Enterprise generative AI is shifting from conversational interfaces to autonomous, task-oriented agents, and that shift is exposing a new bottleneck. Model reasoning is no longer the primary constraint on production AI: operational trust, data governance, and output reliability now decide whether an agent moves from a pilot into daily use. Futurum Research finds that 55.4% of enterprise technology leaders name agent reliability and hallucination management as their top hurdle to scaling generative AI, and 51.2% of organizations building internal AI applications report ongoing data quality and availability challenges.
Frontier models such as Anthropic’s Claude reason with precision over syntax and unstructured text, but they have no native awareness of an enterprise’s data lineage, system-of-record authority, or real-time business definitions. Closing that gap means decoupling deterministic enterprise context from probabilistic model reasoning, and grounding agents in active metadata delivered through open, standardized protocols rather than point-to-point code or unconstrained retrieval.
In its latest thought leadership brief, Elevate AI Agent Quality with Active, Intelligent Context: How Informatica Plugin Grounds Claude Reasoning in Trusted Data, completed in partnership Salesforce, Futurum Research examines why unconstrained retrieval and application-tier AI fall short in production, and how a governed, headless control plane resolves the agentic trust deficit.
In this brief, you will learn:
- Why 76.2% of organizations with active agentic AI interest are running the Model Context Protocol (MCP) in production, piloting it, or strongly considering it for future projects
- How naive vector retrieval and unconstrained SQL generation introduce context window saturation, semantic guesswork, and security and lineage gaps
- How a governed AI control plane pairs Claude’s reasoning with certified business glossaries, master data management, and point-of-entry data quality and lineage checks
- A four-phase blueprint for standardizing semantic definitions, benchmarking governed retrieval against naive approaches, configuring identity-aware security, and monitoring FinOps observability
If you are interested in learning more, be sure to download your copy of Elevate AI Agent Quality with Active, Intelligent Context today.
Author Information
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
Benjamin Brown is Vice President of Custom Research at Futurum, where he leads the operationalization and execution of research projects and business value analysis for technology vendors, channel partners, and buyers. He believes research only earns its value when it reaches people quickly and guides their actions. The insight has to be genuinely worth knowing and rigorously proven; the context and relevance have to be strong enough that readers immediately grasp what it means for them; and the work has to be easy to find, interpret, and apply, whether the reader is a human or an AI. Unread research changes nothing, and ignored decks and presentations move no one. Research matters only when it is discovered, understood, and acted on.
