AI agents that take safe actions and leave an audit trail
Agents should not "guess." We build tools-first systems that act deterministically, log every step, and escalate to humans when confidence is low.
Four agent types
Agents we build
Each agent is purpose-built for a specific operational domain. They integrate with your Shopify store, act on your behalf, and escalate when uncertain.
Ops Agent
Exception detection and automated remediation
Capabilities
- Monitors stuck orders, fulfillment mismatches, and late shipments in real-time
- Triggers deterministic remediation workflows (retry shipment, escalate to 3PL)
- Routes edge cases to human operators with full context pre-populated
- Creates audit trails for all actions taken and decisions made
Real scenario
When a fulfillment is 48 hours late, the Ops Agent detects the exception, checks the 3PL API, and either re-triggers shipment or creates an urgent task for your ops lead.
Catalog Quality Agent
Data validation and enrichment at scale
Capabilities
- Validates product data against quality rules and completeness thresholds
- Fills missing attributes using contextual inference from similar products
- Normalizes variant names (e.g., "Md" → "Medium", "Blk" → "Black")
- Flags policy violations (incorrect pricing, missing required fields)
Real scenario
When importing 500 products from a spreadsheet, the agent validates each entry, flags 23 items with missing size charts, auto-normalizes color names, and queues flagged items for human review.
Merchandising Agent
Inventory-aware collection and bundle suggestions
Capabilities
- Proposes tags and collections based on inventory levels, margin, and seasonality
- Suggests product bundles using purchase pattern analysis
- Identifies promotion candidates based on slow-moving inventory
- All recommendations require human approval before going live
Real scenario
During a seasonal transition, the agent analyzes inventory data and suggests moving 18 winter coats into a "Last Chance" collection with a proposed 15% discount, pending your approval.
Support Deflection Agent
On-site assistant powered by your store data
Capabilities
- Answers customer questions using store policies and product specifications
- Retrieves order status and shipping information without CRM integration
- Escalates to human support when confidence is below threshold
- Logs all interactions for quality monitoring and training
Real scenario
A customer asks 'What's your return policy for sale items?' The agent retrieves your policy document, answers accurately, and logs the interaction. If asked about a complex warranty claim, it escalates immediately.
Architecture
How our agents work
The tools-first pattern: agents don't call APIs directly. They call deterministic tools that validate inputs, enforce schemas, and log actions.
Agent Request
AI formulates an action based on context
Tool Invocation
Agent calls a defined tool (not API directly)
Validation Layer
Tool validates inputs against schemas
API Call
Validated action sent to Shopify
Model Context Protocol (MCP) Integration
We implement custom MCP servers that connect AI models to your Shopify store. Each tool is explicitly defined with input schemas, validation rules, and permission scopes. The agent never has raw API access—only tool access.
Example tools: get_product_list, update_order_tags, create_draft_order, validate_inventory_level
The “hallucination problem”
Merchants are terrified of AI agents promising inventory that doesn't exist or hallucinating discounts. This is the #1 barrier to AI adoption in B2B commerce.
Agent Request
May contain errors
Governance Middleware
Validates against schema
Shopify API
Only validated actions
Our guarantee
No hallucinated inventory. No phantom discounts. No unvalidated actions.
Every AI action passes through governance middleware that validates against strict schemas before reaching the API. If the agent tries to create an order with invalid data, the middleware intercepts it and forces self-correction.
Schema validation rate
Hallucinated parameters
What makes these real
Safety & governance
Every agent we build includes these five layers of protection.
Evaluation sets (50-200 realistic scenarios)
Every agent is tested against a comprehensive suite of real-world scenarios before deployment. We validate behavior on edge cases, ambiguous inputs, and error conditions.
Tool-based actions (no free-form side effects)
Agents call deterministic tools that enforce schemas and business rules. No direct API access means no hallucinated parameters or unvalidated actions.
Human approval for risky operations
High-impact actions (refunds, inventory adjustments, promotions) require explicit human approval. The agent prepares the request; you make the final decision.
Full action logging + audit history
Every agent action is logged with timestamp, input context, decision rationale, and outcome. Full audit trail for compliance and debugging.
Rollback-friendly architecture
All state changes are tracked and reversible. If an agent makes a mistake, we can roll back to the previous state with full context of what changed.
Understanding the difference
What makes this different from "just a chatbot"
Chatbots generate text. Agents take validated actions. This isn't about conversation—it's about safe automation.
Traditional Chatbot
Conversational, unpredictable
Our Agents
Deterministic, auditable
Action model
Free-form text → unpredictable API calls
Action model
Defined tools → validated actions
Safety
Can hallucinate parameters or invent actions
Safety
Schema-validated, no hallucination possible
Audit trail
Limited logging, hard to debug failures
Audit trail
Full action log with context and reasoning
Approval flow
Acts immediately or not at all
Approval flow
Human approval for risky operations
Error handling
Fails silently or with cryptic messages
Error handling
Explicit error states with escalation
If you want agents, you need clean data and clear policies first.
AI agents are only as good as the data they operate on. Start with our 7-day Readiness Audit to identify gaps before building agents.