back_office_ops · workflow
Trendyol autooptimizer: autonomous AI agent achieves 4x inference serving performance on Gemma 4 26B in 18 experiments
Manual LLM inference serving configuration tuning requires hours of iterative flag-flipping, restarts, and benchmarking across a huge interacting parameter space, with results tracked only in half-remembered spreadsheets and no systematic search strategy.
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 · User specifies model and framework
The user points autooptimizer at a model and a serving framework to begin the optimization loop.
Tools used
autooptimizervLLMSGLang
Outcome
The autonomous agent ran 18 experiments on Gemma 4 26B and delivered a serving configuration scoring 4x higher than the defaults (baseline 167, final ~640) with zero human intervention, completing work that would otherwise take a human a full day.
Results
Volume4x higher than the defaults
Grounding & classification
Source type: technical build writeup
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agentic workflowai agentbuilder submittedfailure mode describedmetric backednamed customersource backedtools describedworkflow describedecommercesoftwarecycle time reductionemployee productivitythroughput increasetechnical build writeupback office opsagentic task execution