Best Practices for Agentifying ERP Workflows

Legacy ERP systems like SAP, NetSuite, PeopleSoft, and Oracle EBS are notoriously difficult to integrate with modern automation platforms. GLIK enables a non-invasive, AI-native approach by embedding orchestration logic outside the ERP boundary.

Here are best practices to follow:

1. Target File-Based Surfaces First

Most ERPs generate structured output as:

  • PDF invoices

  • CSV exports

  • Excel-based ledgers

  • Email-based approvals or exceptions

Use these as the entry point for your GLIK workflow instead of trying to interface directly with the ERP backend.

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2. Use OCR and Classifiers to Bridge Structured + Semi-Structured Inputs

Blocks like Doc Extractor and Question Classifier let you turn PDFs or form exports into actionable signals. This is critical when dealing with:

  • Expense receipts

  • Supplier forms

  • Procurement logs

3. Load Internal Logic via Knowledge Retrieval

Instead of hardcoding policy logic, define your:

  • Thresholds

  • Blacklisted vendors

  • Approval hierarchies in a Markdown or CSV file, then use Knowledge Retrieval to pull and apply them dynamically.

This makes your workflows portable across departments — even if each one has a different policy schema.

4. Treat Approval Chains as Agent Loops

Where ERPs traditionally rely on multi-step human review flows:

  • Use Loop and IF/ELSE blocks to simulate those steps

  • Add Agent blocks to represent managers, reviewers, or compliance units

  • Use Template or HTTP Request blocks to output approvals or escalate issues via email or Slack

5. Log Everything for Auditors

ERP replacements often fail because they lose visibility. Use:

  • Variable Aggregator

  • Code block

  • Save Point Agent (or custom Template → JSON output) to generate a complete trail of decisions, timestamps, and policy sources.


GLIK vs Traditional RPA/BPM Tools

Aspect
Traditional RPA / BPM
GLIK AI Orchestration

Integration method

UI scripting, screen scraping

Agent-based logic via files, forms, memory, LLM

Deployment speed

Weeks/months

Hours/days

System risk

Modifies fragile interfaces

Wraps around existing systems (non-invasive)

Automation logic

Rules, flows, macros

LLMs + agents + knowledge-based policy execution

Resilience

Breaks on UI changes or updates

Abstracted from UI or code-level changes

Memory & policy reuse

Siloed automations

Scoped variables, reusable policy logic

Escalation path

Hard-coded, rarely dynamic

Agent delegation + human-in-the-loop compatible

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