5 data foundation and technology stack gaps stalling your AI agents

Quality data is the fundamental bedrock of effective AI. Without it, even the most advanced models will produce inaccurate or irrelevant results that destroy user trust.

Data needs to be accurate, clean, and managed under clear governance. You can push all the data into an AI tool, but if the quality is not there, the output will not tell the right story.

Autonomous agents need complete, real-time data to make decisions. When data is scattered across 50 different systems with inconsistent quality standards, your AI will inevitably hallucinate or fail.

Organizations need systems that can ingest, process, and retrieve data in real time while maintaining security and compliance standards. Poor or siloed data will always show up in the results.

To address the data accessibility gap, you must implement the following solutions:

• Build a unified data access layer that connects all critical data sources through a single platform.

• Deploy real-time data pipelines instead of relying on batch processing.

• Establish automated data quality monitoring to catch errors before they reach your models.

• Incorporate semantic search capabilities so that agents can find relevant concepts rather than just exact keyword matches.

Large language models (LLMs) are powerful, but they possess a fundamental limitation. An LLM's performance is not solely a function of its static internal knowledge, which is frozen at its last training date.

Its practical success critically depends on the external information and tools provided to it at the moment of inference. Without a native ability to access your live proprietary data, models will generate plausible but factually incorrect information.

This is where context engineering becomes essential. For models to pull the most relevant context, teams need access to advanced search techniques that match user query intent with relevant context from source systems.

Without accurate context, an agent will fail by hallucinating, selecting the wrong tool, or drifting away from its original objective. Context poisoning occurs when these errors compound across subsequent interactions.

Build a comprehensive context engineering practice by:

• Implementing retrieval augmented generation (RAG): This involves the AI retrieving external data "just in time" from a knowledge base, such as internal company documents or public websites. RAG enables the AI to answer questions using information it was not originally trained on, thereby ensuring its responses are both current and accurate.

• Managing memory strategically:

  • Short-term: Use checkpointers for current conversation state.

  • Long-term: Persist cross-conversation information to appropriate stores.

  • Implement trimming, summarization, and relevance filtering.

• Optimizing tool availability: Minimize tool count while maintaining coverage. Consider RAG-based tool selection to combat "tool confusion" where too many tools reduce accuracy.

• Ranking outputs for smarter content surfacing: Tools like Jina Reranker rescore retrieved content by how closely it actually matches a query, replacing rough similarity matches with precise relevance ranking. This ensures a reliable way to control what material gets surfaced and prioritized.

Organizations carry years of architectural compromises that make it exceedingly difficult to build enterprise-scale AI. Legacy infrastructure and outdated systems severely limit the capabilities of modern agents.

AI agents need to both retrieve context from and take actions within existing systems. When legacy systems lack standardized interfaces, agents cannot easily interact with your enterprise environment.

Without proper integration architecture, organizations face a difficult dilemma. They must choose between building custom AI from scratch or relying on fragmented SaaS AI features that remain disconnected from relevant enterprise context.

We need a durable enterprise architecture that connects context from all relevant distributed source systems. Modern integration layers are non-negotiable for autonomous operations.

Address legacy integration challenges by:

• Building a durable enterprise architecture: Ensure you connect context from all relevant distributed source systems and SaaS applications. SaaS applications still serve a purpose by bringing use case-specific knowledge, providing a workflow engine, and delivering a system of record.

• Implementing a context retrieval mechanism: This will pull knowledge from SaaS applications within your enterprise architecture to build context-aware AI applications.

• Modernizing incrementally: Don't attempt to replace all legacy systems. Instead, as we did at Elastic, adopt tools like LangChain as a library that helps orchestrate AI integrations, which allows us to build a more structured framework that naturally inherits the access controls and context of our native systems.

• Establishing AI/machine learning (ML) integrations: We use a platform approach using the Elasticsearch Platform that gives teams quick access to preferred LLMs and AI development frameworks to speed up development.

Organizations frequently lack visibility into how their AI systems actually perform in production environments. Without robust performance management, IT teams have no visibility and cannot trust the outputs of their autonomous experiences.

This unprecedented power of AI agents brings an unprecedented level of complexity. We are deploying systems that can draft a brilliant business proposal one minute and hallucinate an entire legal case the next.

Large language models are notoriously opaque and non-deterministic. This introduces severe challenges across reliability, cost, quality, and safety dimensions that traditional monitoring simply cannot handle.

Their latency and resource utilization spike unpredictably based on the sheer length and complexity of the output. With highly variable output lengths, your token count and infrastructure bill can fly off the rails without warning.

Layer observability into your AI architecture with three critical components:

• Implement application performance monitoring (APM) to track performance: Ensure you have visibility into the services and infrastructure supporting AI applications, enabling quick isolation of bottlenecks before they impact users.

• Deploy LLM observability: Provide critical business insights into AI model performance, context accuracy, and usage patterns including conversation grouping by use case.

• Add cloud monitoring: Correlate the performance and cost of cloud infrastructure, enabling quick diagnosis of infrastructure-related bottlenecks.

Beyond the technical hurdles, organizations suffer from cultural and structural problems that inhibit innovation. Legacy processes, shadow IT, and inconsistent governance models all slow down and introduce risk to agentic AI adoption.

AI cannot succeed as an IT-only initiative. When business partners actively participate in defining requirements and maintaining data quality, AI initiatives advance the strategic goals of the organization.

Without proper governance and organizational alignment, security vulnerabilities silently accumulate. This technical debt is the result of years of small security shortcuts and legacy systems left in place for far too long.

When AI is treated as a plug-and-play novelty, it stays a novelty. When it is approached as a strategic capability backed by strong governance, it becomes a massive force multiplier for your workforce.

Implement organizational structures and processes to ensure long-term success:

• Designate an AI champion: Successful AI implementations involve identifying a single-threaded leader to drive your organization's vision forward. Pair this with a dedicated group of individuals set on a clear goal and measurable outcomes.

• Build a center of excellence: Start with a core team and expand. Our AI outcomes at Elastic accelerated exponentially when we identified a lead for our use cases. We have since expanded into a small center of excellence focused on AI.

• Establish data governance frameworks: Implement data contracts that create accountability while improving data quality and accessibility through the data mesh approach.

• Create security protocols: Use AI-driven tools to automate threat detection, streamline vulnerability analysis, and even handle some of the routine documentation.

Scaling AI initiatives demands that modernization investments, organizational change, and strategic AI deployment must proceed in parallel.

When you address your data accessibility, context engineering, system integration, monitoring, and governance gaps, you transform your infrastructure. You move from a fragile environment into a future-ready foundation.

The shift to agentic AI requires clear business objectives, a pristine data foundation, and robust human oversight. By preparing your architecture today, you ensure your autonomous systems will deliver a clear ROI tomorrow.

Ready to put these lessons to work and transform your infrastructure? Read the 8 steps to build a scalable generative AI app to ensure your next initiative delivers measurable results.