Data foundation that powers successful enterprise AI agents
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Most organizations severely underestimate the foundational work required to prepare enterprise data systems to support AI agents. Without reliable ingestion pipelines, clean and well-governed datasets, scalable vector infrastructure, and real-time retrieval mechanisms, AI agents are unable to deliver the desired outcomes.
As enterprises push beyond experimentation into real-world use, AI agents are rapidly becoming transformative business tools. According to a recent report, AI agents are projected to generate up to $450 billion in economic value by 2028, yet only 2% of organizations have fully scaled their agent deployments so far.1 This stat highlights the vast untapped upside, but also underscores a critical challenge: without a strong foundation in data readiness and governance, most businesses will struggle to cross the chasm from pilot to production.
Data architecture considerations when building AI Agents
Building effective AI agents goes beyond just plugging in an LLM. It requires a robust and scalable data architecture that supports intelligent, real-time responses. AIOps play a vital role, ensuring that agents move from experimentation to enterprise-grade reliability.
- Ingestion pipelines: Reliable pipelines are essential for bringing in structured and unstructured data from multiple sources, such as CRMs, ERPs, and documents, while maintaining freshness and consistency.
- Vector databases: These specialized databases store and retrieve embeddings efficiently. They power semantic search and context-aware responses by matching user queries with relevant knowledge.
- LLM routing logic: Routing logic determines how queries are handled, parsing intent, classifying types, and directing them to the right tools or knowledge sources. It’s critical for ensuring fast, accurate answers.
- Caching and observability layers: Caching improves response time by storing frequent query results, while observability layers track performance, accuracy, and usage trends. Together, they ensure reliability and continuous improvement of the agent.
6 key steps to build and train AI Agents
Developing an AI agent requires a structured approach that covers purpose, data, training, and validation. By following these six key steps, you can build an agent that is accurate, scalable, and aligned with real-world use cases.
How Sigmoid’s RAPID framework accelerates AI Agent development
Building enterprise-ready AI agents involves navigating a complex maze of infrastructure setup, governance, cost control, model selection, and development velocity. Many organizations get stuck at the PoC stage due to fragmented tools and a lack of operational clarity. To address these gaps and operationalize AI at scale, Sigmoid has developed the RAPID framework, a production-grade foundation that brings together deployment automation, policy-driven governance, model flexibility, and developer-ready accelerators into one cohesive stack.
- One-click Deployment with Terraform
Spin up fully compliant infrastructure with a single command, ensuring faster setup without compromising architectural standards.
- Single Pane Governance
Enforce data security with runtime-based routing policies that prevent leakage and ensure only approved LLMs access sensitive data.
- Transparent Billing and Cost Controls
Avoid cost overruns with built-in caching, usage monitoring, and dynamic cost attribution across teams and use cases.
- LLM Garden and BYOM (Bring Your Own Model)
Flexibly choose from hosted or self-deployed LLMs, selecting based on cost, latency, or accuracy needs.
- GenAI Accelerators
Leverage ready-to-use components, prebuilt connectors, and chatbot templates to shorten development cycles significantly.
- Prompt Studio
Design, test, and manage prompts using GitOps-style workflows, with built-in version control and performance monitoring.
Conclusion
AI agents are transforming how enterprises interact with data, make decisions, and serve users. But deploying them at scale requires more than just plugging into an LLM. You need clean, integrated data across systems. You need a robust infrastructure that handles ingestion, vectorization, retrieval, and observability. That’s where Sigmoid’s RAPID framework comes in, a production-ready foundation designed to help enterprises move fast without compromising on quality, security, or cost control.
About the author
Rakesh Dhale is an experienced MLOps Lead with over 5 years of expertise in designing and implementing end-to-end MLOps solutions. Skilled in platforms like Azure ML, Databricks, SageMaker, and Kubeflow, Rakesh specializes in streamlining the entire ML lifecycle, including data ingestion, feature engineering, model training, hyperparameter tuning, deployment, monitoring, and CI/CD automation. With a strong focus on scalability, reliability, and automation.
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