Agentic RAG in Production: Orchestration, Evaluation & ROI
Most RAG pilots stall after the demo stage — they hallucinate, fail under orchestration, or can’t prove measurable ROI.
This session introduces Agentic RAG: retrieval that’s decided, evaluated, and improved by agents inside enterprise workflows.
You’ll learn how to combine:
- Salesforce Agentforce for agentic orchestration in sales, service, and checkout flows.
- Data Cloud as the factual grounding layer with Customer 360 and predictive signals.
- LangGraph orchestration patterns for multi-agent reasoning, reflection, and escalation.
- Evaluation frameworks (RAG Triad, TruLens, Ragas) for provable accuracy, fairness, and ROI.
We’ll end with a live scenario — the Smart Checkout Helper — that grounds itself in customer data, self-corrects, and quantifies business lift.
Problems Solved
- RAG prototypes hallucinate or fail under enterprise constraints
- Lack of orchestration between retrieval, reflection, and agent decisions
- Inability to measure accuracy, groundedness, or ROI
- Poor alignment between AI output and business outcomes
- Missing governance for privacy, bias, and auditability
Why Now
- RAG must evolve from prototypes to governed, production-grade systems
- Enterprises demand traceable, measurable, and self-improving AI
- Platforms like Salesforce Agentforce and Data Cloud now enable full-stack orchestration and grounding
- LangGraph and similar frameworks make multi-agent reasoning practical
Core Concepts
- Data Cloud Grounding: Use Customer 360 and predictive scores to anchor context.
- Agentforce Orchestration: Embed agentic flows that choose when and how to retrieve or act.
- LangGraph Patterns: Supervisor, Pub/Sub, and Router designs for dynamic control.
- Agentic RAG Patterns: retrieve/skip, reflect/re-query, and multi-hop retrieval loops.
- Evaluation Tooling: RAG Triad (context, groundedness, relevance), TruLens, and Ragas for continuous validation.
- Governance Framework: Privacy, bias mitigation, and fallback mechanisms.
- ROI Focus: Conversion lift, call deflection, and AHT reduction as measurable outcomes.
Agenda
Introduction: From RAG Demos to Enterprise Systems
Why most RAG initiatives fail beyond proof-of-concept.
The shift from static retrieval to agentic orchestration and measurable grounding.
Pattern 1: Grounding in Salesforce Data Cloud
How Data Cloud provides the factual substrate: traits, segments, and predictive scores.
Designing retrievers that query governed, unified customer data.
Pattern 2: Agentic Execution in Salesforce Agentforce
How agents in Agentforce can decide when to retrieve, re-evaluate, or escalate.
Sales, service, and checkout scenarios — driven by decision orchestration.
Pattern 3: Orchestration with LangGraph
Building reasoning loops using Supervisor, Pub/Sub, and Router nodes.
Designing agent workflows with multi-hop and reflection capabilities.
Pattern 4: Evaluation Frameworks & Tooling
RAG Triad metrics: context precision, groundedness, and relevance.
Hands-on tooling: TruLens, Ragas, and observability dashboards for factuality and fairness.
Integrating continuous evaluation into CI/CD for AI pipelines.
Pattern 5: Governance & Responsible AI
Embedding privacy, bias, and audit controls into the orchestration layer.
Designing fallback modes and traceability for compliant AI operations.
Pattern 6: Measuring ROI & Business Impact
Mapping RAG metrics to business KPIs:
- Service: Deflection rate, AHT reduction
- Sales: Conversion lift, deal velocity
- Marketing: Engagement uplift, cost per interaction
How to communicate ROI to leadership.
Demo: Smart Checkout Helper A self-correcting, grounded AI agent combining: - Data Cloud grounding
- Agentforce orchestration
- LangGraph reasoning
- RAG Triad evaluation
Live flow from retrieval → reasoning → measurement.
Wrap-Up & Discussion Key design principles for moving from demo to deployment. Checklist for Agentic RAG production readiness and ROI measurement.
Key Framework References
- Salesforce Agentforce: Multi-agent orchestration and decision workflows
- Salesforce Data Cloud: Unified traits, grounding layer, and predictive signals
- LangGraph: Graph-based orchestration patterns for reasoning and reflection
- RAG Triad, TruLens, Ragas: Evaluation and quality measurement frameworks
- NIST AI RMF / ISO 42001: Governance and explainability alignment
Takeaways
- End-to-end Agentic RAG Architecture Blueprint
- Evaluation Playbook for context, groundedness, and relevance
- ROI Measurement Framework linking AI metrics to business outcomes
- Governance Checklist for privacy, fairness, and resilience
- Ready-to-adapt Smart Checkout Helper reference design
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of AI & Data Architecture at Salesforce, where he focuses on enterprise AI, agentic systems, cloud-native architecture, distributed systems, data platforms, security, and large-scale transformation.
Over his career, Rohit has designed and led complex enterprise platforms across AWS, Google Cloud, microservices, real-time data, API ecosystems, resilient distributed systems, and AI-enabled architectures. His work increasingly focuses on the challenges enterprises face as software evolves from deterministic services to AI-native and agentic systems—particularly around reliability, governance, evidence, security, observability, cost, and safe autonomy.
Rohit is the author of System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems, published by Apress. The book presents a modern approach to system design covering scalability, distributed systems, AI architecture primitives, security, reliability, economics, agentic systems, and real-world architectures including e-commerce, ride sharing, payments, fraud detection, messaging, video streaming, file storage, and search. (Springer Link)
Book:
Amazon: https://a.co/d/09Zs1twa
Publisher / Springer Nature: https://link.springer.com/book/10.1007/979-8-8688-2782-2
O'Reilly: https://learning.oreilly.com/library/view/system-design-with/9798868827822/
Rohit is also an O’Reilly instructor and a frequent speaker at technology conferences including No Fluff Just Stuff, UberConf, GIDS, and other international events. His talks focus on practical architecture lessons from building and operating complex systems, including AI control planes, trusted agents, inference at scale, evidence-first RAG, AI security, distributed-system failure, and AI-era software architecture.
As a trusted advisor and architecture leader, Rohit works at the intersection of business strategy and deep technical architecture—helping teams translate complex business problems into scalable, resilient, secure, and economically sustainable systems.
Rohit holds an MBA in Corporate Entrepreneurship from Babson College and graduate-level education in Computer Science from Boston University and Harvard University.
Connect with Rohit:
LinkedIn: http://linkedin.com/in/rohit-bhardwaj-cloud
X / Twitter: @rbhardwaj1