Workflow · Production

How Hornet builds a verifiable retrieval engine for AI agents

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Agent attempts API call
trigger
“It might not create a valid configuration or query at first try, but as the API is verifiable, the agent can observe the failure and associated error response”
2
Syntax validation via OpenAPI
validation
“Hornet APIs are defined by an OpenAPI specification. This way, the agents create syntactically correct document and query schemas, just like checking if code compiles”
3
Configuration model validation
validation
“some settings can't be used together. Syntax checks alone can't catch this, but a Hornet configuration model can. We model which combinations are allowed and give the model concrete and detailed feedback during validation. When an agent …”
4
Behavioral quality validation
validation
“Here we check whether the engine behaves as expected: Do the right documents appear? Are they ranked correctly? Does the query perform well? Is the resource footprint acceptable?”
5
Agent self-correction
feedback_loop
“The agent can read the response and correct it until success. This is the core of the agentic feedback loop”
6
Versioned deployment
output
“deployments are versioned rollouts that can be verified and safely reverted”
Reported 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.

Reported metrics
Context quality and reasoningBetter context means better reasoning, which means better outcomes
Retrieval relevance and recall/latency tradeoffsimprove relevance, tune recall/latency tradeoffs
Reported stack
HornetOpenAPI
Source
https://blog.hornet.dev/how-we-build-a-retrieval-engine-for-agents
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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-reinf…

What tools did this team use?

Hornet, OpenAPI.

What results were reported?

Context quality and reasoning: Better context means better reasoning, which means better outcomes; Retrieval relevance and recall/latency tradeoffs: improve relevance, tune recall/latency tradeoffs (source-reported, not independently verified).

What failed first in this deployment?

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.

How is this workflow AI workflow structured?

Agent attempts API call → Syntax validation via OpenAPI → Configuration model validation → Behavioral quality validation → Agent self-correction → Versioned deployment.