How Unified Context Turns AI Into Real Enterprise Performance - with Ravi Marwaha of Arango

2026-06-10 · Source: The AI in Business Podcast · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

Ravi Marwaha, COO & CTPO at Arango, highlights that enterprise AI agents fail in production not due to model limitations, but from lacking a live, temporally aware context layer grounded in current business states. He advocates treating context as infrastructure, rather than a data pipeline issue, to enable agents to reason accurately, explain decisions, and deliver measurable outcomes. Arango's multi-model data platform integrates various data types to provide consistent, governed context. The discussion covers five practical frameworks for CIOs and chief data officers on building real-time, explainable context layers atop existing enterprise systems. Examples include Zscaler's customer support, semiconductor chip design, and PSI's clinical trial site selection, demonstrating how unified context improves relevance, accuracy, and explainability without requiring infrastructure replacement.

Key takeaway

For CIOs and Chief Data Officers deploying enterprise AI agents, prioritize defining the specific business context and required information before focusing on models or data pipelines. Treat context as an always-on infrastructure, integrating it atop existing systems rather than attempting costly rip-and-replace initiatives. Crucially, design for explainability from the outset, ensuring every agent decision is traceable and auditable to build trust and defend ROI in production environments.

Key insights

Enterprise AI agent failures are primarily due to a lack of live, business-grounded context, not inherent model limitations.

Principles

• Agents must "know" rather than "guess" for critical decisions and actions.
• Explainability is an engineering requirement, not an optional feature.
• Context must be treated as an always-on, evolving infrastructure.

Method

Start by defining the specific business context and required information for optimal outcomes, then work backward to data and pipelines. Build a unified context layer on top of existing systems, integrating and resolving entities across them.

In practice

• Design context systems to stay current with change data capture and event-driven updates.
• Prioritize explainability by ensuring every AI decision can be programmatically reconstructed and traced.
• Build for scale, anticipating multiple agents and exploding data volumes.

Topics

• Enterprise AI Agents
• Contextual AI
• Multi-model Data Platforms
• Knowledge Graphs
• AI Explainability
• Real-time Reasoning

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Architect, MLOps Engineer

Related on AIssential

• What Is a Context Graph — and Why Is Everyone Talking About It? — Context graphs provide machine-readable institutional memory, enabling AI to accurately interpret complex organizational knowledge and decision logic.
• How to Leverage Explainable AI for Better Business Decisions — Explainable AI transforms business data into actionable insights by revealing the "why" behind predictions.
• Data Products: The Essential Context for Enterprise AI — Enterprise AI agent success hinges on robust, architected context layers, not just advanced models.
• The context problem: Why enterprise AI needs more than foundation models — Enterprise AI requires deep institutional context to move beyond demos and deliver real production value.
• Why Enterprise AI Fails Without a Context Engine - with Eran Yahav of Tabnine — An enterprise context engine provides AI agents with crucial organizational knowledge, significantly boosting success rates and efficiency.
• People need to understand why systems make decisions, not merely whether the systems appear to perform well. A map of the Explainable AI (xAI) landscape. — Explainability is now a prerequisite for trustworthy AI, demanding tailored methods for diverse contexts.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.