Just three years after generative AI grabbed the world’s attention with ChatGPT’s mainstream debut, agentic AI is now making waves across enterprise boardrooms. According to KPMG, 51% of companies are considering implementing agentic AI systems, though only about 12% have deployed them.
Many companies struggle to understand the fundamental differences between agentic and generative AI approaches — and why they might need one versus the other.
The confusion is compounded by a sudden influx of new agentic AI systems entering the market, with promises not necessarily living up to the hype. Vendors are slapping “agentic” labels on everything from simple chatbots to complex automation workflows, creating what industry experts call “agent washing.” Meanwhile, executives face mounting pressure to deploy AI solutions while grappling with questions about foundational readiness, security concerns, and ROI.
It’s true that generative AI models and agentic AI solutions each offer distinct value propositions for different enterprise needs, but you don’t have to choose one type of AI technology over another. Both offer value, though you’ll hit a wall with an agentic AI system unless you have solid foundational data management and security protocols in place.
The type of artificial intelligence technology itself isn’t the bottleneck. It’s whether your organization has the basic infrastructure to support the complexity of introducing agentic AI into an enterprise setting.
Differences Between Generative AI vs Agentic AI
Gen AI is a reactive system. It uses a large language model (LLM) with a static execution plan with predefined steps to create content or perform a complex task. It can compile information, respond to prompts with natural language answers, and generate all images, videos, and code.
An agentic AI model is autonomous in that it can make decisions without human intervention. It uses intent-driven, adaptive execution to take that next step (or multiple next steps) for you. An agentic workflow processes information, but it also sets goals, makes decisions, and acts to achieve a specific goal. With an agentic system, you don’t have to specify exactly what you want it to do. Instead, you specify what you want your agents to achieve. These autonomous agents then figure out a plan to get there.
Generative AI Explainer
Generative AI tools use large language models to understand and respond to user queries. These systems work by analyzing massive amounts of training data to learn patterns in language, then use that knowledge to generate relevant responses. Generative AI excels at taking existing information and recombining it in new ways. GenAI systems create “new” content (in the sense that it’s not a copy of its training data) to answer questions, create images and video, or even write code.
The underlying large language model acts as the brain that processes your input and figures out the most appropriate response based on what it learned during training. This technology has become the foundation for many conversational AI applications we see today, from chatbots to virtual assistants.
A notorious downside of GenAI is that it “hallucinates” —that is, it makes up information that’s misleading or outright false. You need guardrails in place to avoid this in enterprise situations. A recent viral example of this is a 2025 summer reading list. The list was published in multiple newspapers including the Chicago Sun-Times and The Philadelphia Inquirer. Ten of the 15 books included in the list don’t exist. In this case, human oversight could’ve helped avert this embarrassing disaster.
As the newspapers that published this reading list can attest, producing convincing but false information can be incredibly damaging to a company’s reputation. That’s why enterprises need safeguards like retrieval-augmented generation (RAG) to ground AI responses in verified company data rather than relying solely on the model’s training.
Relevant reading: Putting the ‘R’ in RAG: How Advanced Data Retrieval Turns GenAI Into Enterprise Ready Experiences
Agentic AI Explainer
Agentic AI systems can make independent decisions without human input. Autonomous decision-making applications, or agentic AI applications, can perceive their environment, reason about complex problems, act through various tools and integrations, and learn from each interaction to improve over time.
The main difference between agentic AI vs generative AI is autonomy, with the former able to break down complex objectives into multiple steps and adapt their approach based on real-time results. Real-world agentic applications are already transforming business operations.
Customer service agents at companies like Bosch Power Tools use agentic AI to automatically access multiple systems, translate documents, and handle complex multi-step support cases.
Research and document processing represent another major case study for agentic AI capabilities. AI agents can search across dozens of repositories, synthesize information, and generate comprehensive reports, all while making autonomous decisions based on a given objective. Marketing firms are using agents for automation to speed up previously time-consuming tasks like proposal creation. What once took seven days or more, can be reduced to a few hours.
Digital Transformation Requires a Phased Approach
If you’re chasing the latest AI technology without addressing the foundational issues that ultimately determine success or failure, then you’re doing AI wrong.
Enterprises operate in what can only be described as “acronym soup” — a chaotic mix of ERPs, CRMs, CMSs, and dozens of other siloed systems. Different teams make purchasing decisions independently, creating fragmented data landscapes that challenge even the most sophisticated AI system. But your end users don’t care about your internal complexity. They just want to find what they want when they want it.
The problem gets worse when you consider that companies have been making siloed technology investments for years, each promising to improve efficiency and productivity. But without unified data management, these systems create more problems than they solve.
To do digital transformation right, particularly when it comes to agentic AI systems, take a phased approach that includes:
Phase 1: Build Your Data Foundation
Both generative and agentic AI need clean, well-organized data to function effectively.
One way to provide this information is with an agnostic search platform that can index and retrieve relevant information from wherever it lives, without requiring costly data migrations. After all, it’s extremely unrealistic to expect teams who own individual systems to give up the solutions that work for them and their mandates — instead, leverage a platform that connects content instead of forcing your enterprise to choose just one storage system.
With a single, unified index, content can be surfaced consistently and coherently across your digital journey — especially if you choose a platform that incorporates early-stage binding to reinforce security and permissions at the document level.
Phase 2: Establish Search Infrastructure
Once you have your foundation in place, you’ll need architecture that helps serve relevant content to the right people at the right time. Retrieval is often the most difficult part of RAG methods, because no amount of augmenting or generating is going to rectify working from the wrong information.
Enterprise-grade search that leverages AI can make sense of huge swathes of information, parsing out the right document or snippet to answer a prompt or query. The search platform you choose should evaluate information not only by robust search techniques like term frequency across the corpus, but also by searcher behavior. This provides greater relevance into what ‘successful’ content looks like for each individual person, especially at scale.
If you’re thinking about adopting a vector database, consider why a search engine might be a better choice. Check out our blog, Why Search Engines Outperform Vector Databases for RAG.
Phase 3: Start With Generative AI
Once you have data connectivity and search infrastructure in place, you have the foundation to implement generative AI systems that are more reactive and easily controllable. Coveo customers see 20-30% case deflection improvements from GenAI.
This strong foundation can be reinvested in developing a more complex AI agent or agentic system. Think generative answering for customer service, AI-powered search results, and content recommendations. These applications give you immediate ROI while building organizational confidence in AI capabilities.
Phase 4: Scale to Agentic Applications
Once the above phases are in place, you can iterate quickly on agentic systems. Building your first agent using existing data infrastructure can be done in just a few days. Start simple by choosing one specific use case, control the prompts carefully, and ensure proper guardrails are in place. Focus on simplicity.
Phase 5: Monitor Your Agents Closely
Establish robust evaluation and monitoring systems, as you’ll need significant effort to maintain control over autonomous systems. Avoid the temptation to start with multi agent system orchestrators. Get good with one agent before attempting multiple agents interacting together.
Make Smart AI Choices For Your Organization
Trying to figure out if you should use agentic AI vs generative AI isn’t really the issue here. The real question centers on timing and foundation. If you want to build effective agentic AI systems, you need to establish the right enterprise foundation first.
Skipping this foundational work slows down the entire process since security and compliance teams eventually block deployment when systems can’t meet enterprise standards. Success starts with identifying actual business needs rather than chasing the latest technology trends. Not every workflow requires the complexity of autonomous agents. Simple, predictable processes work well with generative AI solutions. Save agentic AI for complex, multi-step tasks that demand reasoning capabilities, such as research agents that synthesize information from multiple sources.
The underlying foundation determines whether a given AI technology or AI solution will work efficiently in your environment. Get your data connectivity and search infrastructure right first, then layer in proven generative AI applications. Only after mastering these controlled, predictable systems should teams move toward the less predictable world of autonomous agents.
Watch our Agentic AI Masterclass for more info or contact us to talk with a Coveo AI expert to build a roadmap for your enterprise.