Workflow · saas · workflow
How Hornet builds a verifiable retrieval engine for AI agents
Hornet's API surface is not in any LLM's pre-training data, making standard approaches like prompt injection and in-context learning insufficient, while most organizations face complex engines, steep learning curves, and heavy operational overhead when building retrieval for AI.
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 · Agent attempts API call
An agent attempts to create a configuration or query using Hornet's API surface.
Tools used
HornetOpenAPI
Outcome
By making Hornet's API surface fully verifiable, agents can configure and optimize retrieval through self-correcting feedback loops, enabling production-ready retrieval with safe versioned deployments and a self-reinforcing improvement cycle.
What failed first
Injecting documentation into the prompt, relying on in-context learning, and hoping frontier models would figure out Hornet's new API surface all failed to work well enough.
Results
Volumeimprove relevance, tune recall/latency tradeoffs
Grounding & classification
Source type: technical build writeup
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agentic workflowai agentragknowledge basefailure mode describedsource backedtools describedworkflow describedsoftwareaccuracy improvementtechnical build writeupagentic task execution