What’s it about?
Autonomous AI systems depend on continuous data access to make decisions and complete tasks. Two fundamental methods are available: directly extracting information from websites through scraping, or structured access via official programming interfaces. Both approaches have specific advantages and disadvantages that companies must consider when implementing autonomous AI solutions.
According to industry surveys, four out of five companies already use AI technology. With the growing prevalence of agent-based systems, the question of optimal data access is becoming increasingly important for business practice.
Background & Context
Web scraping enables immediate access to publicly available information by analyzing the HTML code of websites. The advantage lies in speed and independence: no permissions are required, no API fees are incurred, and data collection can be organized flexibly. However, this method brings significant risks. Website structures change frequently, which can lead to faulty data extractions. Moreover, companies often operate in legal gray areas, as it is not always clear which data may be used in which way.
Official interfaces, by contrast, offer structured, high-quality data with legal security. Service level agreements guarantee availability and stability; versioning enables predictable updates. For business-critical applications, these properties are of great importance. The downside manifests in lengthy negotiation periods, potential access restrictions, and sometimes considerable costs for licensing and integration.
Industry experts are observing the development of hybrid middleware solutions that attempt to combine the strengths of both approaches. Such systems could connect structured API access with the flexibility of scraping technologies while taking compliance requirements into account. The Fraunhofer Society notes in its analyses that multi-agent systems benefit particularly from standardized data access, while Bitkom emphasizes in its whitepaper on AI agent security the importance of controlled data sources.
What does this mean?
- Companies must weigh individually for each use case whether speed and cost savings or quality and legal certainty take priority.
- For prototypes and non-critical applications, scraping can be a pragmatic solution, while business-critical systems should rely on reliable API access.
- The legal framework for data access by AI agents is continuing to evolve — companies should regularly review their strategies.
- Hybrid approaches could become the standard in the medium term, dissolving the strict separation between the two methods.
- Data infrastructure costs are becoming an important factor in the calculation of AI projects.
Sources
How AI agents should (and do) consume data (Computerwoche)
Bitkom Whitepaper: Security of AI Agents
Fraunhofer IESE: Agentic AI and multi-agent systems
Handelsblatt: Securing competitive advantages with Agentic AI
This article was created with AI and is based on the cited sources and the language model’s training data.