Multi-Agent Workflows & The Right Data Foundation for The Next Evolution of Enterprise AI

Author(s): Tobi Beck

Originally published on Towards AI.

Single AI agents are hitting enterprise limits, but multi-agent workflows unlock 3–5x better performance through specialized collaboration — if you solve the data foundation challenge first.

While most enterprises are still figuring out single-agent implementations and “basic” AI workflows, early adopters are already discovering fundamental limitations that explain why currently 95% of enterprise AI pilots fail [1]. These failures stem from core architectural constraints: single agents experience significant performance degradation when handling increased context size — even when the additional context is irrelevant to the target task [2] — and their effectiveness diminishes when stretched across multiple responsibilities due to context-switching overhead that compounds as complexity grows.

But the next wave is here: multi-agent systems with specialized agents working together, each with domain expertise. The advantage is practical: specialized agents that collaborate seamlessly, exchange insights in real time, and deliver compound intelligence that scales with business complexity. Financial projections that automatically trigger supply chain adjustments. Customer behavior analysis that immediately informs product roadmaps. Strategic planning that connects market intelligence to operational execution in real-time.

The technical foundation for this is already proven — multi-agent systems are already delivering measurable improvements in enterprise workflows. In fact, multi-agent systems show 3–5x improvement in complex task completion compared to single-agent approaches [3], and it is estimated that 25% of enterprises will deploy autonomous AI agents by 2025, doubling to 50% by 2027 [4].

But here’s where most multi-agent implementations break down: without the right data foundation and grounding within enterprise context, you’re building smart systems that work in isolation. The collaboration advantage disappears when agents can’t access the same information, creating the very silos that these multi-agent architectures are meant to solve.

The Evolution from Single to Multi-Agent

Why Single Agents Hit Limits

Single agents face fundamental limitations that become apparent at enterprise scale. Non-deterministic behavior creates reliability issues where identical inputs can produce completely different approaches. Cascade failures occur when early mistakes compound through execution chains, and improper termination conditions can trap agents in loops.

Beyond technical limitations, single agents struggle with scope. For single agents trying to master too many tasks at once, context-switching overhead grows exponentially as complexity increases.

The Multi-Agent Promise

Multi-agent architectures solve this by combining specialized expertise with parallel processing. Each agent focuses on what it does best — financial modeling, supply chain optimization, customer intelligence (or mere subtasks thereof) — while collaborating with other specialists.

The advantages are compelling: specialized agents working simultaneously rather than sequentially, resilient architectures where individual agent failures don’t collapse the entire system, and exponential capability growth as you add new specialized agents. This is all supported by major tech companies investing heavily in multi-agent frameworks.

Where Multi-Agent Implementations Break Down

But here’s the reality most organizations discover: without the right data foundation, you’re not building collaborative intelligence — you’re building expensive, isolated agents that can’t actually work together, or even do their own task properly.

When your financial forecasting agent works from yesterday’s CRM export while your supply chain agent uses last week’s inventory data, collaboration becomes impossible. When agents can’t access the same information sources, you get conflicting recommendations and fragmented insights.

The multi-agent advantage disappears when agents work in data silos. This is where most implementations fail, and why solving data access becomes the foundational requirement for multi-agent success.

The Technical Foundation: Unified Data Access as the Multi-Agent Enabler

In most enterprises, AI implementations make or break based on AI-ready data. This context is critical to understand for the next generation of agentic systems. According to Gartner research, organizations that don’t enable AI use cases through AI-ready data practices will see continued failure rates of over 60% for AI projects in the future [5].

Not surprisingly, over 75% of organizations state that AI-ready data remains one of their top five investment areas for the next 2–3 years [5]. The advent of multi-agent workflows is only going to amplify these challenges exponentially through further strain on data infrastructure.

Why Every Agent Needs Universal Data Access

Multi-agent collaboration only works when every agent can access any relevant data instantly. Traditional architectures fail here because they require pre-built integrations, data movement, and complex access provisioning for each new agent.

The solution requires zero-copy federation that provides every agent instant access to any data source without movement or duplication. When agents can query across Salesforce, Snowflake, Databricks, and Oracle seamlessly, collaboration becomes natural rather than engineered.

Real-Time Context Sharing

But agents need more than just raw data access — they need shared understanding of what the data means. Multi-agent systems represent the ultimate contextual challenge — multiple agents with different specializations need access to overlapping but distinct data contexts simultaneously. But by default, most data is not contextualized because this context lives across different places of the enterprise. The data platform might have technical metadata such as the schema and columns names, but definitions might live in your data catalog, and your semantic modeling in your BI tool. This requires a unified metadata layer that provides consistent definitions, relationships, and business context across all sources. When one agent works with a different definition of churn, entire workflows will break and trust will erode.

Governance That Scales with Agents

More agents mean more data access points. Traditional approaches that try to govern data at each individual source create an exponential management nightmare — imagine trying to maintain consistent privacy policies across 50 data sources for 20 different agents.

Thus, enterprises will need a central access point through which agents will consume their data. Policy-driven access control through a unified data fabric layer means each agent automatically operates within appropriate boundaries while accessing data from a single, governed interface. This centralized approach scales because governance policies apply consistently regardless of which agent requests access, how many agents are collaborating, or how many underlying data sources are involved.

The alternative — managing governance at every individual system — becomes impossible at multi-agent scale, creating the very compliance chaos that derails AI initiatives.

Building Your Multi-Agent Future

Start with Data Unity

The foundation of successful multi-agent workflows isn’t the agents themselves — it’s the underlying data architecture that enables their effective collaboration. Organizations succeeding with multi-agent architectures solve data access first, then layer on agent capabilities.

Without the right data architecture, you’re building smart agents that work in isolation. With it, you’re creating collaborative intelligence that compounds with each additional agent.

The Architecture Requirements

A successful multi-agent data architecture needs several key components:

Instant federated access to any data source without moving or duplicating information. This eliminates the bottlenecks that slow agent collaboration and ensures all agents work with the same, up-to-date data.

Unified context and governance that provides consistent metadata, definitions, and access policies across all data sources. This prevents the miscommunication that breaks multi-agent workflows.

Real-time collaboration capabilities that allow agents to share insights and coordinate actions without complex integrations or manual handoffs.

For example, imagine a customer service AI agent that needs comprehensive customer history to resolve a complex issue. Instead of being limited to CRM data, it can instantly federate information from billing systems in SAP, product usage logs in Google Big Query, and recent transaction data in Amazon Redshift — all through a single query, with appropriate governance controls applied automatically. The customer service agent gets a complete picture in seconds, not the partial view that creates frustrating customer experiences.

This enables seamless collaboration between specialized agents across your enterprise data landscape, where each agent can access exactly the data context it needs without the traditional barriers of data silos or complex integrations.

The Competitive Opportunity

Organizations that master multi-agent workflows will have significant advantages in complex decision-making, integrated planning, and automated compliance. These capabilities become increasingly critical as business complexity grows and competitive pressures intensify.

The question isn’t whether multi-agent AI will transform enterprise operations — it’s whether your organization will lead or follow this transformation.

The Path Forward

Multi-agent workflows represent the next evolution beyond single AI agents, but only when built on unified data foundations. Enterprise applications require specialized agents collaborating over shared, governed data access — not isolated intelligence working from disconnected datasets.

The technical foundation exists today. Competitive advantage goes to organizations that solve contextual and unified data access via a data fabric architecture first, then scale their agent capabilities on that foundation.

The future of enterprise AI isn’t just smarter agents — it’s agents that can truly work together. But that future requires rethinking your data architecture from the ground up, building systems that enable collaboration rather than enforce silos.

Organizations ready to make this foundational investment will discover that multi-agent AI isn’t just an incremental improvement — it’s a fundamental transformation in how intelligence works at enterprise scale.

References

[1] A. Challapally, C. Pease, R. Raskar, and P. Chari, “The GenAI Divide: State of AI in Business 2025,” MIT NANDA, 2025.

[2] K. Narasimhan et al., “𝜏-Bench: Benchmarking AI agents for the real-world,” arXiv preprint arXiv:2406.12045, Jun. 2024. [Online]. Available: https://sierra.ai/blog/benchmarking-ai-agents

[3] C. Bronsdon, “Benchmarks and Use Cases for Multi-Agent AI,” Galileo AI Blog, Mar. 25, 2025. [Online]. Available: https://galileo.ai/blog/benchmarks-multi-agent-ai

[4] T. Tully, J. Redfern, and D. Xiao, “2024: The State of Generative AI in the Enterprise,” Menlo Ventures, Nov. 20, 2024. [Online]. Available: https://menlovc.com/2024-the-state-of-generative-ai-in-the-enterprise

[5] E. Zaidi and R. Edjlali, “A Journey Guide to Deliver AI Success Through AI-Ready Data,” Gartner Inc., Research Report G00836469, Jul. 11, 2025. [Online]. Available: https://promethium.ai/resources/gartner-report-journey-guide-ai-ready-data

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Published via Towards AI