obsidian-vault/WORK/Projects/Farmer Portal AI Agent (Planning).md

Project: Farmer Portal AI Agent

Created: 2026-02-25
Source: #work-assistant
Status: Planning

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Overview

Implement an AI agent for the farmer portal that provides detailed data analysis while maintaining strict row-level security isolation per farmer.

Background

Farmers currently access daily delivery reports via a form-driven web interface. They can search their historical data but need a conversational interface for deeper insights. The critical requirement: agent can ONLY access the currently logged-in farmer's data — no cross-farm data access possible.

Current Architecture

• Existing token-based authentication
• Row-level security (RLS) already implemented for web UI
• Farmers "locked" to their data via session tokens
• No accidental data overlap in current system

Goal

Add an AI agent interface that:

• Provides conversational access to farmer data
• Uses existing RLS/session infrastructure
• Maintains same security guarantees as current form-based search
• Cannot be bypassed via prompt engineering or user manipulation

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Technical Approach

Security Model (Layered)

Layer	Implementation	Responsibility
1. Session/Auth	Existing token validation	Verify farmer identity
2. API Gateway	Inject farmer_id from session	Never accept ID from user/agent
3. Database RLS	SQL policies on farmer_id	Hard enforcement at data layer
4. Agent Context	Read-only session injection	Agent sees farmer_id as context, not input

Data Flow

Farmer Login → Session Token (farmer_id: "FARMER_2847")
      ↓
Farmer Asks Agent → Request includes session token
      ↓
API Validates → Extracts farmer_id from session
      ↓
Agent Tool Call → API injects farmer_id, ignores any agent-provided ID
      ↓
Database Query → RLS policy: WHERE farmer_id = session_farmer_id
      ↓
Response → Filtered results returned to agent

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Components

1. Session-Aware API Endpoints

• Extend existing API for agent access
• Endpoint: POST /api/agent/query
• Session token extracted from headers (same as web UI)
• Agent tools call these endpoints, never direct DB access

2. MCP Tool Design (if using MCP)

@mcp.tool()
def query_farmer_deliveries(ctx: Context, date_range: str, commodity: str = None):
    """Query farmer's delivery history."""
    farmer_id = ctx.session["farmer_id"]  # Injected by app, not agent
    return api_client.post("/api/agent/query", {
        "farmer_id": farmer_id,  # Server-side only
        "date_range": date_range,
        "commodity": commodity
    })

3. Agent Context Injection

System prompt includes:

You are helping a farmer view their own delivery data.
Farmer ID: {{farmer_id}} (read-only context)
Access Level: Self-data only

You can:
- Search their historical deliveries
- Summarize trends
- Compare time periods
- Answer questions about their data

You CANNOT:
- Access other farmers' data
- Accept farmer_id from user (it's already set)
- Bypass database filters

4. Database RLS (Existing)

• SQL Server: Row-level security policies
• PostgreSQL: CREATE POLICY per farmer_id
• Middleware: SET LOCAL app.current_user_id = ?

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Key Principles

✅ Do

• Derive farmer_id exclusively from validated session
• Pass farmer_id via secure context/cookies, not parameters
• Use existing RLS policies (no new security model)
• Log all agent queries with farmer_id for audit
• Treat agent as untrusted — all enforcement at API/DB layer

❌ Don't

• Ever accept farmer_id from user/agent input
• Allow agent to construct raw SQL with farmer_id
• Trust prompt instructions to enforce security
• Give agent database credentials

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Tasks

• Design agent API endpoints (extend existing auth)
• Create MCP tools or API client with session injection
• Implement agent context (farmer_id read-only)
• Write system prompt with security boundaries
• Add audit logging for agent queries
• Test with farm boundary verification (attempt cross-farm access)
• Deploy to staging
• Farmer acceptance testing

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Related Projects

• Requisition and Purchase Order System — similar multi-tenant pattern
• Scale projects — may need similar agent integration later

Notes

(Add technical decisions, vendor discussions, security review notes here)

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Created from Discord discussion on AI agent data isolation