What is agentic AI?
Agentic AI refers to AI systems that are capable of autonomously pursuing goals, developing multi-step plans, and executing tasks with minimal human intervention. Rather than responding only to individual prompts, these systems can reason about objectives, make decisions, use external tools, and coordinate multiple actions to achieve a desired outcome. They continuously evaluate progress and adjust their approach when conditions change, while remaining aligned with human-defined goals and constraints.
| Attribute | Details |
| Category | Artificial Intelligence / Automation |
| Application | Autonomous task execution, multi-step process automation, proactive monitoring |
| Typical Use Cases | Controlling, financial planning, forecasting, IT operations, customer service |
| Related Terms | Generative AI, Large Language Models (LLMs), Multi-Agent Systems, Human-in-the-Loop, Explainable AI |
| Key Benefits | Automation of complex analytical workflows, proactive anomaly detection, scalable intelligence |
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 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?
Generative AI and agentic AI are frequently mentioned together, yet they describe fundamentally different concepts. They complement each other — but are not interchangeable.
| Feature | Generative AI | Agentic AI |
| Core principle | Generate on demand | Act toward a goal |
| Interaction model | Prompt → Output | Goal → Planning cycle → Result |
| Time horizon | Single step | Multi-step, iterative |
| Tool use | Limited or none | Extensive, dynamic |
| Degree of autonomy | Reactive | Independent and adaptive |
| Typical risk profile | Low | Variable, depending on configuration |
In practice, generative AI frequently serves as the engine that agentic AI employs: an LLM handles language understanding, formulates intermediate outputs, and manages communication — while the agentic framework controls the workflow, coordinates tools, and organizes decision loops. Generative AI is thus often a component of agentic AI, but not the same thing.
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:
Controlling and finance: An agent monitors the reporting data model daily, identifies data gaps and outliers, investigates possible causes in upstream and downstream systems, and produces a summary for the controller.
Financial planning and forecasting: A planning agent retrieves current actuals, compares them against the budget, automatically calculates rolling forecasts based on defined rules and scenario assumptions, and presents prioritized action options.
Software development: AI agents take on sub-tasks in the development process — analyzing error reports, writing code, running tests, and proposing pull requests.
Customer service: Agents handle incoming requests end to end: reading the inquiry, researching internal knowledge bases and CRM systems, drafting a response, and escalating to a human agent when needed — with a complete conversation log.
Market and competitive intelligence: A research agent continuously monitors defined sources such as news portals, company reports, and industry publications, condenses relevant information, and generates periodic briefings for management.
IT operations: Agents monitor system states, detect anomalies, execute initial diagnostic steps, and automatically initiate countermeasures — such as restarting services or escalating to the responsible team.
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.
Agentic AI at Bissantz
Bissantz has spent decades working on a fundamental question: how do people genuinely understand numbers — and how can software help?
DeltaMaster, Bissantz’s BI and analytics platform, applies AI-assisted automation and agentic AI workflows to accelerate controlling processes: data is consolidated automatically, anomalies are detected and contextualized automatically, and findings are translated into actionable recommendations. The human retains control over interpretation and decision-making. Automation where it creates value; human judgment where it is irreplaceable.
FAQ – frequently asked questions
Agentic AI refers to AI systems that autonomously pursue goals, make decisions, and plan multi-step sequences of action — without requiring human instruction at every step
Generative AI generates content in response to a prompt. Agentic AI acts toward a defined goal across multiple steps, using tools and adapting its course based on intermediate results. Generative AI is often a component of agentic AI systems.
An AI agent is a software system that perceives its environment, makes decisions, and executes actions autonomously — typically to achieve a specific goal over multiple steps.
Safety depends on the governance design. Well-configured systems with guardrails, human checkpoints, and complete audit trails can be deployed responsibly. Autonomous systems without these controls carry significant risk.
Agentic AI can automate variance analysis, monitor KPIs proactively, generate reports, and calculate planning scenarios — enabling controllers to focus on interpretation, judgment, and decision-making.
Summary
Agentic AI marks a fundamental shift in how AI systems are deployed: from reactive tools that respond to prompts, to autonomous agents that pursue goals, execute plans, and adapt in real time. For controlling and business intelligence, this creates genuine opportunities — automated analysis, proactive monitoring, and scalable reporting — alongside clear responsibilities: traceability, governance, and human oversight must be built in from the start. With DeltaMaster, Bissantz brings this balance to life: AI-driven automation where it adds value, human judgment where it is indispensable.
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