AI agent development is off to the races. A 2025 survey from PwC found that AI agents are already being adopted at nearly 80% of companies. And, these agents have an insatiable lust for data: 42% of enterprises need access to eight or more data sources to deploy AI agents successfully, according to a 2024 Tray.ai study.

“AI relies on data, and since 90% of data today is unstructured, it is important to create an effective interface for AI agents to get the right enterprise data,” says Or Lenchner, CEO of Bright Data, a web data collection platform.

We’ve seen large language models (LLMs) become more informed with retrieval-augmented generation (RAG) techniques, in which AI systems pull in external sources before generating a response. More recently, a new class of scraping and browser automation tools has emerged that can mirror human-like behavior on the web.

Web MCP, for instance, is a Model Context Protocol (MCP) server that can enable AI agents to circumvent CAPTCHAs, perform on-screen browser automation, and scrape real-time data from public web sources. Other tools, including MCP servers, browser automation frameworks, and scraping APIs, offer similar capabilities. These include Browser Use, Playwright, Puppeteer, ScrapingBee, and Apify, among others.

ChatGPT, Gemini, and Claude were trained in part on publicly available web content, and they can retrieve current web information at run time using retrieval or browsing tools. Official APIs, on the other hand, are often pricey, are rate-limited, and require onboarding time. So, why shouldn’t agents scrape whatever’s online as their primary data source?

Well, anyone acquainted with social media knows that public data is riddled with inaccuracy, bias, and harmful content. Although scraping offers quick, free, and universal access, it’s highly problematic. After all, it’s contingent on public pages or applications that were designed for human consumption, not machine readability.

APIs, on the other hand, guarantee more structure and stability via authenticated access to internal and private integration points.

So, an age-old conundrum resurfaces for AI agent builders: to scrape or integrate. And like most technical dichotomies, it depends—both come at a cost. Below, we’ll weigh the pros and cons of each, taking a pragmatic approach to determine which is the best course of action, and when.

Why agents need external data

AI agents often need to perform tasks that require access to data. For this, an AI knowledge base can provide internal institutional context. But an internal knowledge base is rarely enough, and external data is often necessary to deliver real value.

“Agents without live external data are frozen at training time,” says Bright Data’s Lenchner. “They can’t reason about today’s prices, inventory, policies, research, or breaking events.”

Agents benefit from real-time information ranging from publicly accessible web data to integrated partner data. Useful external data might include product and inventory data, shipping status, customer behavior and history, job postings, scientific publications, news and opinions, competitive analysis, industry signals, or compliance updates, say the experts.

With high-quality external data in hand, agents become far more actionable, more capable of complex decision-making and of engaging in complex, multi-party flows. “This unlocks autonomous operations,” says Deepak Singh, CEO and co-founder of AvairAI, creators of an AI agent for sales teams.

Real-time external data connections can open up a wide range of autonomous actions that produce tangible outcomes, including:

• Approving loans with real-time credit verification
• Verifying compliance documents
• Validating customer information across systems
• Incorporating market sentiment into financial reviews
• Coordinating delivery based on real-time traffic or warehouse capacity
• Personalizing responses based on individual user preferences

“It’s not just about giving agents more data,” says Neeraj Abhyankar, VP of data and AI at R Systems, a digital product engineering company. “It’s about giving them the right data at the right time to provide the best possible outcomes.” 

When to scrape website data for AI agents

So, how should agents retrieve external data? One approach is scraping public web sources, such as social media feeds or product catalogs. This typically involves scraping tools, browser automation, or proxy networks that extract data from the HTML of public websites.

According to Lenchner, the advantages of scraping are breadth, freshness, and independence. “You can reach the long tail of the public web, update continuously, and avoid single‑vendor dependencies,” he says. 

Today’s scraping tools grant agents impressive control, too. “Agents connected to the live web can navigate dynamic sites, render JavaScript, scroll, click, paginate, and complete multi-step tasks with human‑like behavior,” adds Lenchner.

Scraping enables fast access to public data without negotiating partnership agreements or waiting for API approvals. It avoids the high per-call pricing models that often come with API integration, and sometimes it’s the only option, when formal integration points don’t exist.

“Many vendors are locking down access to user-generated data behind expensive APIs, and scraping allows alternate paths,” says Gaurav Pathak, vice president of AI and metadata at Informatica, a cloud data management provider.

However, scraping has plenty of drawbacks, starting with data quality. “Preprocessing scraped data can be messy and inexact,” says Keith Pijanowski, AI and ML solutions engineer at MinIO, a data infrastructure company for AI.

“It’s building on quicksand,” says AvairAI’s Singh. “Websites change layouts without notice, breaking your scrapers. You’re violating the terms of service, risking legal action. Rate limiting and CAPTCHAs create constant technical battles.”

The lack of schema, context, and data validation also increases the risk of agents collecting the wrong public data, leading to wasted engineering effort. “We’ve seen enterprises spend more money maintaining scrapers than they would on proper integrations,” adds Singh.

Others highlight the legal exposure as well. “Enterprises are hesitant to use AI produced via scraping because of the liability they could end up inheriting through derivative works,” says Krishna Subramanian, co-founder and COO of Komprise, provider of an intelligent data management platform.

Because of its fragility, scraping is often a poor fit for core production-grade agentic systems that require consistent, compliant results. Instead, it’s best reserved for use in peripheral areas like:

• Proof-of-concepts
• Non-critical data
• Competitive market research
• Legal aggregation of clearly public data
• Side projects
• When no integration points exist

When to integrate data for AI agents through APIs

Another option is for agents to retrieve external data through official integration points. This includes structured responses via REST, GraphQL, or SOAP-based APIs, event-driven updates through webhooks, or via official MCP servers.

While integrations incur more upfront setup than scraping, they generally deliver higher data quality and avoid legal troubles. They’re also more predictable: APIs are typically specification-driven, backed by service-level agreements (SLAs), and versioned to minimize breaking changes.

“Relying on official integrations can be positive because it offers high-quality, reliable data that is clean, structured, and predictable data through a stable API contract,” says Informatica’s Pathak. “There is also legal protection, as they operate under clear terms of service, providing legal clarity and mitigating risk.” 

“Official integrations via APIs, webhooks, or secure file transfers offer stability, traceability, and compliance,” adds R Systems’ Abhyankar, noting that reliable data exchange is especially important for auditability in health care and financial services.

Others underline the importance of stability for enterprise-grade agent experiences. “When your agent makes a million-dollar decision, you want data you can trust and vendors you can hold accountable,” says AvairAI’s Singh. “Official integrations provide the stability enterprises need.”

On the flip side, official integrations carry some drawbacks that limit their usefulness for broader AI use cases. Platform owners might constrain API access with rigid data models or bespoke rate limits. “APIs may omit fields that are publicly visible on the web, deliver delayed data, and can revoke access at any time,” says Lenchner.

On top of that, time and politics can be a drawback, particularly for deep partner integrations. “Getting API access can take months of partnership negotiations,” adds Singh.

Even when access is granted, it’s not set in stone. Over the years, developers have witnessed API restrictions or closures from public platforms such as Instagram, Slack, Salesforce, Bing, Reddit, Spotify, Marvel, and plenty of others. Some data providers don’t offer real-time APIs at all, relying instead on batch-based SFTP file transfers.

Cost is another concern. “Cost is the biggest drawback, as quality data will almost certainly come with a high price tag,” says MinIO’s Pijanowski. To his point, sudden API price hikes by platforms like X or Google Maps have historically driven developers toward workarounds.

Official integrations also require custom development and maintenance per API, along with additional authentication and authorization configuration. Still, most experts can stomach the trade-offs in exchange for better reliability, governance, and compliance.

Compared to scraping, official integrations are a more mature and controlled form of data acquisition, making them a better fit for agentic scenarios that span:

• Mission-critical operations
• Partner ecosystems
• Situations that need long-term consistency
• Transactional workflows that don’t require public data
• Governed enterprise applications that require SLAs
• Financial and health care data
• Personally identifiable information (PII)

Choosing between scraping and API integration for agents

AI agents now cover a wide array of use cases, from customer help desks to business workflows and coding assistants. Likely due to this diversity, nearly half of organizations deploy between six and 20 types of AI agents, according to Salt Security’s 2025 AI Agents Report.

Furthermore, the use of agents is spread quite evenly by sector. A 2025 McKinsey study found that AI agents are most commonly used in IT and knowledge management. By industry, agentic AI adoption is highest among professionals in technology, media and telecommunications, and health care.

Because AI agents span different purposes, domains, and industries, it’s hard to pin down a single data strategy that fits every scenario. That said, experts point to clear cases where one approach makes more sense than the other.

If you’re operating in a partner’s ecosystem, using non-public sources, and working with financial data or health care data, official integrations are the clear choice, hands down. But if you’re a startup that relies on ingesting current news, market signals, or social media, it’s a different story.

That’s not to say that scraping is the only—or even the best—way to access public data. For instance, as The Register reports, major AI companies consume Wikipedia’s public data corpus via the Wikimedia Foundation’s enterprise-grade APIs. At the same time, Cloudflare’s announcement to block AI crawlers by default suggests a broader industry shift toward controlled access, and away from unrestricted scraping.

Another way to frame the dichotomy is risk tolerance. “If errors could cost money, reputation, or compliance, use official channels,” says Singh. “If you’re enhancing decisions with supplementary data, scraping might suffice.”

Viewed this way, web scraping becomes more of a tag-along enhancement for AI agents—a way to add contextual, hard-to-integrate public data when legally permissible. Whereas traditional integrations represent the core, trustworthy source of truth that guides real-world actions and autonomous decision-making.

Hybrid approaches and middleware are also emerging to manage both paths. “We’ve built agentic layers that dynamically switch between scraping and integrations depending on context,” says Abhyankar, noting that agents may use public data for visibility while relying on APIs for internal synchronization.

Where will you build your house?

As agentic AI proliferates, the data strategy behind it is coming more into focus. How developers hardwire data access into agents will directly affect accuracy, reliability, and compliance for the long term.

“When collecting external data, it’s not about choosing one method over another,” says Abhyankar. “It’s about aligning the data strategy with business goals, operational realities, and compliance requirements.”

Official integrations are purpose-built for enterprise use and provide better support for governance, auditing, and enforcement. “This is a better long-term strategy because it is well-architected for enterprise consumption,” says Komprise’s Subramanian.

Others agree, arguing that the structured approach affords better foundations than the quicksand of scraping. As Singh puts it, “Betting your operations on scraping is like building your house on someone else’s land without permission.” 

“Access isn’t enough,” he stresses. “You need trusted, accurate, real-time data.”