Empowering Enterprise AI with Coveo: Guide to Agentic AI Systems and Key APIs

Are agentic AI systems just the latest technology trend — or the newest enterprise competitive differentiator?

Gartner estimates that agentic AI will resolve 80% of common customer service issues without human intervention by 2029. Yet a majority of enterprises linger in the wait-and-see phase, as 51% are still exploring the use of AI agents while 12% of their peers race ahead with deployments. 

Is your enterprise in that 51%? If so, you’ve likely got a lot of questions. What is this allegedly new technology, and how can enterprises harness it for optimal outcomes? How does it differ from generative AI, which flooded popular culture only three years ago?

In a recent master class, Juanita Olguin, Senior Director of Product Marketing, and Sébastien Paquet, VP of Machine Learning, decoded the mysteries behind agentic AI — unpacking not only how to identify the technology from similar offerings, but also best practices for building an adoption framework to key insights for evaluating and optimizing outcomes. 

This blog covers the most frequently asked questions from that masterclass, giving you the confidence to take your enterprise into the next phase. 

What Are Agentic AI Systems?

Agentic AI refers to AI systems capable of autonomous decision-making and action-taking to achieve specific goals. 

Unlike traditional automation, which follows predefined rules, agentic AI systems can handle unstructured data, reason through problems, and adjust their behavior based on feedback. This makes them particularly valuable in dynamic enterprise environments where flexibility and adaptability are crucial.

Human Search Interfaces and Agentic AI: Unifying Relevance Across Experiences

While traditional search interfaces are designed for human interaction — surfacing relevant results based on queries—agentic AI introduces a new layer of intelligence by enabling systems to autonomously perform searches and complete tasks on behalf of users.

This doesn’t replace human-facing search interfaces; it complements them. Together, they form a unified digital experience where:

• Human users benefit from intuitive, real-time search interfaces
• AI agents can retrieve and act on information autonomously across systems
• Both are powered by the same relevance layer, ensuring consistency across touchpoints

For example, a support agent might use Coveo’s search UI to resolve a ticket, while an AI assistant autonomously retrieves related documentation to update internal systems in the background. The result: seamless, context-aware relevance across every engagement channel.

What Are the Main Challenges in Implementing Agentic AI Systems?

Many organizations encounter challenges when piloting agentic AI due to gaps in technical and organizational maturity. 

One common scenario: a team tasked with digital transformation — such as a Director of Support Services or a Knowledge Management Lead — begins experimenting with prompting a language model like ChatGPT. Early results feel promising, and the team assumes they’re close to a production-ready solution. But as the initiative scales, it becomes clear that critical elements are missing: prompt consistency, retrieval grounding, quality evaluation, and a feedback loop to iterate safely.

Building the plane as it flies, while exciting, is rarely risk-free. Typical obstacles include:

• Inconsistent prompt design and lack of reusable prompt engineering patterns
  Without systematic approaches to designing, testing, and versioning prompts, organizations struggle to maintain response consistency across use cases and sessions.
• Lack of evaluation frameworks to measure retrieval accuracy, generative relevance, and real-world feedback
  Many teams lack the tools and metrics needed to validate the quality of retrieval-augmented responses, making it difficult to iterate with confidence.
• No mechanism for grounding responses with trusted, up-to-date enterprise data
  Without retrieval pipelines that connect LLMs to indexed content, models hallucinate or produce outdated information — undermining user trust and business value.

Coveo addresses these challenges by offering productized APIs that abstract complexity, strategic guidance from Professional Services, and modular deployment paths that encourage learning through iteration — without jeopardizing long-term maintainability.

What Technologies Power Agentic AI Systems?

Many organizations ask whether deploying agentic AI requires an “AI server” — a term that reflects the broader uncertainty around what infrastructure is actually needed. The good news: you don’t need a dedicated “AI server” on your premises to start building with Agentic AI.

Coveo makes it possible to deploy agentic AI across a variety of technical environments. Organizations can:

• Use cloud-native LLMs through providers like OpenAI, Anthropic, or Cohere
• Host their own models using on-premise setups or cloud services like Azure, AWS Bedrock, or Google Cloud
• Build intelligent workflows on top of SaaS tools or proprietary enterprise platforms, such as AgentForce at Salesforce, Copilot at Microsoft, Amazon Q, AWS Bedrock, and other emerging ecosystems.

This flexible ecosystem means you can tailor the architecture to your business needs — whether prioritizing speed, data sovereignty, regulatory compliance, or cost — while still gaining the strategic advantages of agentic intelligence.

What Are Best Practices for Creating Agentic AI Prompts?

Designing prompts for agentic AI systems is not about ad-hoc user inputs — it’s about carefully engineering system-driven prompts that power autonomous decision-making behind the scenes.

In agentic workflows, prompts must be robust, repeatable, and optimized for reliability across different tasks and contexts. These prompts are pre-set, dynamically assembled, and versioned as part of the solution architecture.

Best practices include:

• Modularize prompts into discrete task units: Break down complex objectives into simple, controllable steps that agents can execute reliably.
• Chain prompts intentionally: Design sequences where each step logically builds on previous outputs, supporting multi-step reasoning and action.
• Ground prompts with enterprise-approved knowledge: Use Coveo APIs like the Passage Retrieval API and Answer API to inject validated, access-controlled content into prompts, ensuring factual and contextual alignment.
• Continuously audit and version prompts: Monitor performance over time to detect drift or model changes, and maintain tight change control over prompt updates.

Building agentic prompts is about constructing reliable micro-behaviors that collectively achieve broader goals. A strong evaluation framework is essential — not only to validate agent outputs, but to ensure each prompting layer behaves as expected under diverse conditions.

Coveo’s Core Offerings for Agentic AI Systems

1. Search API (Lexical + Semantic Hybrid Retrieval)

At the heart of Coveo’s relevance platform is the Search API, which powers both traditional search and retrieval-augmented AI experiences. It combines high-performance lexical search with Coveo’s proprietary semantic embedding model (SE) to support intent-aware matching. This hybrid approach enables agents to retrieve relevant content even when the query uses different phrasing than the source documents.

The Search API also benefits from Automatic Relevance Tuning (ART), Coveo’s machine learning model that continuously learns from user behavior to re-rank results based on contextual signals like click-through rates, conversion, or case deflection. For agentic use cases, this means more precise, context-aware retrieval — whether for grounding generative responses or powering decision-making workflows.

2. Passage Retrieval API

Coveo’s Passage Retrieval API enables developers to extract specific, contextually relevant passages from documents stored in the Coveo index. This facilitates efficient retrieval of ranked text data, enhancing the performance of large language models (LLMs) and ensuring that AI-generated responses and downstream actions are based on accurate information.

Relevant reading: Agentforce & Beyond: Bringing Relevance to Every Touchpoint

3. Answer API

Currently in Beta, the Answer API is the headless, API-first version of Coveo’s Relevance Generative Answering. It delivers the same core value — concise, context-aware responses grounded in indexed enterprise content — but without a pre-built UI. This makes it ideal for embedding into custom applications, conversational interfaces, or agentic workflows where full control over orchestration and experience design is required.

By handling query interpretation, passage selection, and generative synthesis in one call, the Answer API offers a streamlined path to integrate RGA capabilities into environments beyond search results pages—extending Coveo’s trusted answer generation to wherever agents or copilots.

Relevant reading: Choosing the Right Retrieval Method for Your AI Project

Accelerate Agentic AI Solutions Development with Coveo

For decades, Coveo has developed a leading enterprise retrieval platform — enabling secure, scalable, and relevance-driven experiences across search, GenAI, and now, agentic AI.

These capabilities aren’t going away — they already deliver immense value and can be directly reused when moving toward agentic applications. You don’t need to build a new information retrieval layer from scratch to power AI agents. Instead, you can augment existing and future investments with the same unified relevance engine that supports Coveo’s search and GenAI experiences.

Organizations exploring agentic AI can benefit from:

• One platform to access enterprise knowledge: No need to duplicate content pipelines — agents can query the same unified index used for traditional search.
  
• Flexible integration paths: Use Coveo’s modular APIs — such as the Passage Retrieval API or Answer API—to power the retrieval layer for agent-driven workflows.
  
• Proven foundations for grounded, accurate outputs: Whether in search interfaces or AI pipelines, the same relevance engine ensures responses are consistent, contextual, and up-to-date.

This unified foundation enables enterprises to evolve incrementally toward agentic systems by leveraging existing infrastructure and best practices, while accelerating the development and deployment of knowledge-aware agentic AI solutions.

Is Relevance Generative Answering Agentic? Understanding the Terminology

Relevance Generative Answering is not agentic in itself, but it serves as a critical building block toward agentic AI. Agentic systems go beyond retrieval and synthesis — they involve planning, decision-making, and taking actions toward a goal. Relevance Generative Answering focuses on generating accurate, grounded responses based on enterprise content, but does not independently take actions or iterate toward outcomes. 

If you’re one of the previously mentioned 51% still exploring agentic AI, Relevance Generative Answering (or one of the many models available in Coveo’s ML suite) is a market-tested, enterprise-grade solution with proven value for companies like SAP Concur, Xero, Forcepoint, and many more. With a much lower implementation risk, enterprises can first adopt Relevance Generative Answering and use the benefits gained there to finance agentic projects. 

What Are Some Enterprise Agentic AI Use Cases?

Coveo’s suite empowers enterprises to implement Agentic AI across domains:

• Customer Support: Deploy intelligent agents that autonomously resolve inquiries, reducing response times.
• Internal Knowledge Management: Use PR API to enable employees to quickly surface relevant info.
• Marketing Automation: Leverage Answer API for real-time content personalization and multi-channel orchestration.
• Web Content Management: Autonomously generate, test, and optimize content using agent-driven systems.

Implementation Roadmap

To integrate agentic AI with Coveo:

1. Define Use Cases: Prioritize based on business value, focusing on where autonomous agents can reduce repetitive work or enhance decision-making.
2. Assess Infrastructure: Identify integration points for Coveo APIs, and determine where on-premises vs. cloud-hosted LLMs (OpenAI, Azure, Bedrock, Anthropic) make sense based on security, latency, and cost.
3. Clarify What Agentic AI Is: Distinguish between agentic systems (which act on goals with autonomy) and simple LLM integrations (which respond without autonomy). This will help align project goals with expectations.
4. Choose the Right Models for the Task: Use generalist models for open-ended queries and domain-tuned LLMs for specialized tasks. Incorporate secure retrieval (e.g., Passage Retrieval API) to keep generations grounded.
5. Pilot & Measure: Start with a narrow scope and clearly defined KPIs that span retrieval precision, generation quality, and user satisfaction.
6. Iterate & Scale: Use Coveo’s analytics and feedback to refine agent behaviors and broaden deployment to more channels or workflows.
7. Ensure Governance: Apply continuous evaluation frameworks, logging, and compliance policies to manage quality, bias, and access over time. 

Conclusion

Agentic AI is reshaping enterprise intelligence. With tools like RGA, PR API, and Answer API, Coveo offers the building blocks for scalable, secure, and context-aware automation. Whether through custom deployments or managed services, Coveo enables your enterprise to move beyond traditional search — and toward a future powered by intelligent, autonomous systems.

Ready to explore? Let’s build the future of enterprise AI — together. Learn how Coveo can help bring your enterprise agentic AI dreams to life. 

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