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When people talk about artificial intelligence (AI) today, the first thing that often comes to mind is generative models like ChatGPT, Anthropic, or Gemini. These so-called generative AI systems create content—whether text, images, or code—based on training data and user input. They're impressive, but above all, reactive.
AI agents, on the other hand, go a step further: they act autonomously, pursue goals, and can independently make and execute decisions over extended periods of time. Agentic AI systems don’t just respond to commands—they can derive their own intentions, handle new requirements, and take past actions into account, much like a human assistant.
The difference is fundamental: unlike generative AI, which creates content in response to input, agentic AI systems act proactively and adaptively. They use capabilities such as planning, contextual understanding, and tool integration to complete complex tasks with minimal human intervention.
Agentic AI is already being used across various business areas:
According to Garner, Agentic AI is expected to be the top trend in 2025, with more than 12% of all workplace decisions made by agents as early as 2028.
But for AI agents to effectively optimize processes, they need data. This often raises questions about data scope and, more importantly, data quality within the organization. Many companies still collect data in unstructured or inconsistent ways. In fact, only about 12% of data professionals trust their company's data enough to use it with generative AI, according to a 2024 survey by software firm Precisely.
Still, it’s possible to build systems that yield good results using existing enterprise data. This is where Retrieval-Augmented Generation (RAG) becomes essential. In the architecture of agentic AI, RAG systems play a central—and often indispensable—role when agents need to work with current, dynamic, and company-specific information.
Retrieval-Augmented Generation (RAG) is a concept where an AI system doesn’t rely solely on its internal language model (LLM) to respond, but actively fetches external information—e.g., from databases, knowledge documents, or CRM systems—to improve its answers.
In an agentic system, RAG modules are typically part of the tool layer and are actively used by agents to perform their tasks better. A typical workflow might look like this:
Possible RAG data sources include HubSpot campaigns, Salesforce records, internal knowledge bases (Wikis, Google Drive), Word and PPT files, Slack channels, email archives, or project management tools like Asana, Notion, or Jira.
A full-featured AI agent system, including RAG integration, typically consists of the following layers:
This architecture enables companies to effectively implement agentic AI systems and integrate them into existing processes. Its modular structure allows for gradual adoption and scaling based on business needs.
Using this architecture, Uhura was able to monitor legislative processes relevant to an association. The AI agent proactively provides updates on changes that could impact members. Additionally, employees can review and compare previous statements via ChatGPT interfaces to ensure consistency with more recent positions.
In most of these cases, we at Uhura Digital follow a planning and implementation roadmap that ensures agentic AI projects deliver results quickly and effectively. Agile prototyping and rapid learning and expansion of applications are essential components.
Phase 1: Strategic planning (Don’t overinvest in a complex “AI strategy” at the beginning—start with a prototype and learn quickly.)
Phase 2: Piloting
Phase 3: Scaling
There are now a variety of tools and SaaS services available that enable you to create your own systems and securely make internal data assets available as a knowledge base. A build-or-buy decision depends on individual circumstances but, more importantly, on long-term strategic considerations:
Build (in-house development):
Buy (off-the-shelf solutions):
The right decision depends on factors like company size, available resources, specific requirements, and overall strategic direction.
Agentic AI gives companies the opportunity to automate processes, improve efficiency, and relieve employee workloads. With a strategic implementation, businesses can gain competitive advantages and prepare for the future.
Whether you're a CMO, CFO, marketing manager, or CEO—you’ve likely already considered how AI agents could support your operations. Uhura can help you identify, design, and implement agentic AI solutions to increase business efficiency—get in touch.
You can learn more about our technology expertise and AI integrations on our Technology services page.