Agent-managed paid acquisition system
This workspace demonstrates a paid-growth operating loop where experiments, economics checks, and budget movement are explicitly linked for operator review.
Iteration cadence target for budget and creative updates.
Target CAC/LTV ratio threshold for sustained scaling.
Budget-shift actions recorded for operator auditability.
Live acquisition flow
Acquisition data readiness
Active source
Sample
Acquisition sample data
Sample data · 5 source rows
Provider snapshots
0 live · 0 fallback
Latest: None
Campaigns
Test cells
Budget actions
Audit logs
Health endpoint: /api/acquisition/health/demo-db
Workspace data operations
Reset workspace data
Clears acquisition workspace records, then reseeds representative acquisition baseline data.
Revenue proof foundation
warning
Acquisition launch baseline
12 treatment · 2 control · low confidence
Expected baseline
Observed revenue
Incremental revenue
Incremental profit
Baseline conversion
Observed conversion
Evidence chain
Agent actions
- 1 budget actions · applied
- 4 test cells · approved
Exports
- Agent audit export · audit
- Acquisition audit · actions
Confidence flags
- Sample data keeps this proof directional until a provider snapshot is active.
- Confidence is low; keep proof directional until reviewed.
Architecture infographic
Acquisition flow diagram
High-level architecture for agent-managed paid acquisition.
Architecture modules
Campaign manager
Stores campaign objective, constraints, channels, and state transitions (draft → testing → scaling).
Creative generation
Produces headline/description variants and predictive quality signals for faster test-cell construction.
Audience + keyword selector
Builds target pools, exclusions, and testable combinations for channel-specific execution.
Agent orchestrator
Runs iteration loops: score cells, pause weak performers, shift budget, and request new variants.
Performance analytics
Aggregates spend, conversions, CAC, and ROAS to inform budget decisions and operator review.
Audit + controls
Logs budget actions and decision context so humans can override and tune safely.
Operator decision canvas
A shared frame for how inputs become governed actions and measurable learning.
Inputs
- Market/entity signal stream
- User or audience intent graph
- Budget + policy constraints
Decisions
- Priority scoring and ranking
- Intervention/playbook selection
- Budget and channel allocation
Actions
- Message/creative generation
- Campaign launch + pacing
- Override and approval checkpoints
Learning
- Outcome and efficiency metrics
- Counterfactual comparison
- Next-iteration policy updates
Operating sequence
1) Define campaign objective, budget, channels, and economic constraints.
2) Generate creatives and audience/keyword candidates.
3) Assemble test cells and allocate initial spend.
4) Ingest performance and compute score quality.
5) Reallocate budget toward winners while enforcing guardrails.
6) Promote winning cells to scaling and continue monitoring.