Agentic AI: Why 2025 is the year of autonomous AI agents. A reality check

AI agents are THE hot topic for 2025. Everywhere you look, you hear about autonomous AI systems that perform tasks independently, make decisions and orchestrate complex workflows. Google, Salesforce, Microsoft and many others are integrating ‘agent-based’ features into their enterprise tools. Startups are building business models based on agents. And on LinkedIn, demo videos of impressive prototypes are generating ‘next-level FOMO’ that triggers the impulse: ‘I must have that too!’

But there is a huge gap between the ‘incredible’ demos and the harsh reality of production. Many ‘agents’ are really just workflows with LLM nodes. The promised magic turns out to be a combination of structured JSON output, deterministic code and targeted control.

Does this mean that the agent hype is unjustified? Not at all. For certain use cases, we have indeed reached the point where trust in reliability and expected business value suggest the productive use of agents. The key lies in separating illusion from reality and understanding what agents really are – and what they are not.

In this blog post, I will look behind the hype and offer you a reality check. I will clarify terms, provide an understanding of important basic principles of agents, and look at practical aspects of implementation. In doing so, I will show you everything you need to consider in order to bridge the gap between impressive demos and reliable production systems. Because even if the hype surrounding agents is almost annoying, the potential of agentic solutions is too great to ignore.

What exactly is an agent? And what is it not?

If you ask ten developers for their definition of an AI agent, you will get twelve different answers. This conceptual confusion is no accident – it reflects the rapid evolution of the field. And the need for greater clarity. The term ‘agent’ is used inflationarily and often incorrectly. Many systems referred to as ‘agents’ are in fact only modular software components with LLM integration.A practical example: A workflow that extracts orders from emails and creates them in SAP uses LLMs for text processing – but it is not an agent. It is a process automation with defined steps. Valuable? Absolutely. An agent? No.

So what makes a real agent? A pragmatic definition describes agents as autonomous software components that use tools to achieve goals without each individual step being explicitly specified. This involves two key criteria:

Autonomy

The agent's ability to make decisions about the actions to be performed based on context and reflection on its own actions.

A concrete example: A coding agent is given the task of ‘Implement an API for user management.’ It independently analyses the requirements, selects the appropriate framework, creates the database structure, implements authentication and writes tests – all while making independent architectural decisions based on best practices and the project context. No rigid template, just intelligent adaptation to the specific circumstances.

Ability to act

The ability of the agent to interact with “the world” – i.e. not just to generate text outputs, but to perform actions.A concrete example: An IT support agent does not give you instructions on how to configure your internet router, but does the configuration for you. They log in, change settings and verify the result.

A look at reality shows us a spectrum of agency:

• Minimal agents execute simple loops and decide when they are finished.
• Task agents choose between different tools based on the task at hand.
• Reflexive agents evaluate their own results and adapt their strategy.
• Proactive agents recognise problems before they arise and take preventive action.

Most ‘agents’ in production currently operate at the lower end of this spectrum. And for good reason.

The new challenge for enterprise IT

Delegating decisions to probabilistic autonomous systems means, in the worst case, a loss of control while retaining full responsibility for the outcome. This raises entirely new questions:

• How do I balance autonomy and control in an agent architecture?
• How do I deal with identity, trust and permissions for autonomous agents?
• How do my human employees work with agents?

The good news is that agents are not magic, but software

Before you start worrying about losing control, a dose of reality will help: AI agents are not magic, but modular software with clearly defined interfaces. What may sound sobering given the hype is actually liberating.

The intelligent, autonomous behaviour of an agent is usually based on a combination of:

• Structured JSON output of the models
• Deterministic code for critical paths
• Targeted control through configuration

The ‘agent’ translates the LLM output in JSON form into deterministic code paths. We design the degrees of freedom and control mechanisms. AI engineers and architects adjust these to suit the specific risk profile of an application case.

Architecture patterns for successful agents

The most successful agent implementations follow a common principle: small, focused agents with clearly defined areas of responsibility instead of monolithic super agents. These ‘micro agents’ retain their autonomy but operate in defined contexts.

In practice, five central design patterns dominate:

• 1. Chaining: Linking LLM steps, whereby the output of one step becomes the input for the next. Ideal for multi-stage analyses or processing processes.
• 2. Routing: The agent decides which sub-tools or paths to use based on the input. Perfect for classification tasks with different follow-up processes.
• 3. Parallelisation: Simultaneous processing of multiple aspects for better performance. Particularly valuable for independent subtasks.
• 4. Orchestrator workers: A main agent divides complex tasks among specialised sub-agents. The master pattern for complex systems.
• 5. Evaluator optimiser: Self-evaluation and iterative improvement of own outputs. Essential for quality assurance.

These patterns are not theoretical concepts. They work. Today. In production.

Memory management: the underestimated success factor

Successful agents forget deliberately. They implement:

• A short-term memory for the current context,
• a long-term memory for important facts and preferences,
• an episodic memory for past interactions, and
• expiration mechanisms for irrelevant information.

An agent without intelligent memory management will suffocate in data garbage after three days. With the right memory, it will run stably for months.

MCP: The new standard for tool use by agents

Think of the Model Context Protocol (MCP) as an app store for agent capabilities. Whereas the use of tools has always required individual integration in the past, the MCP now provides a plug-and-play architecture.

A practical example: A data analysis agent discovers during its work that it needs a new visualisation tool. In the past, this would have required a developer to program the integration. With the MCP, the agent can:

• find the tool independently via the MCP registry,
• check whether its auth policy allows its use,
• integrate and use the tool, and
• log its use transparently.

This elegantly solves the dilemma of autonomy control: the agent can independently use new tools (autonomy), but only those that are registered via the MCP and approved for its context (control).

Governance: The new reality of relinquishing control

This example brings us back to the question of agent governance. After all, agent autonomy does not release us from responsibility. However, relinquishing control to autonomous systems requires new governance models.

At this point, it may be helpful to remember that your human colleagues also make mistakes from time to time and to consider how you deal with this in your organisation. There are, of course, many differences, but a conscious culture of error will also be necessary for agents.

In practice, the solution usually lies not in total control or blind trust, but in hybrid human-agent collaboration.

Clear decision points are essential here. The agent must know

• when it can make autonomous decisions,
• when it should ask for clarification, and
• when it must escalate.

From demo to production

Governance issues show that understanding agents is one thing – using them productively is quite another. There is a huge gap between impressive LinkedIn demos and the harsh reality of production. And this is precisely where the wheat is separated from the chaff.

A proof of concept is quick to build. A few API calls, a bit of prompt engineering, and your agent is already solving impressive tasks in a protected environment. But then come the uncomfortable questions: How does the agent behave when faced with 10,000 requests a day? What happens in edge cases that no one has thought of? How does it integrate into existing systems with all their legacy peculiarities? And above all: How do you ensure that it will still work as reliably in three months as it did on the first day?

The pragmatic way forward

So what should you do? The most successful agent implementations follow clear principles:

• 1. Start small, grow in a targeted manner: One task per agent. We master complexity through orchestration, not through super agents.
• 2. Memory with an expiry date: Don't remember everything, forget what you need to forget. An agent that knows what it doesn't need to know is a good agent.
• 3. Frameworks can help, but they can also add complexity: Use abstraction, but understand what's going on underneath. Sometimes “simple Python” is easier than an abstract framework.
• 4. Monitoring from day one: Agents are difficult to debug. Without proper logging and tracing, you are blind.

Conclusion: Agents are here – use them correctly!

2025 is the year of agents because we finally understand what they are: not magical problem solvers, but well-orchestrated software components. The standards exist, the patterns are proven, the tools are there.

The key is not to build the perfect autonomous agent. It's about developing the right agents for the right tasks and integrating them sensibly into existing processes.

Agents are changing the way we work – but only if they are understood, controlled and used responsibly. The hype may be annoying, but the potential is real. It's time to take advantage of it.