From response to action. AI agents that deliver results.
Chatbots simply answer questions. AI agents (Agentic AI) perform tasks in your ecosystem: planning steps, executing tools, and achieving goals. This is a new era of automation, where AI takes over the manual work of data integration, quality, and analysis from your team.
Sky
User-defined
Plan
Autonomous action plan
Execution
Agent runs tools
Score
Human Approved
We provide solutions based on technologies from world leaders
Gartner predicts that by 2026 40% of enterprise applications will be integrated with task-specific AI Agents - an increase from less than 5% in 2025.
(Gartner, “Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025”, 2025)
Agentic AI (Agent AI) is an artificial intelligence system that, unlike chatbots and conversational assistants, autonomously plans actions, launches tools, and performs complex end-to-end tasks in real IT systems. The agent receives a goal, selects the steps to achieve it, and executes them via API—in CRM, ERP, data, or documentation systems—stopping at key moments to obtain human approval (Human in the loop).
The chatbot asks "how can I help?" The agent asks "what goal do I need to achieve?" and independently achieves it.
How many manual steps will AI Agents eliminate?
10
20 hrs
AI agents can take over up to 75% of your manual tasks
150 h
freed-up man-hours of your team per month
Redefining process efficiency.
Traditional work (Chaos)
Agentic AI (Effect)
25 %
companies using GenAI will launch pilots or proof of concept of agentic AI in 2025, and by 2027 this percentage will increase to 50%.
Deloitte, “Autonomous generative AI agents”, 2025
80 %
Agentic AI will be able to solve typical customer service problems autonomously by 2029 - which is expected to lead to a 30% reduction in operating costs.
Gartner, “Gartner Predicts Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029”, 2025
79 %
organizations declare that AI agents are already implemented in their companies, and 88% plan to increase AI-related budgets in the next 12 months.
PwC, “AI agent survey”, 2025
Where do AI Agents give the fastest return?
Data Quality
The agent automatically translates business requirements into technical quality rules and independently monitors anomalies.
Integrations & API
The agent organizes the work around integration, eliminating the need to manually map fields between new systems.
MDM & Products
The agent supports the enrichment of product descriptions and attribute validation, drastically shortening Time-to-Market.
Technical Knowledge
The RAG agent searches the documentation, providing canned answers with direct quotes from company sources.
See how your digital employee thinks.
Full control and transparency. You can see every decision and every document used by the AI.
From concept to working Agent.
1. Discovery
We select 1 key process and define measurable KPIs of success.
2. Resource Map
We define the Agent's permissions – what it can "see" and in what systems it can operate (API).
3. Scenario Draft
We establish security frameworks, ethical principles, and checkpoints for AI.
4. PoC Implementation
We run the Agent in a safe test environment (Sandbox).
5. Production & Scaling
The agent takes over real tasks, and we add further automation scenarios.
"At Agentic AI, it's not about the computer talking to us, but about it working for us. We design agents that understand the context of your data and can autonomously deliver results within your unique IT architecture. This is the end of the era of simple chatbots."
Konrad Mokrzański
Presales Manager & Architect, Integral Solutions
Foundations: People – Processes – Technology
Three pillars of effective implementation of AI Agents in an organization.
Team
AI agents free humans from repetition. We're implementing Human-in-the-Loop: AI plans and executes, but you have the final say before data is recorded.
Procurement
We analyze decision-making processes and exception handling. The agent becomes an integral part of the team, with clear responsibilities and authority.
Technology
We connect advanced LLM models to your infrastructure via a secure API. Your data is protected and never used to train public models.
Why Data Is the Foundation of AI Agents' Success
15 %
of daily work decisions will be made autonomously by agentic AI by 2028 — up from 0% in 2024.
Gartner, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End 2027”, 2025
43 %
Data leaders point to data quality, completeness, and readiness as one of the main barriers to moving GenAI initiatives from pilot to production.
Informatica, “CDO Insights 2025”, 2025
Benefits for Roles
CTO/CIO
Reduction of operational debt and higher predictability of implementations in complex data ecosystems.
CDO / Head of Data
Rapid preparation of "AI-ready" data and automation of tedious quality maintenance processes.
Data Engineering
Shorter lead time in data pipelines and elimination of repetitive work when correcting errors.
Integration Architect
Full control over the flow of information and order in the integration architecture.
According to McKinsey, 88% of organizations already uses AI regularly in at least one business function. However, only 2% of companies have fully scaled implementation of AI agents.
McKinsey, “The State of AI: Global Survey 2025”, 2025 · Capgemini Research Institute, “Rise of agentic AI”, 2025
Gartner warns that over 40% of Agentic AI projects will be canceled by the end of 2027 due to rising costs, unclear business value or insufficient risk controls.
Gartner, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End 2027”, 2025
AI Agents vs Chatbots vs RPA
Focused on conversation, they generate text but cannot independently execute actions in external systems.
Rigid automation based on fixed rules breaks down at the slightest interface or data change.
They autonomously plan their path to their goals. They understand the business context, learn from mistakes, and independently select the right tools (APIs) for the task.
Criterion
Chatbot / GenAI
RPA
Agent AI (Agentic AI)
Action
Answers questions and generates content
Executes pre-designed steps (scripts)
Autonomously plans steps and achieves the goal
Understanding the context
Limited to conversation
None - works according to the rules
Complete - understands the business goal and selects the tools
Exception handling
Responds with a question or escalates
Stops, requires intervention
He tries to solve it himself, escalates when necessary
Integration with systems
Via API (limited)
Via UI scraping or API
Via API - native, multi-system
Learning
From training data (once)
No adaptation
Learns from results and feedback
Human supervision
Optional
Minimum (according to the rules)
Human-in-the-loop - human approves key steps
Best for
FAQ, Content Generation, Assistant
Repeatable, structured office processes
Complex data processes, integrations, MDM, data quality
Sectors in which we operate
Finances
Automatic transaction verification and KYC/AML process support.
Retail
Autonomous enrichment of product catalogs (Agentic PIM) and sentiment analysis of opinions.
Pharmacy
Search thousands of research documents and automatically check the quality of HCP data.
Industry
Monitoring production parameters and generating intelligent instructions for service technicians.
FAQ
ChatGPT generates text based on a prompt. The AI agent is a system that uses LLM models as the "brains" to operate your systems (e.g., SAP, Salesforce) and deliver results.
No. We implement approval gateways (Human-in-the-loop), where an Agent prepares a change proposal and a human approves it with a single click.
RAG stands for Retrieval-Augmented Generation. It allows the Agent to use only your current files and databases, ensuring the accuracy of responses and the security of your information.
No. AI agents communicate with your systems through existing APIs, acting as an additional intelligent orchestration layer.
AI agents are only as effective as the data and knowledge that powers them.
Customer 360 and MDM
Personalization of agent actions requires a complete, real-time customer profile. Customer 360 provides agents with context—history, preferences, and open cases—so that every interaction is relevant, not generic.
Application Integration and API Management
An AI agent can only be autonomous if it has access to the systems where business happens. iPaaS and API Management provide it with secure connections to CRM, ERP, and operating systems within defined permissions.
Data Governance and Data Quality
Without high-quality data, AI agents operate with confidence but lack precision. Data Governance ensures that the knowledge bases and records feeding the agents are up-to-date, consistent, and free from contradictions that lead to hallucinations.
Advanced Data Analytics
Insights from predictive models are only valuable if they translate into action. AI agents powered by Altair RapidMiner's predictions automate decisions—from dynamic pricing to churn prevention—without human intervention.
Applications and Low-code (Mendix)
Interfaces for AI agents – operator panels with agent activity overviews, human-in-the-loop tools, and automation monitoring dashboards – can be built and modified in Mendix quickly, without a full development cycle.
Knowledge Management and KMS Lighthouse
An AI agent provides precise answers only when it has access to a structured, up-to-date knowledge base. KMS Lighthouse is the foundation of the RAG architecture; without it, generative AI answers confidently, but often incorrectly.
199 billion USD
this is how much the global Agentic AI market will be worth in 2034 (growth from USD 5,25 billion in 2024 - CAGR 43,84%).
Agentic AI is the fastest growing segment of enterprise technology – overtaking cloud computing and mobile at this stage of adoption.
Globe Newswire / Grand View Research, 2025 · Gartner Top 10 Strategic Technology Trends, 2025
Ready to see how much work Agent AI can take off your plate?
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