Knowledgeagent
AI Agents: 7 Examples from Business Reality
Seven concrete examples of technological workforce: phone reception, document work, automations, drafts, coding sessions, and more.
AI agents are AI systems that independently complete tasks rather than simply answer questions. The following seven examples show concrete work areas: from phone operations through documents to coding sessions.
1. Answering calls and managing callers
The most tangible agent works in phone reception: it answers calls or routes them, manages caller data, and is configured from the company context for its work area. The statistics then show the team how this work area is being used.
2. Checking documents for deadlines
documents evaluates incoming documents, while core maintains deadlines and tasks in the shared data core. An agent can turn this into a regulated work order: clear routine runs within policies, uncertain or risky cases come back with source for your decision.
3. Completing recurring tasks on schedule
Regular evaluations and recurring routine tasks: automations and work orders let the agent work even when no one is chatting with it. Policies, heartbeats, and approvals give this work area its reliable rhythm and boundaries.
4. Drafting emails and texts
Response drafts, offer copy, summaries of long conversations: assist supports chat, skills, and the creation of artifacts and documents. Whether a result runs independently or needs approval is your choice based on impact and risk, not a blanket restriction.
5. Researching and distilling
Gathering, structuring, and condensing information – for example as preparation for a customer meeting: What happened recently with this customer, which tasks are open, which documents are relevant? The shared data core from core and documents gives the agent the necessary company context for this.
6. Feeding the workflow
Automations and work orders in the agent module connect delegated work with tasks and deadlines in the shared data core. Instead of work disappearing into emails and heads, it gets a visible process with appropriate policies and targeted intervention points.
7. Implementing entire projects
The most advanced form: agents that implement multi-step projects in their own coding session. workspace offers a browser cockpit on request for sessions, runtimes, hosts, and approvals. From “the AI answered” becomes “the technological workforce delivered.”
What all examples share
Two patterns run through all of them: First, agents need context – without access to contacts, documents, and appointments, they remain generic text generators. Second, they need guardrails: safe routine runs within clear policies, risky steps pause at targeted approval points. You maintain direction and ultimate authority.
webRichtung agent bundles these patterns as controllable workforce: with work orders, automations, policies, heartbeats, memory, and approvals for risky steps. What an agent fundamentally is is explained in the definition article What is an AI Agent?
Frequently asked questions
What are typical examples of AI agents?
Phone answering, document analysis, recurring automations, text and document creation, prepared customer context, and agents that perform implementation work in their own sessions.
Do AI agents work completely independently?
Within their parameters, yes. Policies and guardrails establish safe routine; for risky steps, you set targeted approvals. Ultimate authority remains with you.
Which example is best for getting started?
Tasks with clear patterns and limited scope: for example analyzing documents or creating regular summaries. There you can clearly review results and rules.
Do AI agents need access to company data?
Yes – that is the decisive difference from a general chat: An agent that knows the company's contacts, documents, and appointments delivers results that fit your business.