AI Agents in Commerce: Building Autonomous Systems That Think, Decide, and Act

AI Agents in Commerce: The Autonomous Revolution

By Tanqory Engineering Team

2025 marks a fundamental shift in how AI is deployed in commerce. We're moving beyond chatbots that answer questions to AI agents that autonomously execute complex, multi-step tasks. These agents don't just respond—they think, plan, decide, and act.

This article details how Tanqory built production-grade AI agent systems that are already handling millions of autonomous operations daily.

1. What Are AI Agents?

Beyond Chatbots

Traditional AI (Reactive):

User Question → AI Response → Done

AI Agents (Autonomous):

Goal → Plan → Execute Step 1 → Observe → Adjust → Execute Step 2 → ... → Goal Achieved

Key Capabilities

Capability	Description	Example
Reasoning	Break complex goals into steps	"Increase sales 10%" → analyze data, identify opportunities, create campaigns
Tool Use	Interact with external systems	Query databases, call APIs, send emails
Memory	Remember context across sessions	Recall customer preferences, past interactions
Self-Correction	Detect and fix errors	Retry failed operations, adjust approach
Collaboration	Work with other agents	Specialist agents coordinating on complex tasks

The Agent Architecture

┌───────────────────────────────────────────────────────────────┐
│                         Agent Core                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │   Planner   │  │  Executor   │  │  Evaluator  │            │
│  │  (Reasoning)│  │ (Tool Use)  │  │ (Feedback)  │            │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘            │
│         │                │                │                   │
│         └────────────────┼────────────────┘                   │
│                          │                                    │
│  ┌───────────────────────▼───────────────────────┐            │
│  │              Working Memory                   │            │
│  │    Current goal    Execution state          │            │
│  │    Context         Error history            │            │
│  └───────────────────────────────────────────────┘            │
└───────────────────────────────────────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          │               │               │
          ▼               ▼               ▼
    ┌──────────┐   ┌──────────┐   ┌──────────┐
    │ Database │   │   APIs   │   │ Services │
    │  Tools   │   │  Tools   │   │  Tools   │
    └──────────┘   └──────────┘   └──────────┘

2. Production AI Agents at Tanqory

Agent 1: Autonomous Customer Support

What It Does:

• Handles 85% of customer inquiries without human intervention
• Processes refunds, tracks orders, modifies subscriptions
• Escalates complex issues with full context to human agents

Architecture:

Customer Message
       │
       ▼
┌──────────────┐
│ Intent       │──► Classify: Support / Sales / Complaint / Other
│ Detection    │
└──────┬───────┘
       │
       ▼
┌──────────────┐
│ Context      │──► Fetch: Order history, previous tickets, preferences
│ Assembly     │
└──────┬───────┘
       │
       ▼
┌──────────────┐
│ Action       │──► Plan: Steps needed to resolve
│ Planning     │
└──────┬───────┘
       │
       ▼
┌──────────────┐
│ Execution    │──► Execute: API calls, database updates, notifications
│ Engine       │
└──────┬───────┘
       │
       ▼
┌──────────────┐
│ Response     │──► Generate: Human-like response with actions taken
│ Generation   │
└──────────────┘

Real Results:

Metric	Before Agents	After Agents	Change
Resolution Time	4.2 hours	3.5 minutes	-98%
First Contact Resolution	45%	89%	+44pp
Customer Satisfaction	3.8/5	4.6/5	+21%
Cost per Ticket	$12.50	$0.85	-93%
Human Agent Load	100%	15%	-85%

Agent 2: Inventory Optimization Agent

What It Does:

• Monitors stock levels across 50,000+ SKUs in real-time
• Predicts demand using multiple signals (trends, seasonality, events)
• Automatically generates and executes purchase orders
• Coordinates with suppliers via automated communications

Decision Framework:

# Simplified agent decision logic
class InventoryAgent:
    def optimize(self, sku):
        # 1. Analyze current state
        current_stock = self.get_stock_level(sku)
        sales_velocity = self.calculate_velocity(sku, days=30)
        lead_time = self.get_supplier_lead_time(sku)

        # 2. Predict future demand
        demand_forecast = self.ml_model.predict(
            sku=sku,
            horizon_days=lead_time + safety_buffer,
            factors=['seasonality', 'trends', 'promotions', 'events']
        )

        # 3. Calculate optimal action
        reorder_point = demand_forecast * 1.2  # 20% safety margin

        if current_stock < reorder_point:
            order_quantity = self.calculate_eoq(sku, demand_forecast)

            # 4. Execute autonomously
            po = self.create_purchase_order(sku, order_quantity)
            self.send_to_supplier(po)
            self.notify_stakeholders(po)

            return ActionTaken(type='reorder', quantity=order_quantity)

        return ActionTaken(type='no_action', reason='sufficient_stock')

Real Results:

Metric	Before	After	Improvement
Stockout Rate	8.5%	1.2%	-86%
Overstock Value	$2.4M	$0.8M	-67%
Manual PO Creation	500/day	12/day	-98%
Demand Forecast Accuracy	72%	91%	+19pp

Agent 3: Dynamic Pricing Agent

What It Does:

• Analyzes competitor prices in real-time (every 15 minutes)
• Considers demand elasticity, inventory levels, margin targets
• Adjusts prices automatically within defined guardrails
• A/B tests pricing strategies autonomously

Multi-Factor Decision Matrix:

Price Decision = f(
    competitor_prices,      # Real-time competitive position
    demand_elasticity,      # How price-sensitive is this product?
    inventory_level,        # Need to clear stock?
    margin_floor,           # Minimum acceptable margin
    seasonality,            # Time-based demand patterns
    customer_segment,       # Different prices for different segments
    promotion_calendar      # Upcoming sales events
)

Guardrails (Human-Defined Boundaries):

• Maximum price change: ±15% per day
• Minimum margin: 12% for all products
• Competitor matching: Only for verified competitors
• Review required: Changes affecting >$100K revenue

Results:

Metric	Manual Pricing	Agent Pricing	Change
Price Updates/Day	200	15,000	+7,400%
Margin Optimization	Baseline	+3.2pp	Significant
Competitive Response Time	24 hours	15 minutes	-99%
Revenue per SKU	Baseline	+8.5%	Significant

3. Building Reliable AI Agents

The Reliability Challenge

AI agents can fail in ways traditional software doesn't:

• Hallucination: Making up information or taking wrong actions
• Infinite Loops: Getting stuck in repetitive patterns
• Scope Creep: Taking actions beyond their authority
• Context Loss: Forgetting important information mid-task

Our Reliability Framework

1. Structured Output Enforcement

{
  "thought": "Customer wants refund for order #12345",
  "plan": [
    "Verify order exists and is refundable",
    "Check refund policy compliance",
    "Process refund via payment gateway",
    "Send confirmation email"
  ],
  "next_action": {
    "tool": "order_lookup",
    "parameters": {"order_id": "12345"}
  },
  "confidence": 0.95
}

2. Action Validation Layer

Agent Proposed Action
         │
         ▼
┌─────────────────┐
│ Schema Check    │──► Is the action format valid?
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Permission Check│──► Does agent have authority?
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Safety Check    │──► Could this cause harm?
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Business Rules  │──► Does it comply with policies?
└────────┬────────┘
         │
         ▼
    Execute or Reject

3. Human-in-the-Loop Triggers

• Confidence score below threshold (< 0.8)
• High-value decisions (> $10,000 impact)
• Customer escalation requests
• Novel situations not in training data
• Consecutive failures (> 2 attempts)

4. Comprehensive Logging Every agent action is logged with:

• Full reasoning chain
• Tool calls and responses
• Decision confidence scores
• Outcome measurements
• Human override records

4. Multi-Agent Orchestration

When One Agent Isn't Enough

Complex commerce operations require multiple specialized agents working together:

Example: New Product Launch

┌─────────────────────────────────────────────────────────────────┐
│                    Orchestrator Agent                            │
│         "Launch new product SKU-789 in EU market"               │
└─────────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│ Inventory     │    │ Pricing       │    │ Marketing     │
│ Agent         │    │ Agent         │    │ Agent         │
├───────────────┤    ├───────────────┤    ├───────────────┤
│  Set up SKU   │    │  Analyze      │    │  Create       │
│  Allocate     │    │  competitors  │    │  campaigns    │
│  initial stock│    │  Set launch   │    │  Schedule     │
│  Configure    │    │  pricing      │    │  promotions   │
│  fulfillment  │    │  Define       │    │  Prepare      │
│               │    │  discounts    │    │  content      │
└───────────────┘    └───────────────┘    └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              │
                              ▼
                    ┌───────────────┐
                    │ Compliance    │
                    │ Agent         │
                    ├───────────────┤
                    │  Verify legal │
                    │  Check taxes  │
                    │  Approve      │
                    │  launch       │
                    └───────────────┘

Agent Communication Protocol

Message Format:

{
  "from": "inventory_agent",
  "to": "pricing_agent",
  "message_type": "data_share",
  "content": {
    "sku": "SKU-789",
    "initial_stock": 5000,
    "unit_cost": 24.50,
    "warehouse_locations": ["EU-DE", "EU-NL"]
  },
  "context": {
    "task_id": "launch-789",
    "step": 3,
    "deadline": "2025-12-15T00:00:00Z"
  }
}

Conflict Resolution

When agents disagree:

1. Escalate to orchestrator for arbitration
2. Apply business rules as tiebreaker
3. Human review for unresolved conflicts
4. Log and learn for future improvements

5. The Technology Stack

LLM Selection

Use Case	Model	Why
Complex Reasoning	Claude 3.5 Sonnet	Best at multi-step planning
Fast Classification	GPT-4o-mini	Low latency, good accuracy
Code Generation	Claude 3.5 Sonnet	Superior code quality
Embeddings	text-embedding-3-large	Best retrieval performance

Agent Framework

Core Components:

• LangGraph: For agent workflow orchestration
• Custom Tool Framework: Type-safe tool definitions
• Vector Store: Pinecone for semantic memory
• State Management: Redis for session state

Infrastructure

┌─────────────────────────────────────────────────────────────────┐
│                       Agent Gateway                              │
│    Rate limiting  Authentication  Request routing           │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    Agent Execution Cluster                       │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │ Worker 1 │  │ Worker 2 │  │ Worker 3 │  │ Worker N │        │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘        │
│                    (Auto-scaled 10-100 workers)                  │
└─────────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│ LLM Gateway   │    │ Tool Gateway  │    │ Memory Store  │
│ (Load balanced│    │ (Rate limited │    │ (Redis +      │
│  multi-model) │    │  access)      │    │  Pinecone)    │
└───────────────┘    └───────────────┘    └───────────────┘

6. Measuring Agent Performance

Key Metrics

Autonomy Rate:

• Percentage of tasks completed without human intervention
• Target: >90% for routine tasks

Task Success Rate:

• Percentage of tasks achieving desired outcome
• Target: >95%

Efficiency Gain:

• Time/cost savings vs. manual execution
• Measured per task type

Safety Metrics:

• False positive rate (unnecessary escalations)
• False negative rate (missed escalations)
• Policy violation incidents

Real-Time Dashboard

Agent	Tasks Today	Success Rate	Autonomy	Avg Duration
Support	12,450	94.2%	87%	2.3 min
Inventory	3,200	98.1%	96%	45 sec
Pricing	28,000	99.5%	99%	12 sec
Marketing	450	89.3%	72%	15 min

7. Lessons Learned

What Works

1. Start narrow, expand carefully: Begin with well-defined tasks before complex workflows
2. Guardrails are essential: Every agent needs boundaries and human escalation paths
3. Observability from day one: You can't improve what you can't measure
4. Test with real scenarios: Synthetic tests miss edge cases that production reveals

Common Pitfalls

1. Over-automation: Not every task should be autonomous
2. Insufficient context: Agents need comprehensive information to decide well
3. Poor error handling: Agents must fail gracefully and learn from failures
4. Ignoring latency: LLM calls add latency; design for user experience

Future Roadmap

2025 Q4:

• Multi-modal agents (processing images, documents)
• Voice-based agent interactions
• Cross-platform agent coordination

2026:

• Fully autonomous supply chain management
• Self-improving agents that learn from feedback
• Agent-to-agent commerce negotiations

Key Takeaways

Aspect	Traditional Automation	AI Agents
Flexibility	Fixed rules	Adaptive reasoning
Complexity	Simple tasks	Multi-step workflows
Maintenance	Code changes	Prompt/context updates
Edge Cases	Fail or escalate	Reason and adapt
Scale	Linear cost	Sub-linear cost

Conclusion

AI agents represent the next evolution in commerce automation. By combining the reasoning capabilities of large language models with robust tool integration and safety frameworks, we can build systems that handle complex tasks autonomously while maintaining human oversight where it matters.

At Tanqory, AI agents are already processing millions of decisions daily—reducing costs, improving speed, and freeing human teams to focus on strategic work. The future of commerce is autonomous, and it's already here.

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This article is part of our Engineering Series exploring cutting-edge AI implementation at Tanqory. For questions or collaboration opportunities, reach out to info@tanq.com.sg

Published by Tanqory Engineering Team | December 2025