MarketMirrow — Autonomous Agent Architecture Report
Confidential System Design Document • March 2026
MarketMirrow Platform
Nigeria's Real-Time Commodity Price Intelligence Network
MarketMirrow is Nigeria's community-powered commodity price intelligence platform — a real-time, crowd-sourced system enabling farmers, traders, consumers, and businesses to track food and fuel prices across markets nationwide.
This report presents a comprehensive autonomous agent architecture designed to transform MarketMirrow from a workflow-driven application dependent on manual human operations into a semi-autonomous intelligence platform where agents handle data verification, quality assurance, reward distribution, anomaly detection, and market intelligence — allowing humans to focus on governance, strategy, and edge-case decisions.
~70%
Admin workload reduction from Phase 1 agents
5x
Faster price report verification with AI agent
85%+
Operational tasks autonomous by Stage 4
Key Finding: The highest-impact opportunity is the Price Verification Intelligence Agent, which can eliminate the single largest operational bottleneck — the growing manual review queue for incoming price reports. Combined with the Reporter Trust Agent and Payment Automation Agent, Phase 1 deployment alone delivers transformative operational efficiency.
"To democratize access to real-time commodity price intelligence across Nigerian markets — empowering buyers, farmers, traders, and businesses to make informed economic decisions."
| Module | Purpose | Primary Entities |
|---|---|---|
| Price Reporting | Community price data collection from web, mobile, WhatsApp | PriceReport, Commodity, Market, Unit |
| Verification & Trust | Multi-layer quality assurance and reporter credibility | VerificationRecord, PeerValidation, ReporterProfile |
| Market Intelligence | Price aggregation, trend analysis, forecasting | CommoditySnapshot, NationalIndex, Forecast |
| Advertising Platform | Commodity-contextual ad marketplace for sellers | Advertisement, AdvertiserWallet, PointPackage |
| Field Agent Network | Paid field agent management, incentives, and payouts | AgentWallet, AgentPayment, ReporterProfile |
| Alert System | User-configured price alerts and shortage detection | PriceAlert, ShortageAlert, WhatsAppContact |
| Analytics & Exports | Data exports, API access, business intelligence | CommoditySnapshot, APISubscription, APIUsageLog |
| Admin Governance | Multi-tier admin hierarchy with compliance oversight | AdminAuditLog, User (roles) |
| User Type | Role | Portal Access | Key Actions |
|---|---|---|---|
| Super Admin | Platform owner | Admin Portal (full) | Governance, invite admins, escalations |
| Platform Admin | Operations manager | Admin Portal | Verify reports, approve ads, manage payments |
| Advertiser Admin | Ad ops manager | Advertiser Admin Portal | Campaign approvals, analytics, wallet management |
| Field Agents Admin | Reporter network manager | Field Agents Admin Portal | Reporter management, payout approvals |
| Field Agent / Reporter | Crowd-sourced data contributor | Reporter Dashboard | Submit prices, track earnings, leaderboard |
| Public Consumer | Price information seeker | Public Dashboard | View prices, trends, set alerts |
| Advertiser (Business) | Commodity seller/trader | Advertiser Portal | Create campaigns, buy points, track performance |
| API Developer | B2B data consumer | API Developer Portal | Access price data via API, manage subscriptions |
| Entity | Role | Key Relationships |
|---|---|---|
| PriceReport | Core data unit — a single price observation | → Commodity, Market, Location, Unit, ReporterProfile |
| Commodity | Item being priced (rice, fuel, tomato, etc.) | ← PriceReport, Advertisement, CommoditySnapshot |
| Market | Physical market where price was observed | ← PriceReport, Advertisement |
| Location | Geographic hierarchy (state/LGA/city) | ← Market, PriceReport, CommoditySnapshot |
| ReporterProfile | Reporter metadata, trust score, badge | ← PriceReport, AgentWallet, AgentPayment |
| CommoditySnapshot | Aggregated price statistics per period | ← PriceReport, → NationalIndex |
| VerificationRecord | Audit trail for each verification action | ← PriceReport |
| Advertisement | Ad campaign from a commodity seller | → Commodity, Market, AdvertiserWallet |
| AgentWallet | Agent earnings account | ← AgentPayment, → ReporterProfile |
| PriceAlert | User-configured price notification | → Commodity, Location, Market |
| NationalIndex | Composite price index (food basket, etc.) | ← CommoditySnapshot |
| Forecast | AI-generated price prediction | ← CommoditySnapshot, → Commodity |
100% manual queue review
Manual, infrequent updates
100% manual review
Mostly manual with some tracking
Basic flagging only
Backend functions exist but manual triggers
Bot exists, partial automation
ML pipeline exists, minimal orchestration
Workflow 1 — Price Report Lifecycle (Current State)
Workflow 2 — Price Report Lifecycle (With Agents)
Workflow 3 — Ad Campaign Approval (Current)
Workflow 4 — Agent Payment Process (Current)
| Bottleneck | Severity | Impact Area | Root Cause | Agent Opportunity |
|---|---|---|---|---|
| Manual price report verification queue | 🔴 Critical | Data freshness, reporter experience | No automated verification logic | Price Verification Agent |
| Manual ad campaign approval | 🔴 Critical | Advertiser revenue, platform growth | All campaigns require human review | Ad Intelligence Agent |
| Manual trust score updates | 🟠 High | Data quality, fraud vulnerability | No automated recalculation trigger | Reporter Trust Agent |
| Manual reward calculation & payment | 🟠 High | Agent retention, satisfaction | No event-driven payment logic | Payment Automation Agent |
| No proactive price anomaly detection | 🟠 High | User value, public trust | No continuous monitoring loop | Anomaly Alert Agent |
| WhatsApp reports require manual transcription | 🟡 Medium | Agent scalability | No NLP parsing pipeline | WhatsApp Engagement Agent |
| Market coverage gaps not tracked | 🟡 Medium | Data completeness | No geographic monitoring system | Geo-Intelligence Agent |
| Price forecasting not updated | 🟡 Medium | Forecast accuracy decay over time | Manual/irregular pipeline runs | Forecasting Agent |
The following opportunities were identified by analyzing all workflows, human decision points, repetitive administrative tasks, and data intelligence gaps across the MarketMirrow platform.
| Opportunity | Current Problem | Agent Improvement |
|---|---|---|
| Price Report Verification | Admins review each report manually. Queue grows continuously, delaying data availability. | Agent auto-verifies using statistical/spatial analysis. Only edge cases reach humans. |
| Reporter Quality Management | Trust scores stagnate. Badge promotions never happen without manual intervention. | Agent recalculates scores after every report outcome. Badge changes happen in real time. |
| Ad Campaign Review | All campaigns, even simple ones, wait in a queue for days before going live. | Agent auto-approves low-risk campaigns within minutes. Only flagged content needs humans. |
| Price Crisis Detection | Users only get alerts they explicitly configured. Platform misses systemic price crises. | Agent scans all incoming data, detects anomalies proactively, and generates platform-wide alerts. |
| Agent Reward Issuance | Rewards accumulate unprocessed. Agents are demotivated by delayed payments. | Agent automatically issues micro-rewards within seconds of report verification. |
| Market Data Aggregation | Snapshots are irregular. Dashboard shows raw data instead of clean aggregations. | Agent runs daily aggregation, produces structured snapshots, and computes national indices. |
| WhatsApp Submission Parsing | WhatsApp messages require manual data extraction and entry into the system. | Agent parses messages, extracts structured data, confirms with sender, creates report automatically. |
| Price Forecasting | Forecasts exist as a feature but are not regularly updated with fresh data. | Agent runs weekly forecasting cycle using latest snapshot data and AI trend analysis. |
| Fraud Pattern Detection | Fraud is reactive — only caught after damage is done. | Agent monitors cross-entity patterns continuously, generating escalations before fraud materializes. |
| Geographic Coverage | No system tracks which markets are under-reported or which regions need more agents. | Agent maps coverage density weekly and triggers targeted agent recruitment messages. |
Below are full specifications for all 10 proposed agents. Click any agent card to expand its full specification. Agents are ordered by recommended implementation priority.
| Agent Name | Strategic Importance | User Impact | Efficiency Gain | Implementation Ease | Dependency Complexity | Risk Level | Phase |
|---|---|---|---|---|---|---|---|
| Price Verification Intelligence | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | High | Low | Medium | Phase 1 |
| Reporter Trust & Quality | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | High | Low | Low | Phase 1 |
| Ad Campaign Intelligence | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | High | Low | Low | Phase 1 |
| Price Anomaly & Shortage Alert | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | High | Medium | Low | Phase 1 |
| Agent Payment & Reward | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | High | Low | Low | Phase 1 |
| Market Intelligence & Snapshot | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Medium | Medium | Low | Phase 2 |
| WhatsApp Engagement | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Medium | Medium | Medium | Phase 2 |
| Price Forecasting | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | Low | High | Low | Phase 2 |
| Fraud Detection & Compliance | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | Low | High | High | Phase 3 |
| Geo-Intelligence & Coverage | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Low | High | Low | Phase 3 |
Phase 1 — Immediate
Price Verification, Trust Agent, Ad Intelligence, Anomaly Detection, Payment Automation. High impact, low complexity. Implement within 1–4 weeks.
Phase 2 — Expanded Intelligence
Market Snapshot Agent, WhatsApp Engagement, Price Forecasting. Builds on Phase 1 infrastructure. Implement within 1–3 months.
Phase 3 — Advanced Autonomous
Fraud Detection & Compliance, Geo-Intelligence & Coverage. Requires Phase 1+2 data maturity. Implement within 3–6 months.
Agents do not operate in isolation — they share data, inform each other's decisions, and escalate to humans according to a clear hierarchy.
┌─────────────────────────────────────────────────────────┐
│ HUMAN OVERSIGHT LAYER │
│ Super Admin ──── Admin ──── Agents Admin │
└───────────────────────┬─────────────────────────────────┘
│ escalations / edge cases
┌─────────────────────────────────────────────────────────┐
│ COORDINATION LAYER │
│ │
│ [Fraud Agent] ─────────────────→ [Trust Agent] │
│ │ │ │
│ ↓ ↓ │
│ [Payment Agent] ←──── [Verification Agent] ←── Input │
│ │ │ │
│ ↓ ↓ │
│ [Wallet Update] [Snapshot Agent] │
│ │ │
│ ↓ │
│ [Forecasting Agent] │
│ │ │
│ ↓ │
│ [Anomaly Alert Agent] │
│ │ │
│ user notifications │
└─────────────────────────────────────────────────────────┘
│ INPUT LAYER │
│ [WhatsApp Agent] → PriceReport │
│ [Web/Mobile Form] → PriceReport │
│ [Ad Campaign Submitted] → [Ad Intelligence Agent] │
│ [Geo Agent] → identifies coverage gaps → recruits │
└─────────────────────────────────────────────────────────┘| Agent | Produces | Consumed By |
|---|---|---|
| Verification Agent | verification_status, confidence_score, flags | Trust Agent, Payment Agent, Snapshot Agent |
| Trust Agent | trust_score, badge_level, accuracy_rate | Verification Agent (weight), Payment Agent (multiplier) |
| Payment Agent | AgentPayment records, wallet balance | Field Agents Admin portal, Reporter Dashboard |
| Anomaly Alert Agent | ShortageAlert records, PriceAlert triggers | Forecasting Agent, User notifications, Admin dashboard |
| Snapshot Agent | CommoditySnapshot, NationalIndex | Forecasting Agent, Public Dashboard, API consumers |
| Forecasting Agent | Forecast entities | Public PricePrediction page, API, Advertiser portal |
| Ad Intelligence Agent | Campaign status decisions | Advertiser portal, Admin notification |
| WhatsApp Agent | PriceReport entities | Verification Agent (same pipeline as web) |
| Fraud Agent | Risk scores, escalations, AdminAuditLog | Super Admin Governance panel, Trust Agent |
| Geo Agent | Coverage gap reports, recruitment messages | Field Agents Admin, Agent outreach |
Confidence score < 60% on report
Action: Flag for human verification queue
Trust score drops below 20
Action: Flag reporter, pause auto-verification
Ad content policy violation detected
Action: Auto-reject, log reason
Price anomaly exceeds 50% deviation
Action: Create ShortageAlert, notify admin
Fraud pattern score > 80
Action: Create escalation in Governance panel
Payment calculation error / wallet mismatch
Action: Hold payment, create audit log
The following infrastructure must be confirmed or built before deploying each agent phase.
CommoditySnapshot entity populated with historical price baselines
⚠️ Needs data seeding
PriceReport entity has all required fields (confidence_score, flags, standardized_commodity_name)
✅ Already exists
ReporterProfile entity tracks trust_score, accuracy_rate, verified_reports, rejected_reports
✅ Already exists
AgentWallet and AgentPayment entities exist and are tested
✅ Already exists
VerificationRecord entity captures full audit trail
✅ Already exists
Backend functions subscription plan (Builder+) active
🔴 Required for agents
AdminAuditLog entity captures all agent decisions
✅ Already exists
Admin notification channel (email/in-app) for escalations
⚠️ Needs implementation
Unit standardization logic for price comparison
⚠️ Partially via backend function
Reporter onboarding flow captures enough profile data
✅ Exists via AgentOnboarding
| Requirement | Phase 2 | Phase 3 |
|---|---|---|
| 90+ days of CommoditySnapshot history | Required | Required |
| WhatsApp Business API access | Required | Required |
| Geographic coordinates on all Market records | Required | Required |
| MLPipelineRun entity logging active | Required | Required |
| Builder+ subscription plan | Required | Required |
| Fraud pattern baseline data (60+ days) | — | Required |
| AgentHierarchy entity populated | — | Required |
| API rate limiting and monitoring | Recommended | Required |
| Real-time notification system (push/SMS) | Recommended | Required |
Upgrade to a Builder+ subscription plan to unlock backend functions and the Base44 agent framework. This is the single prerequisite that blocks all 10 agents from being deployed. Once active, Phase 1 agents can be implemented within 2–4 weeks with the existing entity structure and data.
MarketMirrow — Autonomous Agent Architecture Report • Generated March 2026
Confidential System Design Document — For Internal Use Only