Searching & Retrieving Your GenAI Data
A Deep Dive into Self-Querying
(Multi-Step Approaches)
Workshop
Can your agents decide when to search—and do so safely, efficiently, and with measurable value?
Self-querying allows LLM agents to initiate their own searches, but without clear triggers, controls, and evaluation, self-directed retrieval can quickly become expensive, opaque, and risky.
To win, your self-querying agents must know when to search, stay within guardrails, and justify their cost.
The Challenge
Teams enabling self-querying often struggle with:
- Uncontrolled triggers: Agents query too often—or not at all—without clear conditions or intent.
- Opaque behavior: Limited visibility into why agents searched, what they retrieved, and how results were used.
- Unproven value: Difficulty measuring accuracy, cost, and return on investment from self-directed retrieval.
Unmanaged self-querying increases cost, reduces predictability, and undermines trust in agent behavior.
Our Solution
In this hands-on workshop, your team designs self-querying approaches that balance autonomy, control, observability, and ROI.
- Enable self-querying capabilities for LLM agents in retrieval workflows.
- Determine explicit trigger conditions that justify when agents should query.
- Monitor self-directed search behavior to improve transparency and control.
- Constrain and steer agent autonomy using rules, limits, and feedback.
- Evaluate self-query accuracy and return on investment to support production decisions.
Area of Focus
- Enabling Self-Querying for LLM Agents
- Determining Trigger Conditions for Querying
- Monitoring Self-Directed Search Behavior
- Constraining and Steering Agent Autonomy
- Evaluating Self-Query Accuracy and ROI
Participants Will
- Enable agents to initiate retrieval only when it adds clear value.
- Define trigger conditions that govern self-directed querying behavior.
- Monitor and analyze how and why agents perform searches.
- Apply constraints that balance agent autonomy with operational safety.
- Evaluate self-querying systems based on accuracy, cost, and ROI.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 hours
Skill Level
Advanced; experience with agents or retrieval systems recommended
Tools
LLM agents, self-querying patterns, monitoring and evaluation frameworks