Machine Learning Makes Complexity Computable

AI agents are software-based systems that autonomously perform tasks using a language model. They receive a goal and context, plan the necessary steps themselves, and use tools, data sources, and interfaces along the way.

Unlike chatbots, which react to inputs, agents act proactively: they execute actions, update systems, and make situational decisions. An agent combines a language model with access to data, tools, and planning capabilities.

Agentic AI describes AI systems that autonomously pursue goals, plan steps, and execute actions. Unlike traditional language models that react to inputs, agentic AI systems act proactively: they break down complex tasks into subtasks, use tools, and dynamically adapt their plan to new information.

The term encompasses both individual agents and multi-agent systems.

RPA automates fixed workflows: click A, input B, check C. The process must be entirely predictable. An AI agent, on the other hand, plans its own solution path. It interprets content, reacts to unexpected situations, and uses tools flexibly.

RPA works like a macro, an agent like a case worker. In practice, both complement each other: agents can call RPA bots as a tool when a substep is rule-based.

Agents make sense when there is no clear solution path, when input is highly variable, or when the task requires interpretation and flexible planning. Typical characteristics: unstructured documents, changing requirements, decisions that need contextual knowledge.

If a process can be fully mapped through rules, classical automation is more efficient. Agents step in where rule-based systems reach their limits.

AI agents work exclusively in digital systems. They require clean interfaces, clear process definitions, and sufficient data quality. Missing structure, inconsistent data, or manual media breaks cannot be compensated by agents.

Interfaces must also be agent-compatible: clearly structured, use-case-based APIs with unambiguous actions and consistent responses.

That depends on the operating model. With Human in the Loop, the agent analyzes and suggests, while the human decides. With Human on the Loop, the agent works independently but is monitored via KPIs and spot checks. With Human out of the Loop, the agent operates fully autonomously, with control at system level.

Which model fits is determined by the scope of decisions, regulatory requirements, and fault tolerance.

A multi-agent system consists of multiple specialized agents that work together. Each agent takes on a clearly defined role: one researches, one reviews, one summarizes, one escalates. Coordination happens through orchestration or direct communication between agents.

Multi-agent systems make sense when a task is too complex for a single agent, when different domains are involved, or when parallel processing is needed.

Yes. AI agents connect to existing systems via APIs, webhooks, or protocols like MCP: ERP, CRM, DMS, ticketing, email, calendar systems. Integration is done through standardized interfaces or custom connectors.

An agent can read data from SAP, create tickets in Jira, send emails in Outlook, or store documents in SharePoint. Prerequisite: the systems must be accessible via interfaces.

RAG stands for Retrieval-Augmented Generation. The principle: before a language model responds, relevant information is retrieved from a knowledge base and provided as context. This allows an agent to access internal company knowledge without the model itself being trained on it.

RAG is the foundation that enables agents to work with current, company-specific data: policies, product catalogs, contract content, process documentation.

MCP is an open standard that defines how agents access external tools and data sources. Instead of building a custom interface for each integration, MCP describes available tools in a unified format.

The agent automatically understands which capabilities are available and how to use them. MCP reduces integration effort and makes agents modularly extensible.

Context Engineering determines which information an agent receives at which point in time. An agent is only as good as its context: which documents does it see? Which system data flows in? Which instructions apply?

Context Engineering is the deliberate design of this information space. It determines response quality, hallucination rate, and decision-making capability. Unlike Prompt Engineering, it is not about a single input but about the agent's entire information model.

Memory is the persisted knowledge that an agent builds up over the course of its work. It remembers not just conversation histories but process patterns, exceptions, and implicit company knowledge.

An agent with Memory knows after three months which requests need to be escalated, which phrasings work with customers, and what the exception to rule X looks like. This knowledge makes the agent better over time and is the real value driver.

Security is an architecture question, not a feature. An agent needs its own identity with clear permissions: which systems may it access? Which actions may it perform? The principle of least privilege is mandatory.

Agents may only possess the rights that the respective user has, and only see data that user has access to. Additionally, agents must be protected against prompt injection, manipulated inputs, and uncontrolled action chains.

Prompt injection is the attempt to alter an agent's behavior through manipulated inputs. Countermeasures include strict separation of system prompts and user inputs, input validation, output filtering, and sandboxing of critical actions.

Additionally, monitoring and anomaly detection help identify unusual behavior early. In security-critical contexts, a second verification layer validates agent decisions before execution.

A productive agent must be designed to be fault-tolerant. Critical actions are checked before execution, reversible actions are preferred. Every decision path is logged and traceable. When uncertain, the agent escalates to a human.

Monitoring detects deviations in real time. What matters is not whether an agent ever makes mistakes, but whether its error behavior is controlled, transparent, and containable.

The range is wide. A first agent based on an existing platform like Galilea can be set up in minutes or realized directly as part of ongoing support as a Galilea customer.

A production-ready agent with security concept, RAG integration, system connectivity, and monitoring requires a more comprehensive project. Costs depend on complexity, integration depth, and operating model. We offer various entry formats: from the AI Pilot as proof of concept to the 100-Day MVP for productive deployment.

Through clearly defined KPIs that are set before launch. Typical metrics: throughput time per case, automation rate, error rate, escalation rate, cost per transaction, user satisfaction.

Additionally, we measure model performance: response quality, hallucination rate, latency. What matters is the comparison with the manual process. An agent does not need to be perfect — it needs to be better than the status quo and improve in a controlled manner.

Because we don't just advise — we build. Hundreds of AI projects, our own enterprise platform, a team of developers and architects that delivers in days. We know the architecture decisions, the security requirements, and the pitfalls in operation.

Galilea as our own platform means: no stitching together third-party services, but an end-to-end environment for development, operation, and continuous improvement. From the first agent to a multi-agent system — all from one source.