AI agents require real-time external data to remain effective
If you’re deploying AI agents and relying only on internal data, you’re operating with a blindfold. AI needs context, not just computation. It operates better when it understands the world as it is now.
In 2025, PwC reported that nearly 80% of companies had already adopted AI agents. These agents are built to handle tasks that demand adaptability, customer service, supply chain optimization, financial analysis, and more. But without real-time external inputs, AI agents stall. They become static, trapped in their training data. That’s a serious limitation.
Or Lenchner, CEO of Bright Data, pointed out that 90% of enterprise data today is unstructured. Public web data, partner feeds, news content, and evolving product listings, all of it helps AI remain relevant and accurate. Tray.ai’s 2024 study found that 42% of enterprises rely on eight or more data sources just to get their AI deployed successfully. Internal knowledge bases provide historical context, but they can’t update themselves with new market signals, policy changes, or customer behavior. That’s what external data delivers.
The difference here is operational. Real-time external data unlocks what Deepak Singh, CEO of AvairAI, calls autonomous action. We’re talking about enterprise AI systems that approve a loan using live credit data or reroute deliveries based on traffic in the moment. That capability creates competitive edge.
Neeraj Abhyankar, VP of data and AI at R Systems, was clear about this: it’s not about giving AI more data, it’s about giving it the right data at the right time. That’s how you improve outcomes. If you’re serious about scaling agents across mission-critical workflows, real-time external data isn’t optional. It’s foundational.
Web scraping offers fast data access but has serious trade-offs
Scraping is fast. It’s flexible. And sometimes, it’s the only way you’re getting the data. That’s the appeal. Public web pages are everywhere, update constantly, and are often free to pull from. Tools like Playwright, Apify, and others allow agents to behave almost like a real user, scrolling, clicking, filling out forms, to get the job done.
Or Lenchner mentioned that scraping covers the “long tail” of the web, that messy, unstructured side of the internet with high-frequency change and broad information coverage. Scraping allows AI systems to do real-time research, consumer monitoring, market tracking, and competitive intelligence without waiting for partnerships or API approvals. You’re not blocked by formalities, you get the data directly.
But let’s not ignore the downsides. Keith Pijanowski, AI engineer at MinIO, emphasized that messy sources lead to messy outcomes. The data isn’t structured or validated. Websites change without notice, and suddenly your scraper breaks. That means downtime, and you’re paying engineers to rebuild things that should’ve worked in the first place.
Deepak Singh from AvairAI called this approach « building on quicksand.” He’s right. On top of the technical fragility, scraping creates potential legal exposure. If you’re pulling data against terms of service, you risk compliance violations and reputational damage. Krishna Subramanian, COO at Komprise, said many enterprises back off scraping for that exact reason, they don’t want liability tied to derivative AI outputs.
Scraping has its place, proof-of-concepts, early-stage development, or when no integration point exists. But when you’re talking about production-grade systems? Especially ones that affect your business operations or customers directly? That’s not where you want uncertainty or broken inputs. Getting data fast is good, but only if it’s data you can trust.
Official API integrations deliver structured, reliable, and compliance-friendly data access
When accuracy, governance, and traceability matter, scraping isn’t enough. Enterprise systems that power financial decisions, clinical workflows, or high-stakes operations require clarity and control over data inputs. That’s where API integrations come in.
Official APIs offer structured, well-defined access. They come with change management, version control, and support. More importantly, they’re governed by legal frameworks and often backed by service-level agreements (SLAs). These guarantees provide the kind of predictability that enterprise systems need to scale securely and compliantly. In industries like healthcare and finance, where auditability and traceable data flows are mandatory, this is the only viable path.
Neeraj Abhyankar, VP of data and AI at R Systems, pointed out that APIs don’t just offer data, they offer reliable, compliant data exchanges. For transactional workflows, customer records, or regulated datasets, this level of quality isn’t a preference, it’s a requirement. There’s a reason why AI agents responsible for mission-critical decisions lean heavily on official integrations.
Gaurav Pathak, VP of AI and metadata at Informatica, emphasized that unlike scraped data, what comes through APIs is clean and predictable. You operate within a defined schema, which makes it easier to validate and monitor what your agents consume and act upon. These systems aren’t guessing; they’re executing based on verified inputs.
There are upfront costs. APIs usually involve custom development, platform onboarding, and continuous updates to keep pace with provider changes. You’re also dealing with rate limits, access restrictions, and sometimes narrow data scopes. And yes, pricing models can scale quickly. Keith Pijanowski from MinIO noted these data costs can be high. But when the data affects regulated workflows or financial outcomes, the ROI from reliability outweighs the initial investment.
Deepak Singh, CEO and co-founder of AvairAI, put it plainly: when your agent is driving outcomes worth millions, trust in the data becomes non-negotiable. Official integrations might take longer to implement, but they’re built for durable, enterprise-grade work.
The choice between web scraping and API integration depends on use case, risk tolerance, and operational goals
There’s no single right answer when deciding how AI agents should access data. The right solution depends on what you’re trying to achieve and what kind of risk you’re willing to accept. AI agents vary widely, some personalize emails, others assist in financial planning or monitor supply chain disruptions. The data strategy must match the role and responsibility of the agent.
For low-risk, high-velocity tasks, like market trend monitoring or social sentiment analysis, scraping is still defensible. The data might not be perfect, but it’s fast, broad, and often the only available channel. That matters for startups and fast-moving teams that can’t afford to wait weeks for partner approvals or budget for API costs. It also helps in early-stage R&D and when external data isn’t well-standardized across providers.
But for teams operating inside partner ecosystems, or dealing with personally identifiable information (PII), compliance standards, or financial settlements, the bar is higher. APIs offer security, reliability, and incremental scalability. These are not “nice-to-haves.” They’re operational necessities. As your AI workflow matures and begins to interact with core business systems, the tolerance for error goes down. That’s where official integrations become essential.
The 2025 Salt Security AI Agents Report shows that nearly half of organizations deploy between six and twenty kinds of AI agents, often spanning different functions and levels of risk. A 2025 McKinsey study confirms the most common use cases cluster around IT and knowledge management, but adoption is strong across telecom, media, and healthcare. That kind of diversity reinforces a tailored approach: different agents, different data strategies.
As Deepak Singh from AvairAI said, “If errors could cost money, reputation, or compliance, use official channels.” If you’re amplifying decisions with supplementary or non-critical data, scraping might suffice. And if you’re blending both, you’re not alone, hybrid systems are emerging that automatically switch methods depending on task sensitivity or data availability.
This isn’t about speed versus quality. It’s about clarity in your operational priorities. You shouldn’t scale your data strategy until you know exactly what kind of performance you need from each agent in production. Choose based on value, reliability, and compliance. Nothing else.
Long-term AI agent development hinges on aligning data acquisition strategy with business objectives
As AI agents become more embedded in critical business processes, your data strategy becomes directly tied to the credibility and scalability of your operations. That’s not a side effect, it’s the main outcome. The way you architect external data access will determine not just what your AI agents know, but what they can safely act on, and at what level of trust.
Long-term viability isn’t about using more data. It’s about using the right data under the right conditions, consistently, legally, and with integrity. Whether AI agents are automating customer support, underwriting loans, or managing infrastructure workflows, they must operate on datasets that meet enterprise-grade expectations around structure, lineage, and compliance.
Leaders like Krishna Subramanian, COO of Komprise, are clear: official integrations are a more solid foundation for enterprise applications. They offer framed access, enforceable agreements, and long-term operability through APIs and secure interfaces. You don’t compromise governance or auditability just to move quickly.
Neeraj Abhyankar from R Systems emphasized the need to align your approach with real-world business requirements. That means recognizing that different teams and use cases will require different data sources and delivery mechanisms. Some external sources will be raw and fast; others will be refined and regulated. It’s your job to ensure your organization knows which is being used where, and why.
Deepak Singh of AvairAI makes a key point here: access isn’t enough. Just because the data is available doesn’t mean it’s usable, or suitable. You need confidence that your agents are basing decisions on validated, current, and compliant information. Otherwise, the risk compounds, and scales with each new deployment.
The future of AI agents depends on how companies commit to mature infrastructure. That includes not only intelligent orchestration, but also a disciplined approach to data sourcing. Once AI begins making decisions at scale, it’s not just about performance, it’s about accountability. The more systems you connect, the more critical it becomes to get the access model right. High availability means nothing if accuracy, legality, or governance is neglected.
Leading organizations are starting to build hybrid models, combining the immediate scope of scraping with the control and stability of API integrations. But the priority remains: long-term agentic AI depends on building a data environment that executives can trust, engineers can maintain, and regulators can audit. Scaling AI without that foundation isn’t progress. It’s exposure.
Key highlights
- AI agents need real-time external data to stay relevant: Leaders should ensure AI systems have live access to external data sources to enable current, context-aware decision-making and unlock autonomous operations across functions.
- Web scraping is fast but fragile and risky: Use scraping for supplemental, non-critical data needs or early-stage projects, but avoid relying on it for production systems due to legal uncertainty, data quality issues, and maintenance overhead.
- APIs offer stability, compliance, and trusted data: Prioritize official integrations when accuracy, governance, and auditability are required, particularly in financial, health, and partner-driven environments.
- Choose data methods based on risk and use case: Scraping fits exploratory and time-sensitive tasks, while APIs suit structured, high-value workflows. Adopt a hybrid approach for flexibility while controlling risk exposure.
- Data strategy defines long-term credibility and scalability: Align external data access methods with governance, legal, and operational goals. Trusted, structured inputs are essential for scaling AI safely and sustainably.
