Specialized AI agents that hand off work, not drop it.
A confidential engagement building a multi-agent AI system that classifies, resolves and escalates customer and operational requests — with human staff always the last word on complex cases.
Confidential client engagement. Client details are withheld.
The challenge
The client was handling a high volume of customer and operational requests through manual triage — repetitive questions consumed staff time that complex, high-stakes cases actually needed.
What we built
A multi-agent system with specialists for customer support, ticket classification, knowledge retrieval, order/request status lookups, and internal operations, coordinated so agents hand work to whichever specialist is best suited — and escalate to a human the moment a case exceeds what an agent should resolve alone. Every handoff and escalation is logged for review.
How it's built
AI & automation capabilities
Key features
01 · Classification
Every request routed to the right specialist
Incoming requests are classified and routed to whichever agent actually handles that type of case, instead of one general-purpose bot attempting everything.
02 · Handoff
Agents hand off work, not drop it
When a request spans more than one specialty, agents pass it between each other with a summary attached — nothing gets silently dropped between steps.
03 · Escalation
Human staff get the complex cases
The moment a case exceeds what an agent should resolve alone, it escalates to a human — with the full conversation summary already prepared for them.
04 · Controls
Every handoff and escalation logged
Enterprise controls and a logged audit trail mean the client can review exactly what an agent decided and why, not just what it eventually said to the customer.
Stack
What the client said
“We weren't looking for an AI chatbot that would simply answer questions. We needed something that could handle routine requests while knowing when a real person needed to step in. MetaBuf understood that distinction and built the workflow around it. The team was particularly good at thinking through the practical side of how the system would actually be used by our staff.”
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