Supplier onboarding can be surprisingly expensive.
Supplier onboarding can be surprisingly expensive.
Not because the final decision is always difficult, but because the process usually crosses procurement, security, compliance, policy, and multiple internal systems.
That makes it a very good candidate for a governed agentic workflow.
I built a small AWS reference implementation called supplier-agent-demo around a simple business question:
“Can Supplier Acme be onboarded?”
The answer is:
NOT_READY
The supplier is APPROVED, but its security assessment is still PENDING, and policy requires COMPLETE.
The important part is not the answer. It is how the answer is produced:
INTENT → QUESTIONS → CAPABILITY SELECTION → EVIDENCE → OBSERVATION → INFERENCE → GAP CHECK → DECISION
Every policy gate cites the evidence used to reach the result.
The agent does not simply “make the decision.”
It reasons about what it needs to know.
Capabilities retrieve the facts.
Deterministic policy evaluates the gates.
Evidence proves what happened.
Agents reason. Capabilities act. Evidence proves.
That, to me, is a much better pattern for enterprise AI than simply putting an LLM in front of a workflow and hoping it behaves.
#AgenticAI #EnterpriseAI #AIArchitecture #IntentDrivenEngineering #AWS #AIGovernance #AgenticWorkflows
Pairing this with the architecture diagram will make the post much stronger.
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