What is agentic AI?

At a glance

• Agents plan, act, and self-correct — iteratively and directed to a goal.
• Unlike generative AI, the focus is not on producing output on demand, but on taking autonomous action toward a defined objective.
• Relevant to controlling for automated analysis cycles, proactive monitoring, and multi-stage reporting processes.
• Autonomy demands governance: traceability, data control, and defined human checkpoints are mandatory, not optional.

Definition and background

Agentic AI refers to AI systems that independently pursue goals, make decisions, and plan sequences of action — without requiring human instruction for each individual step.

Unlike conventional AI assistants that respond to prompts and execute discrete tasks, AI agents operate autonomously across multiple steps: they decompose complex objectives into sub-tasks, access tools and external data sources, evaluate intermediate results, and adjust their course as needed.

The term derives from the concept of agency — the ability to initiate actions and influence outcomes in the real world. In practice, this means that an AI agent is not limited to generating recommendations; it can actively carry out the steps required to achieve a defined objective within the boundaries set by its human operators.

How does agentic AI work?

At its core, agentic AI combines three capabilities:

• Planning: The system breaks a high-level goal down into a sequence of sub-tasks and determines a course of action.
• Tool use: The agent independently accesses tools such as databases, APIs, search engines, code execution environments, or other AI models.
• Feedback and adaptation: Intermediate results are evaluated; the agent revises its plan when outcomes do not meet expectations.

Technically, many agentic AI systems are built on large language models (LLMs) extended by planning frameworks. Multiple agents can operate in parallel, dividing labor across specialized roles — a configuration known as a multi-agent system.

What is an agentic AI framework?

An agentic AI framework is the technical and organizational infrastructure that makes AI agents capable of acting in the first place. It defines which tools an agent is permitted to use, the logic by which it plans, how it communicates with other agents, and where human checkpoints are built into the process.

Technical frameworks provide the building blocks with which developers configure, chain, and monitor agents. Typical components include:

• Orchestration: A master agent or planning component coordinates the sequence of sub-tasks and delegates to specialized sub-agents.
• Memory: Agents require memory — short-term for the current task context, long-term for recurring patterns and user preferences. Without structured memory, every agent run effectively starts from scratch.
• Tool registry: A central register of all tools agents are authorized to access — from database queries and calculations to external APIs.
• Guardrails: Rules and boundaries that prevent agents from executing unwanted actions, sharing sensitive data without authorization, or entering infinite loops.
• Logging and auditing: Every step taken by an agent is recorded — not only for debugging purposes, but as the foundation for traceability and compliance.

For use in controlling, the choice of framework is not a purely technical question. What matters is whether the organization’s governance requirements can be mapped onto the framework. An agent that autonomously accesses financial data and generates reports must provide a complete audit trail: which data was retrieved, when, and from which source.

What are the four types of agentic AI?

There is no uniform, officially established classification for agentic AI—various providers and analysts use different four-type models. The most widely used classification is based on the degree of autonomy, which distinguishes four levels: rule-based systems, workflow agents, semi-autonomous agents, and autonomous agents. As the degree of autonomy increases, the AI makes more decisions independently, while the amount of human oversight decreases.

What does agentic AI mean for controlling and business intelligence?

For controlling and business intelligence, agentic AI opens new possibilities — but also raises important questions.

Potentials

• Automated analysis: An agent can independently identify variances in financial data, investigate their causes, produce reports, and propose courses of action — without an analyst triggering each step manually.
• Synthesis of complex data landscapes: Agentic AI can combine structured and unstructured sources — ERP data, market reports, internal commentary — and consolidate them into a coherent situational picture.
• Proactive monitoring: Rather than waiting for queries, an agent continuously monitors defined KPIs and escalates autonomously when thresholds are breached.
• Scenario modeling: Agents can calculate multiple planning scenarios in parallel, assess their plausibility, and prioritize them according to defined criteria.

Limitations and risks

Autonomy is not an end in itself. Agentic AI can propagate errors across multiple steps — an incorrect intermediate result may compound unnoticed as the process continues. In controlling, where numbers form the basis for investment decisions, budget allocations, and operational steering, this carries significant weight. A human-in-the-loop provides the necessary safeguard.

There is also the question of traceability: when an agent conducts analyses and issues recommendations autonomously, the human decision-maker must be able to understand and take responsibility for the reasoning path. Explainable AI is not a convenience feature — it is a prerequisite for responsible deployment.

Agentic AI vs. Generative AI – What is the difference between Agentic AI and Generative AI?

Generative AI and Agentic AI are often mentioned in the same breath – yet they describe fundamentally different concepts that complement each other but do not replace one another.

In practice, Generative AI is often the engine that Agentic AI uses: an LLM handles language understanding, the formulation of intermediate results, and communication – while the agentic framework controls the process, coordinates tools, and organizes decision loops. Generative AI is therefore often a component of Agentic AI, but not the same thing.

Practical example: Agentic AI workflows in controlling

One application scenario for Agentic AI in controlling is the automated monitoring of monthly management reporting. Instead of a controller checking various metrics, data sources, and reports himself every morning, an AI agent monitors defined KPIs, detects anomalies, investigates possible causes, and prepares the relevant findings for the controller.

Suppose a company has defined a variance of more than 5% from the forecast for the current month's revenue as a critical threshold. The agent first checks the current actual data and identifies a significant variance. It then breaks the anomaly down by relevant dimensions – for example business unit, product group, region, or customer. In the next step, it examines which factors can explain the variance. Possible explanations include a decline in sales volume, price changes, a shifted product mix, or the absence of individual large orders.

The key difference from a classic automated analysis lies in the chaining of the analysis steps: the agent does not wait for a single analysis request but pursues the specified goal independently. It plans the necessary steps, uses available data and tools, evaluates intermediate results, and can adjust its analysis when new findings emerge. It is precisely this combination of planning, tool use, and feedback that characterizes Agentic AI.

For the controller, the result could look like this, for example: "Revenue currently 6.8% below forecast. 4.2 percentage points of the variance are attributable to the South region, of which 3.1 percentage points in turn to product group B. The main cause is a decline in volume among three major customers. The effect is partly offset by a positive price effect." An abstract variance thus becomes a prioritized analysis that can serve directly as a basis for further examination.

The distribution of roles is important here: the agent analyzes and prepares, the human decides. In controlling in particular, autonomy must not be equated with complete freedom of decision.

What are some real-world examples of agentic AI?

The use of AI agents is no longer an abstract vision. Productive use cases are already emerging across industries and functional domains:

What these examples share: the value is created not by automating individual steps, but by chaining multiple steps into a coherent, goal-directed process — with humans engaged at the points where judgment, accountability, or broader context are required.

Summary

Agentic AI marks a fundamental shift in the way AI systems are used: away from reactive tools that respond to inputs, toward autonomous agents that pursue goals, execute plans, and adapt in real time. For controlling and business intelligence, this presents real opportunities such as automated analytics, proactive monitoring, and scalable reporting as well as clear responsibilities: traceability, governance, and human oversight must be integrated from the very beginning. With DeltaMaster, Bissantz puts this balance into practice: AI-driven automation where it adds value, and human judgment where it is indispensable.