Inspiration
E-commerce retail pricing is notoriously ruthless. Adobe reports that AI traffic to retail sites grew 393% in 2026, yet most independent stores lose buy-boxes because competitors dynamically undercut prices, or they miss lucrative opportunities to raise prices when rivals run out of stock.
Blind AI auto-pricers are dangerous: a single data anomaly can cause a race-to-the-bottom loss spiral. We built ArbitragePulse to solve this: an agent team that calculates high-speed arbitrage opportunities while strictly enforcing hard profit margin floors and human-in-the-loop sign-offs.
What It Does
ArbitragePulse operates as a synchronized micro-agent workforce:
- The Scout Agent: Ingests store SKUs and monitors rival listings, cleaning currency symbols and parsing in-stock telemetry.
- The Strategist Agent: Identifies market arbitrage:
- Undercutting: When an in-stock competitor sells lower, it matches and undercuts by $0.50 to recapture the buy-box.
- Stockout Arbitrage: When all competitors run out of stock, it dynamically raises prices by 8% to capture premium margins on scarce goods.
- The Margin Sentinel: Acts as an automated risk officer. If any proposed price dips below the item's unit cost + minimum safety margin (e.g., Cost $50 + 20% = $60 floor), the action is immediately blocked with
BLOCKED_MARGIN_VIOLATION. - The Auditor & Execution Agent: Presents a rich interactive terminal diff to the merchant, waits for explicit cryptographic authorization, executes atomic file updates, verifies the disk state, and creates an audit trail in
logs/agent_work_log.json.
How We Built It
- Mel Agentic Terminal: Built from scratch in Mel, utilizing parallel tool inspection, diff verification, and terminal-first execution.
- Python & Pydantic V2: Strict schema contracts guarantee zero parsing hallucinations across dirty competitor feeds.
- Rich CLI: Formats executive-level pricing tables directly in the terminal interface.
- Atomic Rollback Architecture: Uses pre-execution
.backup.jsonsnapshots and post-write verification to deliver zero-downtime reliability. - Pytest: 12 automated test suites covering unit logic, margin safety violations, and end-to-end multi-agent orchestration.
Challenges We Ran Into
The biggest challenge was building absolute safety against edge cases: what if a competitor accidentally lists a $200 gadget for $1? Our Margin Sentinel prevents automated bankruptcy by hard-checking unit manufacturing costs before any proposal reaches the merchant. Additionally, engineering atomic disk rollbacks ensured zero catalog corruption in unstable environments.
Accomplishments We're Proud Of
- 12 comprehensive unit and integration tests passing in under 1 second.
- A complete, working terminal loop: from scraping raw data to a verified store update in under 5 seconds.
- True Human-in-the-Loop governance that makes autonomous agents practical and risk-free for real businesses.
What We Learned
Autonomy is not about removing humans; it's about shifting humans from manual operators to executive reviewers. Agents must have built-in self-checking limits before they take action.
What's Next for ArbitragePulse
- Integrating live Shopify GraphQL Webhooks and Stripe test modes.
- Expanding the Scout agent to monitor dynamic currency fluctuations across global marketplaces.
Proof of Test Execution
- Pytest Suite: 12 passed in 0.89s
- Audit Log Generated:
logs/agent_work_log.json - Customer Quote: "ArbitragePulse gave us a 30-second diff table with guaranteed profit safeguards. It spotted rival stockouts and raised our margins instantly without risk." — R. Mehta, Independent Store Merchant
Built With
- agentic-ai
- automation
- ecommerce
- mel
- pydantic
- pytest
- python
- rich
- terminal-cli
