PS DEV

Session Description

Relevance feedback loops used to take months. Developers would capture interaction data, train models offline, and push updates through slow deployment cycles. The arrival of AI agents as a new class of search user has compressed this cycle to seconds. In agentic workflows, retrieval is no longer a single tool call that returns results; it is a tight, iterative loop where the agent refines its own queries, evaluates result quality, and tries again.

This talk goes beyond basic retrieval-augmented generation (RAG) to explore what comes next: Agentic Retrieval. We are entering a paradigm where agents don’t just reformulate queries, but dynamically adjust the retrieval system itself, tuning scoring models, modifying schema configurations, and making indexing decisions to match the specific demands of a task. This is the logical extreme of the feedback loop: a self-reinforcing system where the agent optimizes its own context window.

We will present the infrastructure principles that make this possible, drawing on our work building agent-native retrieval at Hornet. The talk covers:

• Schema-first API design that gives agents a structured, predictable interface to work with
• Verifiable state changes that let agents confirm the effect of their own modifications
• RL-compatible feedback signals that enable agents to self-correct rather than relying on human-in-the-loop tuning

Attendees will leave with a concrete understanding of how to architect a retrieval stack where agents can tune their own environment in real time, and why the shift from human-facing search to agent-facing retrieval infrastructure demands fundamentally different design choices.