Search Fuels the Agentic AI Age: Is Your Business Ready?

As artificial intelligence continues to redefine how people discover, evaluate, and buy, one thing has become clear: search is the engine that powers agentic AI. In a recent Lucidworks-hosted webinar, “Search Fuels the Agentic AI Age: Is Your Business Ready?”, Keri Rich, VP of Product Management at Lucidworks, joined digital leaders Phane Mane of Boston Scientific, Mike Calahan of Dawn Foods, and Jochebed Zakrzewski of Matric Marine to explore how retrieval-driven intelligence is transforming commerce, customer experiences, and enterprise operations.

The Age of Agentic AI Has Arrived

Lucidworks’ head of product, Keri Rich, opened by underscoring how deeply the rise of agentic AI is changing digital behavior. “The way people shop is fundamentally changing,” she said, citing a Lucidworks-sponsored study in which 85% of business leaders believe some websites will become obsolete as AI agents handle more discovery and purchasing tasks. Nearly every respondent—98%—agreed their brand must be discoverable by AI.

Consumers now search, compare, and even transact through AI-powered tools such as ChatGPT, Perplexity, and Claude. Retailers saw chatbot usage rise 42% during the last holiday season, yet product returns also jumped from 20% to 28%, underscoring the cost of poor retrieval and misaligned recommendations. “Businesses will need to control for hallucination and ensure AI systems recommend relevant content wherever the customer is,” Rich warned.

Retrieval: The Foundation of Trust and Accuracy

Throughout the conversation, Rich emphasized the retrieval layer, or “R,”, often the most important part of RAG (retrieval-augmented generation), as the most critical factor for accurate, trustworthy AI. Without robust retrieval, large language models are forced to generate from incomplete or irrelevant data, risking misinformation and poor user experiences.

Rich explained that effective retrieval depends on strong indexing, context control, and hybrid search, combining traditional search metrics like precision and recall with modern embedding-based models. Lucidworks’ approach uses advanced semantic chunking to send only hyper-relevant context to the language model, optimizing both cost and accuracy. “Treat your LLM like it’s another searcher in your system,” Rich advised. “Feed it only what it needs to give users precise, grounded answers.”

Enterprise-Grade AI: Scale, Governance, and Security

When asked what “enterprise-grade AI” really means, Rich outlined a vision rooted in trust, scalability, and governance. Enterprise data, she noted, is inherently complex—layered with permissions, irrelevant noise, and constantly changing structures. A truly enterprise-grade system must manage data ingestion, enrichment, retrieval, and output governance seamlessly across billions of documents or millions of SKUs.

For commerce platforms, that also means delivering low-latency, consistent experiences to global audiences in multiple languages while maintaining real-time catalog updates. Security and compliance are equally vital: “AI must enforce access controls and protect sensitive data while maintaining transparency, auditability, and policy controls,” Rich said. “Grounding and attribution are non-negotiable if you want users and regulators to trust your system.”

Orchestration and the Rise of Specialized Agents

The panelists agreed that orchestration—how multiple models or agents collaborate—is emerging as the next big challenge. Rich echoed this, advocating for multi-agent specialization over monolithic “one-model-does-everything” approaches. Drawing on studies like Amazon’s, she noted that focused agents consistently outperform general ones because they execute narrow tasks with higher precision and lower cost. “The more specialized the agent, the more likely you are to achieve positive business results,” she said.

Effective orchestration, Rich added, depends on a control layer that keeps shared context and governance consistent across agents. “You don’t need one mega-agent,” she explained. “You need orchestration that routes requests to the right specialist at the right time.”

From Experimentation to ROI

While hype around AI investment continues to grow, Rich reminded leaders to ground innovation in measurable outcomes. Establish key performance indicators early—such as MRR, precision, recall, and context window utilization—and build platforms that allow for experimentation. “Your first implementation might not deliver ROI,” she noted. “But the ability to iterate quickly and safely is what drives long-term success.”

Real-world case studies already show the payoff. Comcast’s “Ask Me Anything” agent, powered by large-language-model search, cut call-handling time by 10%, saving millions annually. Wayfair achieved similar reductions in customer-service handle time, validating how retrieval-driven AI can deliver measurable business impact today.

Search as the Core of Agentic Intelligence

As AI agents increasingly handle the work of discovery and evaluation, search is becoming the connective tissue that ties together knowledge, context, and personalization. Rich’s message to enterprises was clear: success in the agentic AI era depends on owning your retrieval layer and governing your data pipeline. “Search is far more than a box on your website,” she concluded. “It’s now the critical foundation of how AI understands your business and connects your customers to what they need.”

Key Takeaways

1. Retrieval Drives Reliability – The “R” in RAG is critical. Strong retrieval and indexing ensure AI responses are accurate, relevant, and grounded in truth.
2. Enterprise AI Requires Governance – Security, permissions, and auditability must be built in from the start for compliance and trust.
3. Specialization Wins – Focused, task-specific agents consistently deliver higher ROI and lower costs than general-purpose models.
4. Search Powers the Agentic Age – From personalization to orchestration, intelligent search is the backbone of every successful AI experience.

Agentic AI and Search: Common Questions Answered

Question / Topic	Expert Insight	Key Points	Example / Impact
What is agentic AI and why does it matter?	Keri Rich, VP Product, Lucidworks	Agentic AI changes how people discover, evaluate, and buy online. Search is the retrieval engine that makes agentic systems accurate and trustworthy.	85% of leaders predict websites will become obsolete; 98% say brand discoverability by AI is critical.
Why is retrieval (“R” in RAG) essential?	Rich & Fanny Man, Boston Scientific	Retrieval grounds LLMs in truth, reducing hallucinations. It ensures only the most relevant, contextual data powers AI responses.	Retrieval from trusted sources (catalogs, contracts, docs) boosts accuracy, lowers model costs.
What defines enterprise-grade AI?	Rich, Lucidworks	Enterprise AI must handle scale, governance, and data security. It requires contextual chunking, access controls, and auditability.	Billions of documents, millions of SKUs — Lucidworks systems maintain context, speed, and compliance.
How can orchestration improve performance?	Panel Consensus	Multi-agent orchestration allows specialized agents (search, pricing, fraud, fulfillment) to work together efficiently.	Lowers latency and costs vs. a single “mega-agent.” Supports composable commerce strategies.
What metrics prove AI retrieval success?	Rich, Lucidworks	Use traditional and AI-specific metrics: MRR, precision, recall, and context-window utilization.	Ensures AI only uses relevant context, improving response accuracy and ROI.
Where are companies seeing real ROI?	Rich & Mike Callahan, Dawn Foods	LLM-powered search improves customer service and efficiency.	Comcast’s Ask Me Anything tool cut handle time 10%, saving millions; Wayfair saw similar gains.
How should companies start?	Panel Consensus	Start small, define KPIs, experiment safely, and treat AI as an enterprise system.	A/B test AI agents vs. traditional search; measure engagement, conversions, and cost savings.