Accelerated Innovation

Searching & Retrieving Your GenAI Data

A Deep Dive into ReAct Agent Based Retrieval

Workshop
Can your retrieval systems reason, act, and adapt across multi-step search journeys?

ReAct-style agents introduce powerful new retrieval patterns, but many teams struggle to control agent behavior, design effective prompt chains, or evaluate complex multi-hop reasoning paths. 

To win, your retrieval agents must reason transparently, search iteratively, and remain controllable in production environments. 

The Challenge

Teams experimenting with ReAct agent-based retrieval often encounter: 

  • Unclear agent roles: Uncertainty around what capabilities agents should own versus what retrieval systems should control. 
  • Runaway autonomy: Prompt chains that over-reason, loop, or drift without clear guardrails. 
  • Opaque reasoning: Limited visibility into agent rationales and multi-hop query paths. 

Without disciplined design, ReAct-based retrieval becomes unpredictable, untestable, and hard to trust. 

Our Solution

In this hands-on workshop, your team designs and evaluates ReAct agent-based retrieval workflows with an emphasis on control, transparency, and multi-hop reasoning. 

  • Define ReAct agent capabilities specifically for retrieval use cases. 
  • Design prompt chains that support iterative, stepwise search. 
  • Balance agent autonomy with explicit control and stopping conditions. 
  • Capture and structure rationales produced by agents during retrieval. 
  • Evaluate multi-hop query paths for correctness, efficiency, and relevance. 
Area of Focus
  • Defining ReAct Agent Capabilities in Retrieval 
  • Designing Prompt Chains for Iterative Search 
  • Balancing Agent Autonomy and Control 
  • Capturing Rationales in Agent Responses 
  • Evaluating Multi-Hop Query Paths 
Participants Will
  • Define clear roles for ReAct agents within retrieval systems. 
  • Design prompt chains that support reliable iterative search. 
  • Apply controls that prevent runaway or unsafe agent behavior. 
  • Capture agent rationales to improve transparency and debugging. 
  • Evaluate multi-hop retrieval paths with confidence. 

Who Should Attend:

Solution ArchitectsPlatform EngineersBackend EngineersGenAI EngineersSearch Engineers

Solution Essentials

Format

Virtual or in-person 

Duration

8 hours 

Skill Level

Advanced; experience with agents or retrieval systems recommended 

Tools

ReAct-style agents, prompt chaining frameworks, controlled retrieval environments 

Is your team ready to evaluate multi-hop agent-based search with confidence?