7
AI
AI agent vs traditional workflow for invoice processing
Intelligence ↔ Predictability
Context
Automate processing of ~40K supplier invoices per month.
We receive invoices in every format imaginable. A vendor demoed an autonomous agent that reads the invoice, matches the PO, flags discrepancies and posts to the ERP.
Our finance controller wants every step auditable. My instinct: deterministic workflow (state machine) with the LLM used only for extraction, plus validation rules and human review below a confidence threshold.
Where are teams drawing the line between "agent" and "workflow with an LLM step"?
Constraints
- Accuracy
- > 99% on totals
- Audit
- Every step explainable
LLM agentvsDeterministic workflow + LLM extraction
Community verdict
LLM agent 25%Deterministic workflow + LLM extraction 75%
8 engineers · 3 opinions
With these constraints, what would you choose?
One choice per engineer. You can change it any time.
LLM agent
Deterministic workflow + LLM extraction
Predictability →
Trade-offs
Dimensions
LLM agentDeterministic workflow + LLM extractionout of 5
- Adaptability
- 5LLM agent scores 5 of 53Deterministic workflow + LLM extraction scores 3 of 5
- Predictability
- 2LLM agent scores 2 of 55Deterministic workflow + LLM extraction scores 5 of 5
- Auditability
Community discussion
3 comments
Have you made this decision in production? Share your reasoning.
Sign in to commentAgents earn their keep in the long tail: credit notes, split deliveries, currency mismatches. Maybe route only the exceptions to an agent, with a human approving its proposal.
Use the LLM where the input is unstructured (extraction) and code where the rules are known (3-way matching, tolerances, posting). You get the best of both, and an auditor can read the state machine.
Also much easier to threat-model. An agent with ERP write access plus prompt injection hidden in a PDF is a scary combination.