Why context engineering is important, and how teams are delivering it
“How do you get the right data, in the right place, at the right time?”
That’s the core challenge behind bringing agentic AI to life in the enterprise. While large language models (LLMs) have unlocked powerful reasoning and orchestration capabilities, their effectiveness hinges on something more foundational: delivering the right business context for reasoning and taking action. Context engineering is a discipline focused on shaping how data, metadata, access policies, and memory come together to guide agent behavior in a secure and explainable way.
At Cloudera, we see this firsthand while partnering with enterprise customers experimenting with new generative AI (GenAI) and agentic AI use cases. Building agentic AI systems depends on something most organizations struggle with: data architecture that capture, govern, and reuse knowledge across the AI lifecycle.
In this blog, we share our approach to building agentic AI systems, which groups foundational capabilities into three buckets: Connect, Contextualize, and Consume. This approach enables our enterprise customers to build intelligent, trusted, explainable, and production-ready agentic systems.
Connect: Break Down Silos with Control
Modern AI agents can’t thrive in fragmented environments. However, most enterprises have data that’s spread across multiple clouds, data centers, legacy systems, and inconsistent formats. Exposing that data to an AI system without structure or safeguards leads to performance issues and governance risk.
In successful implementations, we’ve seen organizations focus first on creating a unified data layer that spans environments and formats. This doesn’t mean centralizing all data, but instead stitching it together in a data fabric architecture. This provides a unified layer with shared metadata, access policies, federated data engineering, and runtime interoperability.
Implementing an open table format and standard API access simplifies data access while delivering flexibility. Open lakehouse architectures matter here because they provide real-time, consistent views of data across engines—especially for agentic workflows that depend on reliable retrieval augmented generation (RAG) and reasoning.
Contextualize: Give Agents More Than Access
After data is connected, the challenge shifts to helping agents understand what data exists and how it's used. That starts with discovery: automatically identifying data sources across cloud and on-premises systems and activating the metadata—table names, fields, formats, and more. Tools like Cloudera Octopai Data Lineage scan ETL scripts, reverse-engineer pipeline logic, and capture how data moves and transforms across systems from the source to its final destination, capturing all the dependencies on its way.
This information forms the basis for lineage, which shows how datasets are related and how they change over time. Lineage matters when you need to validate a result, explain a recommendation or agent action, or trace a broken output to its source. It creates transparency and confidence in the systems with which agents interact.
Finally, cataloging brings this information into a usable structure. A centralized metadata store helps both humans and agents locate what they need, understand relationships between datasets, and surface policies that affect how data should be handled. A strong catalog acts like a blueprint—delivering a knowledge graph that gives agents a clear, navigable map of the enterprise’s data estate. It captures the technical, operational and business metadata including all the business definitions and the business logic required to understand the data and take action.
Contextualization enables agents to do more than retrieve information. It allows them to explore patterns, ask better questions, and make decisions with a deeper understanding of the environment they operate in.
Consume: Deliver the Right Context at the Right Time
The final step in building agentic systems involves enabling AI to take action in a way that is traceable, safe, and grounded in the right information. This is where architectural choices matter—guardrails, observability, and controlled access all shape whether agents behave predictably when it counts.
We’ve found it helpful to map common context engineering techniques to the underlying data challenges they’re designed to solve. Here are some examples of how they show up in practice:
Data Readiness Challenge |
Context Engineering Technique |
Cloudera’s Approach |
Sensitive data leaking into prompts |
Prompt engineering |
Prompt gateways to redact sensitive data |
Messy, unstructured data or outdated vector indexes |
RAG |
Governed and secure real-time streaming data pipelines |
Lack of lineage, brittle training sets |
Fine tuning |
Improve AI explainability with lineage tracking |
Agents overstepping, opaque decisions |
Tool/API access |
Metadata tagging, autonomous data classification, fine-grained access and full audit trails on every system call |
Agents unable to access internal enterprise knowledge |
Model context protocols (MCPs) |
Controlled access to Apache Iceberg-backed context with REST catalogs |
Choosing the right technique depends on the agent’s role, data sensitivity, and operational environment. Below are common enterprise use cases and the recommended combinations that have worked well in practice:
Use Case |
Recommended Method(s) |
Internal knowledge assistant |
RAG + vector DB + prompt engineering fallback |
Sales enablement bot with customer relationship management (CRM) data |
Function calling + business context injection |
Product-specific support agent |
Fine-tuning or RAG + MCP shared context |
Data analytics multi-agentic workflow to extract insights |
LangGraph + MCP + tool access + chunked memory |
Document understanding (PDF, Excel) |
Multi-modal inputs + preprocessing pipelines |
This approach to consumption ensures agents are operating with precision, security, and alignment to business goals.
Takeaways: From Framework to Action
At Cloudera, we’ve spent years navigating the complexities of enterprise data: bridging silos, enforcing governance, building secure pipelines for AI and analytics, and surfacing lineage across hybrid environments. So when agentic AI patterns began emerging, we weren’t starting from scratch. We knew where context lives, and how to capture it safely and securely with the right guardrails.
With Cloudera Octopai Data Lineage, teams can automatically map data flows, trace dependencies, and catalog metadata across cloud and on-premises environments. Layering in data catalogs, observability, and access control, agents can interact with systems more safely and intelligently. Teams gain visibility, governance, and trust–critical for scaling these workflows across the enterprise.
To make these pieces actionable, we’ve integrated these capabilities into our Open Data Lakehouse and Cloudera AI Studios, giving enterprises the foundation to design, deploy, and manage secure agentic systems in production.
Learn more about how Cloudera can help you with productionizing your AI agents with the right business context that they need.
