Beyond the Lab: Solving the Context Gap to Scale Enterprise AI Agents

In the world of Enterprise AI, we are currently living in a tale of two realities. On one hand, the “demo” has never looked better. We see Agents that can effortlessly draft emails, summarize meetings, and even write code. On the other hand, the vast majority of these high-potential Agents are stuck in the “lab” phase, struggling to make the jump into production environments where they can provide actual ROI.

Why is the gap between a successful pilot and a production-grade deployment so wide?

According to Rowan Trollope, CEO of Redis, the problem isn’t the intelligence of the models — it’s the context. As covered in The Register, Trollope highlighted that for an AI Agent to actually replace or augment a human decision-maker, it needs access to the “judgment data” that currently lives across a wide array of different systems.

The Pricing PDF Fallacy

Starting an AI journey by feeding an Agent a library of PDFs — product manuals, pricing sheets etc — is relatively straightforward, but whilst this allows an Agent to answer basic questions, it fails the moment the Agent needs to apply nuance.

Consider Trollope’s example of a customer service Agent deciding whether to offer a discount. A pricing PDF might give the Agent the standard rates, but it won’t tell the Agent why a human manager gave a 20% discount to a similar customer last week.

“What you need is to find out where the humans apply their judgment and what data they used to make that decision,” Trollope told The Register. “It’s sitting in Slack threads, in email chains, in text messages. That’s where pulling that data together is difficult.”

The Industry Consensus: Agents Need Context Not Just Brains

Trollope isn’t the only one sounding the alarm on the context bottleneck. As the industry matures, the consensus is shifting: the value of an Agent is directly proportional to its connectivity.

Andrew Ng, a pioneer in AI and founder of DeepLearning.AI, has spent much of the last year championing “Agentic Workflows.” He argues that the next leap in AI performance won’t come from larger models, but from Agents that can iteratively use tools to find information.

“For an Agent to be truly useful, it must be able to reason across a variety of tools and data sources. The challenge today isn’t just the reasoning engine; it’s the plumbing — ensuring the Agent has a reliable, secure way to reach into the systems where the work actually happens.” — Andrew Ng, The Batch, 2024/2025.

Context control at the API level matters too. Unified APIs strip agents of field-level control, returning all fields whether the agent needs them or not.

Similarly, Marc Benioff, CEO of Salesforce, has frequently emphasized that “an Agent without data is just a chatbot.” During the launch of Agentforce, Benioff noted that the primary reason enterprise AI projects fail is due to “data silos” that prevent Agents from seeing the full picture of a customer’s journey across sales, service, and marketing.

The “Integration Tax” is Killing Agent ROI

The reality of production-grade Agents is that they open up a massive surface area for integrations. To solve a single customer query, an Agent might need to:

1. Check a contract in Docusign
2. Review past customer communication history across Intercom, email, and WhatsApp for Business to build the necessary context
3. Search through CRMs (Salesforce, HubSpot) or HRIS platforms (Workday, SAP SuccessFactors, Oracle) to understand the necessary approval flows for a given discount
4. Scan Slack threads for recent internal updates

And this is all for one relatively simple Agent for a tightly defined use case.

Building these connectors individually is a huge task, meaning engineering teams face a “connectivity tax,” spending significant time and effort on API authentication, data mapping, and maintenance, rather than on the Agent’s core logic.

This is exactly why so many projects remain “lab experiments.” The cost of building and maintaining the “long tail” of third-party integrations simply outweighs the immediate benefits of the AI.

How StackOne Breaks the Lab Cycle

At StackOne, we believe that connectivity should be a given, not a barrier. We provide the deep agentic connectors needed to give your Agents the context they need to move into production, with connectors that meet the needs of ever-evolving patterns of agentic communication:

• MCP (Model Context Protocol)
• AI SDKs (LangChain, Pydantic, Vercel, Google ADK)
• A2A (Agent-to-Agent)
• RPC (Remote Procedure Call)

Getting Out of the Lab

The message from industry leaders is clear: Context is the new currency of AI. If your AI Agents are currently limited to just reading the manual, they will never achieve the level of judgment required for high-value enterprise tasks.

To move your Agents out of the lab and into the hands of your customers and employees, you need to bridge the gap between the model and nuanced context residing in dozens of disparate systems.

Is limited 3rd-party connectivity keeping your AI Agents in the lab? Get started with StackOne for free today.

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Citations & Further Reading:

• Trollope, R. (2026). Interview with The Register: AI Agents and the Redis Perspective
• Ng, A. (2024). The Power of Agentic Workflows. DeepLearning.AI.
• Benioff, M. (2024). Keynote Address at Dreamforce: The Future of Agentic AI.