Agentic AI validation shows potential for 40% fewer firmware defects for a global networking provider

A global leader in enterprise networking and edge connectivity solutions.

That was the question on the table. In networking and embedded systems, test cases are full code programs—not simple scripts—so when they fail, engineers rather than testers must diagnose the issue, rebuild the test, and restart the cycle. Each failure pulls developers off feature delivery, stretches release timelines, and increases the likelihood that defects reach production.

To address this, they needed a partner to explore what AI could realistically deliver in such a specialized testing domain. The approach had to be low-risk and outcome-driven, creating mutual accountability and the confidence to scale if the results proved viable.

Used an AI Lab-as-a-Service model to explore and validate an agentic AI use case for automating firmware test case triage, diagnosis, and reconstruction. Included GenAI architecture, workflow orchestration, and secure, isolated infrastructure.

Pairing advanced GenAI skillsets with our AI incubator services, we validated this specialized use case with fast, iterative cycles. By refining the agent with real test case data and client collaboration, we confirmed feasibility and established a low-risk path to scale.

1. Designed a sequenced multi-LLM workflow that increases reliability and prevents errors from carrying through each step.
2. Customized and fine-tuned models to address recurring execution errors and domain-specific logic patterns.
3. Implemented automated workflows for failure analysis, RCA, and script regeneration without developer intervention.
4. Operated all models within an isolated infrastructure environment to ensure performance and security.