Putting a semantic cache in front of an AI agent, from signup to dashboard
An AI agent asks the model the same things every time it runs the same task: the same plan step, the same "did that work?" check, the same summary of the same docs. We recorded the whole path of putting the Sulci semantic cache in front of one, from signup to dashboard, in six minutes: This post covers the parts worth reading slowly: the three code changes, the agent settings, and the moment the…
The article describes implementing a semantic cache in front of an AI agent to optimize task completion, specifically deploying a web service to AWS. The agent repeatedly asks the model the same questions for each step of the task, such as deploying the web service, configuring AWS credentials, and checking if the step succeeded.
By caching frequent queries, the model can avoid reprocessing identical inputs, saving time and resources. The caching system uses the Sulci backend, which stores responses and tracks token usage for a dashboard. The agent's settings include strict threshold criteria to ensure only relevant steps are served from the cache, while allowing for unique cases like failed steps.
In the demo, 11 out of 18 steps were retrieved from the cache, demonstrating potential efficiency gains. The overall approach emphasizes optimizing repetitive AI tasks while maintaining the flexibility to handle unique situations.
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