Grounding AI in Reality: Quantexa’s Role in Context Engineering

As AI has evolved, a new discipline has emerged at the heart of enterprise transformation: Context engineering. As organizations move from simple chatbots to sophisticated agentic AI systems, contextual data differentiates between success and failure.

Quantexa has since its inception been at the forefront of delivering decision- and AI-ready data, providing the tools and infrastructure to operationalize context at scale. This blog explores context engineering, why it has become so popular, and how Quantexa’s Agent Gateway and Decision Intelligence Platform are enabling the next generation of agentic AI, and automated and augmented decision-making.

Why has context engineering become so popular, so quickly?

Generative AI systems have relied heavily on prompt engineering, the art of crafting precise instructions to coax better outputs from LLMs. However, as AI use cases become more complex, enterprises need AI that can reason, plan, and act autonomously, grounded in the full context of their business, data, and workflows. Influencer Andrej Karpathy popularized the term context engineering in June 2025 when citing Shopify CTO Tobias Lütke’s preference for the term over prompt engineering. He described it as “the delicate art and science of filling the context window with just the right information for the next step.”

Several factors have driven the rapid adoption of context engineering:

• Explosion of context windows: Modern LLMs can now process tens or even hundreds of thousands of tokens, enabling them to ingest vast amounts of context—documents, conversation history, structured data, and more.

• Agentic AI systems: The shift from reactive chatbots to proactive, tool-using agents has made context not just helpful, but essential. Agents must understand their environment, goals, and constraints to act responsibly.

• Enterprise demands: Real-world deployments require continuity, compliance, explainability, and integration with internal data. Prompt engineering alone cannot deliver these; context engineering can.

• Data is everywhere: Organizations are awash with data. Context engineering offers a systematic way to unify, enrich, and operationalize this data for AI.

As AI systems become increasingly agentic, the need for robust, dynamic context has become a strategic imperative.

What is context engineering?

Context engineering entails designing, integrating, and orchestrating the information environment in which AI systems operate. It strives to encompass everything that shapes intelligent behavior: documents, structured data, memory, workflows, policies, and unstructured sources like audio or video.

Key elements of context engineering are:

• System instructions: Defining the AI’s role, rules, and objectives

• Conversation history: Maintaining memory and continuity across interactions

• External data retrieval: Dynamically pulling in relevant documents, databases, and APIs

• Tool and workflow integration: Enabling agents to use external tools and act within business processes

• Security and governance: Ensuring compliance, access control, and auditability

In practice, context engineering means building systems that gather, filter, and format the right information for each AI interaction. It’s about architecting the entire information pipeline to maximize what the model knows, not just what it’s asked. When it is implemented poorly, context poisoning or context distraction can ensue, in conjunction with challenges such as:

• Scalability: Handling massive context windows for large language models and multi-agent systems without performance degradation

• Dynamic context management: Context management is never static. It needs continually updating

• Data fragmentation and integration: Combining different types and siloes of data – structured, semi-structured and unstructured - from multiple sources

• Relevance filtering in order not to overwhelm the LLM with noise

• Enforcing access controls, for security, governance, compliance, and auditability when sharing sensitive contextual data across systems

• Explainability, transparency, interpretability and traceability for regulatory, trust, bias and quality control purposes

• Interoperability with diverse AI models, tools, and enterprise systems

• Latency and real-time access for time-sensitive decisions in operational environments

In agentic AI systems, networks of specialized agents that collaborate to solve complex problems, challenges are magnified with the system's complexity. Context is neither static nor siloed; it must be curated, shared, and evolved within and across agents, systems and over time to ensure alignment, coordination, trust, and responsiveness. This is how enterprise platforms like Quantexa can help align agents with grounded data appropriate to the agent, and to the moment.

How the Quantexa Agent Gateway enables context engineering

Quantexa’s Agent Gateway addresses the challenges of context engineering in agentic AI systems. Utilizing an MCP (Model Context Protocol) Server, Agent Gateway connects AI agents to trusted, context-rich data and analytics in the Quantexa Platform. It facilitates:

• Data retrieval and orchestration: Agents can access the right data across the enterprise ecosystem, quickly and securely, at the time of decision, orchestrating queries, enforces access controls, and manages memory and prompts

• Context-first: By applying domain knowledge via entities, relationships, and graph analytics, the Gateway injects context into every agent step

• Action and governance: The Gateway automates and helps recommend actions at scale, with routing, guardrails, approvals, and immutable audit trails. This is key to compliance, explainability, and trust in autonomous systems

Quantexa: A data foundation for context engineering across multi-agent systems

In agentic AI architectures, agents must access and reason over external data sources that reflect the true state of the business. Quantexa’s Decision Intelligence Platform uses its Contextual Fabric. This is predicated on Quantexa’s:

• Entity Resolution which connects disparate data points to form holistic, 360-degree views of customers, businesses, organizations, etc

• Graph context: The platform builds dynamic graphs – including knowledge graphs - that visualize relationships and connections, revealing hidden patterns and enabling agents to reason over complex networks.

Context is revealed and actioned through Contextual Analytics, elevating explainable, auditable and actionable insights.

Fig. 1. Quantexa’s Agent Gateway: Powering Context Engineering with Trusted Data

The Quantexa advantage: Consistency, flexibility, and trust

What sets Quantexa apart for grounding context engineering with your organization’s knowledge?

• Consistency: All agents, however complex the system, operate from the same, up-to-date information, drawn from across your organization’s data sources

• Flexibility: The Platform supports many data types, sources, and use cases, from financial crime detection to customer intelligence and risk management

• Scalability: Quantexa’s architecture is proven by the world’s largest organizations to handle billions of records, scalable to support complex, multi-agent workflows

• Explainability and governance: Decision and action is traceable, auditable, and explainable, key to compliance and trust in autonomous systems

AI amplifies how organizations can make timely, data-driven decisions from a decision-first standpoint. AI-driven systems can analyze vast datasets, recognize patterns, and anticipate future scenarios in support of decision requirements. They automate the easy, and recommend actions when human intervention is needed, while adapting to changing business contexts and business rules.

Be AI-ready for the real world

As AI systems become more autonomous and agentic, context engineering will help define the next era of enterprise AI, supported and grounded in an organization’s data estate with the Quantexa Platform and the Agent Gateway. With Quantexa, organizations can build AI systems that are not just powerful, but responsible, explainable, and ready for the real world.

If your organization is looking to move beyond automation and into the realm of autonomous and context-aware decision-making, Learn More. Request a Demo