decisions → actions → outcomes feed nightly distillation and AutoML/Vertex Pipelines for continuous improvement.
Operational Reliability: Real-time health agents monitor anomalies, trigger SLO-driven alerts, and auto-remediate to keep MTTR sub-minute.

Why This Stack Outperforms Cadent

Deeper intelligence via knowledge graph + causal reasoning rather than workflow-only branching.
Smarter scaling through MoE/MLA routing to cut cost/latency while preserving accuracy.
True autonomy with outcome-based retraining loops baked into the pipelines.
Broader operational scope (Media, RevOps, Finance, Support) under one orchestrated brain.
Explainability with decisions grounded in graph paths, signals, and retrieved context.

30-60-90 Day Plan

First 30 Days

Deploy Boss Agent router with core specialist agents and enable Gemini reasoning across Vertex endpoints.
Stand up GraphRAG over the internal + external knowledge graph to anchor retrieval and explanations.
Publish critical tool endpoints on Vertex AI (media ops, finance adjustments, support playbooks) with monitoring hooks.

60 Days

Add closed-loop learning: capture outcomes → retrain with Vertex Pipelines/AutoML → redeploy updated policies nightly.
Introduce health agents for anomaly detection and SLO-based alerting; wire auto-remediation playbooks.
Launch ROAS optimization experiments leveraging MoE routing to balance precision vs. cost.

90 Days

Expand autonomy to cross-domain workflows (media ↔ RevOps ↔ finance ↔ support) with governed guardrails.
Harden reliability with chaos tests, rollback playbooks, and traffic-shift policies per service profile.
Standardize KPI reporting that traces decisions to graph evidence and causal links for auditability.

Drop-In Operational Loop

Observe: Stream signals (campaign, spend, support, finance, SLOs) into the graph-backed context store.
Reason: Boss Agent selects Gemini/Vertex tools via MoE routing; GraphRAG provides grounded context with causal chains.
Act: Execute tool calls on Vertex endpoints; log actions + evidence paths.
Learn: Nightly distillation retrains policies via Vertex Pipelines/AutoML using outcome labels and anomaly feedback.
Explain: Persist decision traces (graph paths + signals) for auditors, operators, and customers.

Production-Grade Ads Decision Plugin (Drop-In)

What It Does

Ingests channel logs (Meta, Google, TikTok, etc.), normalizes to unified events, builds graph-aware features, runs uplift/ROI models, and emits budget/creative/bid recommendations with full telemetry and SLOs.

Modules and I/O Boundaries

ingest: Raw channel logs → unified Event records; parse, normalize, de-dupe, reconcile costs, handle late arrivals.
features: Event → model-ready feature rows; join to the Knowledge Graph (account, creative, audience), compute rolling CTR/CVR/CPA, latency windows, and edge features (user↔creative, geo↔campaign).
models: Feature rows → ROI/uplift scores; train/serve, calibrate, and export inference endpoints + batch runners.
decider: Model scores + constraints → Recommendation actions; budget reallocation, bid/ROAS targets, creative rotation with spend caps, guardrails, and SLO adherence.
telemetry: Spans/metrics/audits → SLO dashboards + audit log; emits OpenTelemetry spans and lineage for each decision.

Data Contracts (Proto/JSON)

Event: ts, user_id?, campaign_id, creative_id, cost, clicks, conv, geo.
Recommendation: ts, scope (campaign|creative|account), action_type (budget_shift|pause|raise_bid), target_id, confidence, rationale.
Config (plugin.yaml): Channels, SLOs, guardrails, spend caps, canary thresholds.

Repo Skeleton (Monorepo-Friendly)

/plugins/relu-roi/
  ingest/
  features/
  models/
  decider/
  telemetry/
/schemas/
  event.proto
  reco.proto
/examples/
  bq/           # sample BigQuery SQL for ingest/features
  py/           # minimal Python runners & notebooks

CI/CD (Per PR)

Lint + typecheck (e.g., ruff + mypy or tsc).
Unit tests: synthetic logs → assert feature/score shapes.
Contract tests: golden JSON for Event/Recommendation payloads.
Canary replay (last 24h prod logs): verify decision p95 latency < 2s and budget drift < 0.5% vs policy.
Observability: emit OpenTelemetry under boss.plugin.relu with feature lineage and decider rationale.

Quick Start Integration

Drop the skeleton under /plugins/relu-roi/ and /schemas/.
Wire ingest to channel exports (BQ tables or S3/GCS) → output JSONL Event.
Point features at the KG service (REST/Graph DB) for joins; cache hot keys.
Serve models via gRPC/HTTP endpoints; provide batch runner for nightly backfills.
Run decider on a 5-minute tick or streaming; write Recommendation to a topic/table.
Enable canary: mirror decisions for 24h replay; gate rollout by SLO + budget drift checks.
Ship OTel to Grafana/Datadog/Cloud Trace with the boss.plugin.relu namespace.

Why These Boundaries Work

Clear contracts keep components swappable (e.g., replace models without touching the decider).
SLO-first design enforces safety (latency, budget drift, guardrails).
Golden contract tests prevent schema drift; replayable canaries make “safe-to-try” default.

Success Metrics

MTTR < 1 minute for critical incidents with automated remediation coverage.
ROAS uplift tracked per campaign with guardrails on spend efficiency and quality.
Latency and cost reductions from MoE routing vs. monolithic prompting.
Audit completeness: % of decisions with linked graph evidence and causal explanations.