The era of AI agents has arrived. These intelligent software entities, capable of making autonomous decisions and taking independent actions, are rapidly evolving from experimental tools to essential components of the enterprise tech stack. Advances in Large Language Model (LLM) capabilities—including planning, reasoning, and problem-solving—combined with the use of existing software categories, have enabled AI agents to plan workflows, use tools, and act with minimal human prompting. This makes them far more powerful than traditional chatbots and conversational AI.  

At Pryon, we are bullish on the opportunities AI agents present for enterprise. And we’re not alone. Adoption is accelerating:  

• Gartner® projects that "by 2028, 33% of enterprise software applications will include agentic AI (up from less than 1% in 2024), enabling 15% of day-to-day work decisions to be made autonomously.”

• Deloitte predicts that 25% of enterprises using generative AI will deploy AI agents in 2025, rising to 50% by 2027.

• Langchain found that 51% of their survey respondents already had agents in production, with 78% planning on putting agents in production soon.

Even the number of Medium articles published on “AI Agents” is outpacing those on “Generative AI”—our favorite unofficial hype barometer.

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AI agents are finally making AI work for business

The agentic era will finally deliver on AI's promise of measurable ROI. Today, the ROI of generative AI is typically measured in terms of productivity improvements—valuable, but often intangible. AI agents change the equation by allowing organizations to automate entire tasks at a fraction of the cost, dramatically accelerating execution speed.

Multi-agent systems amplify this potential by enabling specialized AI agents to collaborate on complex tasks. Instead of relying on a single AI model, organizations can deploy domain-specific agents with specific horizontal or vertical expertise that work together to communicate and coordinate actions. These multi-agent systems divide work, cross-check results, and dynamically adjust plans—essentially creating a digital twin of an organization capable of orchestrating workflows far beyond the capability of any single model.

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By 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously.

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The business value is clear: companies can automate complex, multi-step processes, achieve faster and more consistent outcomes, and free up human experts for higher-level work. Early adopters report significant efficiency gains, such as using AI agents to streamline internal IT support or financial reporting. As a Deloitte report noted, AI agents are expanding generative AI’s applications across industries, unlocking previously impractical use cases.

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^{‍RECOMMENDED READING}
Agentic AI 101: What it is, Why it Matters, and How to Get it Right

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Think you’re ready for AI agents? Think again

Think back to the months before ChatGPT launched—what would you have done differently to prepare your organization for the generative AI boom? The agentic wave presents another opportunity to prepare and position for this step change.  

The #1 action you can take to ensure agentic readiness? Data readiness.

Enterprises generate vast amounts of data, yet much of it isn't immediately usable by AI agents—especially the troves of unstructured data (documents, emails, PDFs, images, etc.) that contain business knowledge. Studies show that up to 90% of enterprise data is unstructured, making it difficult for traditional databases and applications to use. And that’s not all:

• 91% of executives in a Harvard Business Review survey said a reliable data foundation is essential for successful AI deployment.

• McKinsey found that 70% of GenAI initiatives face challenges related to data, with only 1% of an enterprise’s important data reflected in today's models.  

• The Wall Street Journal cited reliability as the #1 concern for AI agent adoption—an issue closely tied to data quality and accessibility.

• Gartner® thinks that “lack of GenAI-ready data is the top reason for failed GenAI deployments. Product managers at data management software vendors must add capabilities to manage unstructured data so that it supports customers’ GenAI implementation, through in-house development and/or integrations and partnerships.”  

To tackle these challenges, organizations have adopted retrieval-augmented generation (RAG) pipelines to ground their AI agents in truth. However, the success of these pipelines hinges on two critical factors:  

1. The quality of the unstructured data they ground on  

2. The agent’s ability to access this data with the appropriate level of granularity

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The potential of AI agents is only as strong as the data that fuels them.

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Unlocking agentic AI’s potential starts with fixing your data problem

The uncomfortable truth for most organizations is that their data isn’t ready for AI agents. Decades of accumulating data across disparate systems have led to fractured, messy data environments. Many enterprises have siloed, outdated, and inconsistent datasets—a far cry from the pristine, real-time knowledge base AI agents require.  

A recent survey of IT leaders found that over 60% admitted to significant gaps in AI readiness due to shortcomings in their data ecosystem. The old adage applies: “garbage in, garbage out”. Poor-quality data leads to poor AI outputs, and even high-quality data that’s poorly refined can result in misleading conclusions (“great data in, garbage out” applies here).  

The three data roadblocks stopping AI agents in their tracks—and how to fix them

To prepare to welcome AI agents, organizations must confront three key data challenges:
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1. Siloed data and lack of integration

Data is scattered across multiple databases and file systems that don’t talk to each other. Different departments maintain isolated records, exacerbated by:

• Incompatible software

• Organizational boundaries

• Inconsistent data formats

AI agents (especially in multi-agent systems) need access to a shared, coherent view of enterprise data. If crucial information is locked in silos or restricted to specific SaaS tools, one agent might act on incomplete data while another agent sees a conflicting picture—enforcing your silos rather than breaking them down.

The reality:

• 40% of business-critical data is trapped in silos

• Data silos cost the global economy $3.1 trillion annually in lost productivity and opportunity

How to fix it

• Unify data sources: Just as you would integrate data for a RAG pipeline, create a unified view of structured and unstructured data sources. This may involve virtualization layers that let agents query multiple systems seamlessly.  

• Manage content updates: Agents require the latest, most accurate data. Implement continuous ingestion or real-time syncs so updates in one system instantly propagate.  

• Plan for multi-agent scalability: If each department deploys its own specialized agent, you’ll need an enterprise-wide approach to data availability—one source of truth, accessible by every agent with proper permissions.

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2. Poor quality data and metadata

Many organizations struggle with data hygiene. Duplicate records, missing fields, errors, and inconsistent formatting confuse automated processes. For example, one system records a date as 01/19/25 while another records it as January 19, 2025.  

Likewise, limited metadata on unstructured files makes it hard for an agent to know what information is relevant. For example, how do we know which of thousands of PDF reports an agent should consult if those files aren’t labeled or indexed with any context? The result is that an AI agent has to work with ambiguous or unreliable inputs (i.e. garbage in) so we’ll be left with poor or untrustworthy outputs (i.e. garbage out).

The reality:

• A Pryon survey of production agent builders found data preparation to be the #1 most challenging and resource-intensive part of the development process.

How to fix it

• Clean, normalize, and label: Just like RAG pipelines, agentic AI benefits from standardized and labeled data. Clear, accurate metadata also enables agents to quickly discover relevant information.  

• Build shared taxonomies and ontologies: Multi-agent systems thrive when they share a common schema or knowledge graph. Define standard business ontologies so that “HR Policies” or “Customer Records” are referenced uniformly across all agents. Consider a model layer to help sort existing content into these taxonomies.

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^{‍RECOMMENDED READING
‍}AI Success Through Data Governance: 7 Key Pillars

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3. Lack of real-time data flow

AI agents thrive on timely information—yet many enterprise data pipelines run on delays.  An AI agent making decisions on yesterday’s data might as well be guessing in the dark for fast-moving organizations.  

Agents need to find, filter, and act on data quickly—sometimes coordinating with other agents in real time. Clumsy search or slow indexing can bottleneck these interactions, causing missed opportunities or contradictory actions.

The reality:

• Many companies are burdened with legacy data architectures and technical debt that prevent real-time updates.

How to fix it

• Use hybrid indexing: Combine vector-based semantic search (for unstructured text) with traditional inverted indexes (for structured data). Agents can then retrieve the right piece of information by meaning, keyword, or metadata attributes.  

• Chunk documents wisely: Break large documents into manageable pieces for faster retrieval, ensuring your AI agents can zero in on relevant sections without being overwhelmed by entire PDFs or knowledge bases.  

• Design for updates: Agents may not only consume information but also generate or modify data, such as log files, transaction updates, and knowledge base additions. Plan for index updates or versioning so the entire agent data layer stays current.

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Two more agentic AI pitfalls to plan for

While not strictly related to data readiness, here are two more pitfalls to get ahead of when scoping your agentic AI implementations.

1. Orchestration

In agentic environments, data may not flow into a single retrieval pipeline and system of record. It could inform multiple agents that may pass tasks among themselves. Without robust orchestration, you risk data races (two agents updating the same record differently) or missed notifications (when an agent doesn’t know another agent has changed something).

How to get ahead

• Automate consistency checks: In multi-agent systems, define rules or triggers to detect conflicts (e.g. two agents scheduling different events for the same resource). Automated checks can flag issues before they propagate.  

• Implement role- and agent-based access controls: Just as RAG solutions include user-based permissions, AI agents need clearly defined scopes of authority. For example, a compliance agent may have wider read access but restricted write privileges.  

• Monitor and audit agent activities: Track which data source each agent accesses and what actions it takes. This is critical not only for debugging but also for security, governance, and regulatory compliance.

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2. Scalability

Enterprises rarely stop at a single agent. You might start with an IT support agent, then deploy a finance agent, then add a marketing assistant, and so on. Each new agent must securely and efficiently tap into your data environment without weeks of rework.

How to get ahead

• Adopt a modular data pipeline: Similar to cross-use case RAG planning, design your ingestion and indexing processes so they can be extended for new data types, domains, or agent functionalities.  

• Facilitate agent-to-agent communication: A shared knowledge layer ensures seamless information exchange for multi-agent systems. By posting updates to the same index/destination, each agent can inform others of new insights or completed tasks, avoiding duplication and maintaining consistency.  

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Laying the foundation for AI agent success

By taking a systematic approach—unifying disparate data sources, refining and labeling content, optimizing indexing, and integrating robust governance—you create an environment where AI agents can thrive. Whether you’re launching a single agent to automate a specific function or orchestrating a multi-agent ecosystem to tackle complex workflows, these data readiness steps form the foundation for success.

The payoff? Agents that can confidently act on up-to-date, trustworthy data, coordinating across teams and systems to drive transformative business outcomes. Data is the fuel that powers AI agents—invest in it now, or risk stalling in the race for AI-driven success.

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About the author

Josh Goldenberg is the VP of Product Management at Pryon. With over 15 years of experience in developing cutting-edge ML/AI applications, he has contributed across a broad spectrum of sectors including technology, financial services, telecommunications, manufacturing, media, retail, public sector, and intelligence. His career spans leading roles at large enterprises such as Google and Thomson Reuters, as well as significant contributions to innovative AI startups like Clarifai and public sector intelligence initiatives.