Creating an AI Agent: what you really need (and what you don't)

Creating an AI agent mainly means contracting and training technological labor. For that, you need four things: a clearly defined task, access to relevant data, precise directives, and established boundaries with risk-based approvals. Technology alone replaces neither a clear assignment nor the business context.

Step 1: Define the task

“An agent that helps me” is not an assignment. Tasks that work well are recurring and clearly defined: checking incoming documents for deadlines, creating weekly summaries, taking calls and logging them. Formulate the task so that you could hand it to a new employee – including what does not belong.

Step 2: Make data accessible

An agent without context guesses. It needs access to the knowledge relevant for its task: contacts, tasks, documents, calendar entries, goals. If this data is scattered across separate tools, that is the first item on the agenda – before the agent itself. The article AI Agent with company data goes deeper into why this is so decisive.

Step 3: Formulate directives

Directives are your agent’s job interview: tell it not just what to do, but under what conditions. A real-world example: “Only create deadlines when date, obligation, and source are clearly documented – mention uncertain cases, do not create them.” Such conditions distinguish a usable agent from one that produces plausible nonsense.

Step 4: Set boundaries and approvals

The most important architectural decision: what can the agent do independently, and where does a person decide? Policies and guardrails let safe routines run without approval bottlenecks. For risky steps – such as far-reaching changes or sensitive external impact – you set targeted approvals. This way the agent works independently while you retain direction and final authority.

Step 5: Test and refine

Start with small scope, check the results, refine the directives. An agent does not become good when created, but when trained – like an employee during probation. Note which corrections you make repeatedly: those are exactly where the next directives and solid policies come from.

Build yourself or use a platform?

Custom development (frameworks, APIs, your own infrastructure) may be necessary for very special requirements – but it means ongoing responsibility for operations, security, and data integration. An existing operating system fits when agent, data, policies, and approvals already support the work assignment.

With webRichtung agent you do not build an isolated tool, but set up a workspace in the shared operating system: with tasks, automations, policies, heartbeats, memory, and targeted approvals. The details are shown in the documentation for automations & approvals.