Agentic AI: From Co-Pilot to Autopilot. The Next AI Update for the World of Work, Processes, and Even Marketing

26.6.2025

What are AI agents — and how do they differ from generative AI?

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This article was written by:

Kai Wermer

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.

Use Cases: Where agentic AI is already in action

Agentic AI is already being used across various business areas:

  • Marketing: Automated campaign optimization, audience analysis, content creation, localization, and more.
  • Customer Service: Handling standard inquiries, escalating complex issues, onboarding customers, etc.

  • Sales: Lead qualification, personalized outreach, automated quote generation, etc.

  • Finance: Automated reporting, anomaly detection, and more.

  • Human Resources: Recruitment, onboarding, travel planning, and expense management.

  • Industry: Maintenance and repair of machinery (predictive maintenance), managing technical documents to identify relevant specs or certifications, generating safety data sheets (SDS) and multilingual labels.

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.

What is RAG, anyway?

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:

  1. An agent is tasked with creating a status report on ongoing marketing campaigns.

  2. It queries an internal RAG system, which searches CRM data, analytics platforms, and previous reports.

  3. The retrieved content is matched to the goal (report generation) and processed into a finished output via an LLM.

  4. The agent reviews the result, adjusts if needed, and automatically sends it to relevant stakeholders.

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.

The architecture for AI agent systems — what does it look like?

A full-featured AI agent system, including RAG integration, typically consists of the following layers:

1. User Interfaces (Input Layer)
  • Channels: Web apps, mobile apps, chatbots, voice assistants

  • Purpose: Collect and transmit user input; may include sensor data


2. Orchestration Layer
  • Agent Orchestrator: Coordinates multiple agents; allows different models per task

  • Workflow Engine: Controls sequences, conditions, goal tracking

  • Role-Based Logic: Determines which agent acts, when, and why


3. Agent Layer
  • Planning Agent: Drafts strategic action paths.

  • Execution Agent: Carries out steps like data retrieval or report generation

  • Reflection Agent: Evaluates results, learns, and adjusts strategy


4. RAG Component (Retrieval-Augmented Generation)
  • RAG Retriever: Searches structured/unstructured sources

  • Retriever Indexing: Uses vector databases (e.g., FAISS, Weaviate) or semantic search

  • Dynamic Prompting: Merges real-time info with user queries

  • Context Transfer: Supplies relevant info to the LLM for processing

RAG acts as a bridge between agents, knowledge sources, and the LLM. It can:
  • Be directly accessed by agents (e.g., "Find info on Project X")
  • Automatically trigger when knowledge gaps arise
  • Connect to databases, CRM systems, CMS, and other internal tools

5. Tool & Action Layer
  • API Connectors: HubSpot, Salesforce, project management platforms

  • Web Tools: Automated research, API calls, form filling, scraping

  • Data Access: Company databases, shared drives, internal knowledge bases


6. Storage & Learning Layer
  • Short-term memory (Session Context): Context for current interactions

  • Long-term memory: Stores agent experiences, logs, and outcomes

  • Feedback Loops: Used to refine agent behavior over time


7. Security & Governance Layer
  • Access Control: Granular user/data/tool permissions

  • Audit Logs: Track and trace agent actions and decisions

  • Compliance Layer: GDPR compliance, data classification, access ethics

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.

Specific use case from associations and industry:


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.

Implementation roadmap: From idea to execution


Phase 1: Strategic planning (Don’t overinvest in a complex “AI strategy” at the beginning—start with a prototype and learn quickly.)

  • But still — Goal Definition: Clearly define which processes should be supported or automated by agentic AI. These must first be identified—which is often not easy.

  • Stakeholder Engagement: Involve relevant departments (e.g., IT, sales, marketing, legal) early to address requirements. Avoid overcomplicating things. Don’t pressure teams unnecessarily, and simplify overly complex requirements.

Phase 2: Piloting

  • Use Case Selection: Identify a suitable pilot project with measurable benefits. It’s fine to start with simple use cases.

  • Data Preparation: Ensure the necessary data is available and of high quality.

  • Development and Testing: Build the agent, integrate it into existing systems, and test for functionality. Select and train internal “ambassadors” to support testing and later help roll out the solution across the company.

Phase 3: Scaling

  • Evaluation: Assess the pilot in terms of efficiency gains, user acceptance, and ROI.

  • Rollout: Gradually expand to additional processes or departments, supported by training and change management.

  • Monitoring and Optimization: Continuously monitor agent performance and adjust as requirements evolve.

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 vs. Buy: Decision criteria for AI agent systems


Build (in-house development):

  • Benefits: High adaptability and full control over data and processes.

  • Challenges: Requires specialized expertise, longer development timelines, and higher initial investment.

Buy (off-the-shelf solutions):

  • Benefits: Faster deployment, lower resource demands, vendor support.

  • Challenges: Limited customization, vendor dependency, potential integration issues, and licensing costs that can scale rapidly. There’s also the risk of vendor lock-in, which can make switching solutions more difficult in the future.

The right decision depends on factors like company size, available resources, specific requirements, and overall strategic direction.

Conclusion: Agentic AI as a strategic enabler


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.