How Do AI Agents Actually Work in ERP Environments?

The Black Box Problem 

Most explanations of AI agents focus on underlying technology - language models, APIs, workflow orchestration. These explanations satisfy curiosity but do not help operational leaders understand what the agent actually does in their ERP environment. 

The operational question is more practical:  

What does the agent do with your data?  

How does it decide what action to take?  

When does it escalate to humans?  

How does information flow between agent and ERP? 

This article explains AI agent operation in ERP environments from an operational workflow perspective. 

The Basic Operational Flow 

AI agents continuously review ERP data, apply your business rules to identify actions needed, execute those actions through communication and system updates, document all activity, and escalate situations requiring human judgment. 

The agent operates in a continuous cycle: 

1. Data Review: Agent queries ERP system for exception data 

2. Rule Application: Agent applies your decision criteria to each exception 

3. Action Execution: Agent takes defined actions (calls, emails, system updates) 

4. Outcome Documentation: Agent records all activity and results in ERP 

5. Escalation: Agent flags situations requiring human intervention 

This cycle repeats daily or continuously depending on process requirements. 

AR Collections Example  

Step 1: Data Review 

Agent queries ERP system for aged receivables data daily, pulling accounts meeting criteria: balance overdue beyond terms, account in good standing, not flagged for special handling. 

What the Agent Sees: Customer name and contact information, invoice numbers and amounts overdue, days past due, payment history, previous collection notes, dispute flags. 

Human Role: Define which data points matter for decision-making. Review data quality. 

Step 2: Rule Application 

Agent applies prioritization rules to determine which accounts to contact and in what order. 

Example Rules: 

• Priority 1: 60+ days overdue, balance over $5,000, no dispute 

• Priority 2: 45-59 days overdue, balance over $2,000, payment promise broken 

• Priority 3: 30-44 days overdue, first-time overdue 

• Do not contact: Flagged accounts, disputes in process, VIP customers 

Agent creates a call queue ordered by priority. 

Human Role: Define prioritization criteria. Adjust rules based on outcomes. Maintain flagged account list. 

Step 3: Action Execution 

Agent initiates phone calls following conversation scripts you defined. 

Conversation Flow: Agent introduces itself and reason for call, states overdue amount, requests payment commitment, responds to customer statements based on script logic, confirms commitment details or documents reason for non-payment. 

Decision Points: 

• Customer commits to payment → Confirms date and amount 

• Customer disputes balance → Documents details, escalates to accounting 

• Customer requests payment plan → Follows payment plan script or escalates 

• Customer becomes hostile → Escalates immediately 

Human Role: Define conversation scripts. Set escalation triggers. Review call recordings. Refine scripts based on outcomes. 

Step 4: Outcome Documentation 

Agent updates ERP system immediately after each call using API to write updates to customer account notes, activity logs, or collection management fields. 

Documentation Includes: Call date and time, contact reached, customer response category, payment commitment details, dispute details if applicable, next action required, escalation flag if needed. 

Human Role: Define what information must be captured. Specify where in ERP to record data. 

Step 5: Escalation Handling 

Agent identifies situations requiring human intervention based on defined criteria. 

Escalation Triggers: Customer disputes invoice accuracy, payment commitment exceeds standard terms, customer reports financial distress, customer becomes hostile, technical issue prevents call completion. 

Agent creates task in ERP assigned to appropriate staff member with complete call context. 

Human Role: Handle escalated situations. Provide guidance on resolution. Update rules based on escalation patterns. 

The Continuous Improvement Loop 

Weeks 1-2: Agent makes calls. Staff review all recordings. Identify conversation flow issues, unclear script language, missing escalation triggers. 

Weeks 3-4: Scripts updated based on issues identified. Success rate improves from 55% to 65%. 

Weeks 5-8: Staff review escalations for patterns. Some situations initially escalated can be handled by agent with additional script logic. 

Ongoing: Monthly outcome review. Quarterly rule refinement. Agent handles increasing percentage of standard situations. 

Human Oversight in Practice 

Human oversight continues indefinitely: 

Daily Oversight: Review escalated situations, monitor call completion rates, check for system errors, validate high-value payment commitments. (30-60 minutes) 

Weekly Oversight: Review sample call recordings, analyze success rates, identify script improvements, review escalation patterns. (1-2 hours) 

Monthly Oversight: Comprehensive outcome analysis, rule refinement discussions, cost-benefit review, expansion or scope adjustment decisions. (2-3 hours) 

This oversight requirement is significantly less than time spent on manual exception handling but remains necessary. 

What Agents Cannot Do 

Strategic Decisions: Agents cannot set collection policies, establish payment terms, or make exceptions to credit rules. 

Relationship Management: Agents cannot build customer relationships, negotiate complex arrangements, or handle VIP accounts requiring personal attention. 

Unstructured Problem-Solving: Agents cannot investigate unique situations or create custom solutions outside defined rule sets. 

Emotional Intelligence: Agents detect frustration but cannot respond with empathy. Human escalation handles emotional situations. 

Success Factors 

Implementations succeed when: 

Clear Rules Exist: Staff can articulate decision criteria for 80%+ of situations. 

Data Quality Is Adequate: Contact information is current. Account data is accurate. 

Staff Engage in Refinement: Implementation team participates in testing, reviews outcomes, and refines scripts. 

Expectations Are Realistic: Understanding agents handle 60-80% of standard situations with human escalation for complex cases. 

Volume Justifies Effort: Process handles 30+ exceptions monthly. 

The Operational Reality 

AI agents in ERP environments are systematic automation of decision-making and coordination work that staff currently perform manually. 

The agent follows rules you define. It operates within boundaries you set. It escalates when situations exceed those boundaries. It documents everything systematically. 

Implementation requires operational knowledge - understanding the exception process, articulating decision rules, defining escalation criteria. The result is not elimination of human involvement. The result is systematic handling of routine situations, complete documentation, and focused human attention on situations requiring judgment. 

About the Author 

This content is published by ERP AI Agent, a consulting practice specializing in AI agents for mid-market ERP exception processes. 

Published: January 2025 Last Updated: January 2025 Reading Time: 7 minutes