// INTEGRATION & AUTOMATION
AI Workflows and Agentic AI:
When Software Makes Decisions
Classic automation follows rigid if-then rules and breaks down as soon as a format changes. An AI workflow with agentic AI understands the intent behind a task, plans the necessary steps itself, and executes them across system boundaries. The shift is fundamental: away from assistants steered by humans, toward agents that orchestrate entire processes independently.
// DEVELOPING PRODUCTION-READY AI SOLUTIONS FOR
What are AI agents and where do they take work off your team?
AI agents are software systems that do not just react to inputs but actively pursue goals. They use LLMs for reasoning, break complex tasks into sub-steps, and use external tools (browser, API, email) to execute them. In practice they show up in three roles.
01
The Researcher
Observes and compares
Independently researches market data, competitor pricing, supplier terms, or regulatory changes. Searches websites, reads PDFs, and consolidates findings into a decision-ready report. Unlike a human, it works around the clock and on multiple sources in parallel.
02
The Operator
Acts in the system
Executes concrete actions in ERP, CRM, or shop systems. Checks stock levels, creates purchase requisitions, updates customer records, or routes cases to the responsible person. Critical actions such as an actual order or a money transfer require human sign-off.
03
The Analyst
Monitors KPIs
Watches operational metrics, detects anomalies, and reports them in context. Instead of a bare alert email, it delivers a short analysis: what happened, which root causes are possible, what options exist. This drastically shortens the time between detection and decision.
How an AI agent thinks and acts
Behind every agent action sits a recurring loop. Four steps that turn a software system into a capable actor.
01
Step 01
Perception
The agent perceives input: an incoming email, a new record in the ERP, an anomaly in monitoring. Unlike a classic script, it is not locked to one specific format. Even if input arrives unstructured, as a PDF or free-text message, the agent can make sense of it.
02
Step 02
Reasoning
Based on the input, the agent builds a plan. Example: "To process this order request I need to first check stock, then price, then lead time, then prepare the order." The plan is not rigid. It can be adjusted if an intermediate step fails unexpectedly.
03
Step 03
Action
The agent uses tools to execute the plan. Tools here are APIs, database access, browser actions, emails, or function calls into your existing systems. The agent selects the right tool per sub-step, uses it, and waits for the result.
04
Step 04
Observation
After every action the agent checks: did it work? Is the result correct? If not, it adjusts the plan and tries an alternative. This makes agents robust against minor disruptions that would immediately crash a rigid RPA script.
RPA vs. agentic AI: the difference in practice
RPA is for hands, AI agents are for minds. Both have their place but solve different problems. The table shows when each approach fits.
Attribute
RPA (the robot)
AI Agent (the employee)
Flexibility
Breaks when the format changes
Adapts and plans alternative paths
Task type
Repetitive, predictable, structured
Complex, unstructured, decision logic
Input
Structured data (Excel, defined forms)
Emails, chats, images, PDFs, unstructured sources
Decision
Follows a fixed if-then rule
Weighs options, chooses based on context
Maintenance
High (with every format change)
Low (agent compensates for minor changes)
Governance and Control: Autonomy Requires Guardrails
The biggest worry with autonomous agents is: will the agent do something stupid? At Xanevo, every agent runs in a defined sandbox with clear permissions. It has access to exactly the systems needed for its task and nothing beyond that. Critical actions like orders, contracts, or financial transactions require human confirmation (human-in-the-loop). Every agent action is logged and auditable.
Specialized Topics for Workflow Architects
Anyone serious about establishing agentic AI in their company will quickly run into special topics. Multi-step pipelines connect several agents into a system that can cover more complex processes than a single agent ever could.
// AI WORKFLOWS
Where could agents
take work off your team?
In 30 minutes we map your recurring processes together and identify the two or three use cases with the highest agentic potential. Concrete, with effort and impact per use case. You decide afterward whether a pilot makes sense.