Selected Delivery Experience
Agentic AI Customer Service Platform
Helped architect and productionize an LLM and speech-to-speech platform with autonomous service agents.
Context
The work addressed a customer-service environment where an agent had to interpret intent, use the right information, respond quickly, and transfer control appropriately when automation was not the right path.
Why the problem was difficult
A convincing conversation in a controlled demo does not prove operational readiness. Real service environments introduce noisy speech, incomplete context, latency constraints, safety boundaries, changing knowledge, unpredictable requests, and consequential handoffs.
Approach
- Designed agent orchestration and prompt architecture
- Addressed speech-to-speech interaction and latency management
- Built observability and controls for hallucination risk
- Defined human escalation paths and production reliability considerations
Production challenges addressed
Measured results
Only verified scale and outcomes are shown.
- Production-oriented service-agent architecture
- Human escalation designed into the operating flow
Key lesson
An autonomous agent is an operational system, not a prompt. The architecture must make uncertainty, handoffs, and failure modes visible and manageable.
A useful next step