CASE STUDY 02 · INTELLIGENT AUTOMATION
A document-driven case workflow where an LLM can interpret ambiguity, while deterministic policy, schemas and human review retain authority over what is allowed to execute.
PROBLEM
LLMs are useful for extracting intent from ambiguous inputs, but enterprise execution requires deterministic controls. The architecture keeps probabilistic recommendation separate from authorization and tool execution.
CORE DECISION
Structured outputs are validated through schemas. Policy evaluates the recommendation. Human approval is inserted when required, and only controlled tools can perform downstream actions.
CONTROL FLOW
Function/tool calling and typed validation reduce free-form downstream behavior.
Document content is treated as untrusted data rather than executable instruction.
Human-in-the-loop remains available for decisions requiring explicit approval.
A regression dataset provides repeatable evidence when prompts or logic change.
TRADE-OFFS
The business flow does not require unrestricted planning or autonomous execution.
Retrieval is not introduced without a knowledge-retrieval requirement that justifies its operational complexity.
Multiple agents would add coordination and evaluation complexity without evidence of a corresponding business benefit.
Execution can delegate to the Enterprise Automation Orchestrator rather than duplicating its reliability responsibilities.
ENGINEERING PRINCIPLE
This is a portfolio/reference implementation for controlled intelligent automation, not a claim of an autonomous production agent platform.