Key offerings
Identifying Use Cases
From POC to production support and the deployment of AI agents
Compliance & Governance for Agentic AI
Salesforce Agentic AI Consulting and Implementation
Agentic AI Development and Quality Assurance
AI Target Operating Model
Develop agents that work in business environments
You know that AI has potential—but you’re not sure where to start or which use cases for agentic AI deliver a measurable ROI.
We’ll help you understand:
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Key concepts of agent-based AI and how agents differ from workflows and agent-based systems
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Cross-industry examples
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Feasibility and ROI
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What’s possible today—and what isn’t
Your benefits with us
Our multidisciplinary team of more than 200 data and AI experts is your implementation partner for customized use cases in the field of artificial intelligence and generative AI focusing always on AI quality, safety and security.
Business meets Tech
AI is not just about innovation-it must align with your company’s requirements, IT infrastructure, and legal framework. Our cross-functional teams deliver solutions that work in practice and comply with regulations.
From idea to implementation
A concept on a slide deck is just the beginning. Our Product Owners bridge the gap between strategy and execution, transforming your vision into a functional AI product through precise and forward-thinking requirements management.
Outcome over output
Success isn’t measured by dashboards alone. Our AI engineers rigorously test models, while our process designers ensure seamless integration into your business workflows. Our goal: AI solutions that deliver real, measurable impact.
Legal guidance from day one
Ethical AI governance built into design and execution: Our Legal Engineers are involved from the very start to ensure compliance with the EU AI Act and data protection regulations. The result: sustainable implementation and user trust built from day one.
Let’s talk about Agentic AI in your organization!
At Diconium, we know that every AI journey is unique. That’s why we meet you exactly where you are-whether you're building the organizational foundations for Agentic AI, identifying value-creating use cases, or scaling AI across your entire company.
The question is not if agentic AI can create value, but where. Let's find out together.
Get in touch with us.
Jürgen Wohler
expert business development
Read more about Agentic AI
Our AI experts share their insights on the Diconium & applydata blog (Diconiums employee blog for Data & AI). Dive into their latest thoughts and perspectives.
Understanding Agentic AI Systems
AI agents handle simple tasks, while agentic AI systems set their own goals, plan across steps, and thus create new opportunities.
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AI Agents
AI agents are reshaping digital experiences – discover why every product and service will soon need its own API.
applydata: Agentic AI
Understand what Agentic AI is and how you can create value with intelligent systems.
Blog: Multi Agent AI with Salesforce
Read more in our blogpost about foundations, use cases & enterprise strategy with Salesforce.
FAQ
What is Agentic AI and how does Agentic AI differ from “classic” AI?
Agentic AI refers to a new generation of AI systems that don’t just respond to requests-they act autonomously, make decisions, and carry out complex tasks over extended periods. These systems are not merely reactive, but proactive and goal-driven-digital agents with the freedom to take action.
A traditional AI model might answer a question or generate text. An agentic AI system, on the other hand, can independently conduct research, analyze data, create a presentation, and even send it via email-all based on a higher-level objective.
Learn more in our blog post.
How does Agentic AI work?
Agentic AI systems pursue goals and act autonomously. Each agent has a clearly defined role and leverages knowledge from databases, tools, or APIs. Decisions are made based on natural language input, allowing agents to respond intelligently and contextually.
Through an orchestrator, each agent stays synchronized with others—ensuring seamless collaboration across the system. The result? Interactions that feel remarkably human.
How are context, memory, and feedback processed in agentic AI?
In agentic AI systems, context, memory, and feedback play a central role. They enable agents to act purposefully, adaptively, and over extended periods of time.
Context helps agentic AI make informed decisions. It includes the task description (e.g., goals, constraints, priorities), environmental information (e.g., current data, user interactions), and the history of previous actions and outcomes. Context is often dynamically updated and considered with each new decision.
Memory allows agents to retain and reuse information over time. Short-term memory includes the current session or task history, while long-term memory stores facts, user preferences, and past results. Technically, memory is often implemented using vector databases.
Feedback is used by agentic AI to improve and adapt. It processes explicit feedback (e.g., “This result wasn’t helpful”) and implicit feedback (e.g., click behavior, success metrics). Agents can evaluate their own outputs and replan accordingly—this is known as self-evaluation. Feedback is used to adjust strategies, correct errors, or select new tools.