customer_support · finance · workflow
Building resilient agentic systems: provider and model failover at Gradient Labs
AI agents make chains of LLM calls where each step costs latency and money, so a single failure could force the entire chain to restart; for a customer-facing financial services agent, high reliability is non-negotiable.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer request received
The AI agent receives a request from a customer of a financial services company.
Tools used
TemporalOpenAIAnthropicGoogleAzure
Outcome
Gradient Labs built a layered resilience system using Temporal for durable execution plus provider and model failover, ensuring customers continue to receive replies even when entire LLM provider groups are down.
What failed first
A provider latency spike shifted the entire latency distribution upward without triggering the existing per-request timeout-based failover mechanism, requiring manual intervention.
Results
Time savedwell over 10s
Grounding & classification
Source type: technical build writeup
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agentic workflowai agentconversational aibuilder submittedfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedfinancial servicestechnical build writeupcustomer supportagentic task executionescalation workflow