The problem worth solving
There is a gap the whole AI-agent industry now calls the productionization gap: an agent is easy to build and hard to trust. In a real support context a confident but unsupported answer is not a bad experience, it is a compliance and trust liability. The interesting problem is not can the agent answer, it is does the agent know when it should refuse.
How it works
A Manager agent orchestrates four specialists, plus a separate independent finalizer. Verification is its own stage that can veto a response, not a rule folded into drafting. That separation is the single most important structural decision in the system.
The architecture
Each agent has its own model and temperature, chosen for its job. The retrieval and verification agents run near-deterministic, because a checker should not be creative.
| Agent | Role | Model | Temp |
|---|---|---|---|
| Resolution Coordinator | Manager, orchestrates the pipeline and enforces the safety gate | claude-sonnet-4-6 |
0.2 |
| Classification Agent | Category, priority, intent | gpt-4o-mini |
0.2 |
| KB Retrieval Agent | Semantic RAG, returns articles with relevance scores | gpt-4o |
0.1 |
| Response Drafting Agent | Drafts a reply grounded strictly in retrieved articles | claude-sonnet-4-6 |
0.2 |
| Grounding Verification Agent | Verifies every claim, returns Grounded / Not Grounded / No Source | claude-sonnet-4-6 |
0.1 |
| Response Finalizer | Independent, polishes the human-approved draft | gpt-4o |
0.3 |
I stress-tested my own grounding claim, with numbers
A promise like the system refuses when it cannot ground an answer is only worth making if it is measured. I ran a six-ticket set, four that should ground and two that must escalate, each multiple times, because a single run cannot reveal consistency.
The headline result: verification caught what retrieval missed
On the SAML SSO ticket, retrieval returned a false roughly 90% match. Retrieval alone would have passed that to the drafter and produced a confident answer built on unrelated content. The verification agent recognized the retrieved content did not support a SAML answer, suppressed the draft, and escalated. This is the concrete proof that grounding needs a dedicated verification stage rather than trust in a retrieval score.
The product point underneath the system
When uncertain, the system over-escalates to a human rather than hallucinating, which is the correct direction to fail for support. The real product question is not eliminate all variance, it is where the safe-fail boundary should sit: a tunable, instrumented decision a PM makes, not an engineering afterthought.