Brand Monitor — Capstone Reference Implementation
What This Is
A reference implementation of Capstone Option 1 (Brand Monitor) from the Agentic Engineering Course.
This is NOT a production system. It's a teaching tool that demonstrates the concepts from Modules 1-7:
- Multi-agent architecture with domain locking
- Security (damage-control rules, L3+)
- Cost estimation and cascade routing
- Mental models and agent expertise
- Observability and evaluation
Structure
brand-monitor/
├── ARCHITECTURE.md # System design document
├── README.md # This file
├── config/
│ ├── brand.yaml # Brand configuration
│ └── damage-control-rules.yaml # Security rules
├── agents/
│ ├── scanner.md # Scanner agent persona
│ ├── analyzer.md # Analyzer agent persona
│ └── reporter.md # Reporter agent persona
├── data/
│ ├── sample_scan.jsonl # Example scan results
│ └── sample_analysis.jsonl # Example analysis results
└── tests/
└── test_pipeline.py # Test suite (run with pytest)
How to Use
# 1. Study the architecture
cat ARCHITECTURE.md
# 2. Review agent definitions
cat agents/scanner.md
cat agents/analyzer.md
cat agents/reporter.md
# 3. Run the test suite
python -m pytest tests/test_pipeline.py -v
# 4. Compare against your own capstone implementation
Key Lessons Demonstrated
| Concept |
Where |
Lesson |
| Multi-agent architecture |
ARCHITECTURE.md |
M4 Orchestration |
| Domain permissions |
Each agent .md file |
M4 Domain Locking |
| Damage control |
config/damage-control-rules.yaml |
M3 Security |
| Cost estimation |
tests/test_pipeline.py (TestCost) |
M6 Economics |
| Cascade routing |
Scanner=Flash, Analyzer=Sonnet, Reporter=Opus |
M6 Cascade Routing |
| Mental models |
Agent system prompts |
M4 Mental Models |
| Evaluation |
tests/ directory |
M6 Evals |
| Security audit |
Config files |
M3 Defense-in-Depth |
Cost Summary
| Agent |
Model |
Cost/Run |
| Scanner |
Gemini Flash |
~$0.0009 |
| Analyzer |
Claude Sonnet |
~$0.021 |
| Reporter |
Claude Opus |
~$0.225 |
| Total |
|
~$0.247 |
| Daily (4 runs) |
|
~$0.99 |
| Monthly (30d) |
|
~$29.64 |
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