Agentic AI Solutions Stuck in Sandbox Environments Due to Lack of Context

Agentic AI solutions aren't scaling as expected, with many stuck in sandbox environments. This is largely due to a lack of context, according to Deepak Khosla, Chief Growth Officer at Impetus Technologies.

For many CTOs, the first question around agentic AI has been which model to use or how much to invest in fine-tuning, prompt engineering, and infrastructure. However, these questions aren't the full strategy.

“The real reason we still see so many agentic AI solutions sitting in sandbox environments, or moving into production only as chatbot-oriented use cases, is that more complicated AI solutions are missing something very key”

— context.

Context is what makes agentic solutions perform better, think better, take actions, and repeat actions - and do so in a uniform way. Without it, even the best model will struggle to do real enterprise work.

It's also a cost issue. Without the right context, enterprises may spend far more on memory, inference, tokens, and storage to get the same result. A strong context and semantic layer help agents perform better while making cost and FinOps easier to manage.

Many leaders still think of agentic AI as a smarter chatbot because many use cases so far have been chatbot-oriented. But the right agents, if built correctly, will take action, make a sequence of decisions, operate with some level of agency, and work continuously toward an outcome.

Morgan Stanley's work with OpenAI in wealth management is a useful example. The assistant was designed to help financial advisors access Morgan Stanley's vast intellectual capital, including hundreds of thousands of pages of research and commentary.

The challenge is that this context isn't AI-accessible. CTOs should work toward AI-ready data foundations while recognizing that well-designed agents can help navigate complexity across systems. The answer is balance. Modernize where needed, unlock context where possible, and make legacy knowledge usable without unnecessary disruption.

The goal is to make business and technology processes more AI-native, whether in underwriting, loan origination, or supply chain visibility. Agentic AI becomes powerful when the right building blocks, semantic layer, and controls are in place.

It won't scale through experimentation alone. It will scale when enterprises build the context, execution, and control layers that allow agents to do real work safely, securely, and repeatedly.

There are four key gaps that need to be addressed: the data gap, the semantic gap, the execution gap, and the trust gap. The data gap refers to information that exists but that agents can't reach. The semantic gap comes next, where even if all that data sits in a lake or warehouse, the data itself is meaningless unless the agent can interpret it.

The execution gap is whether the agent can make the right recommendation with the right constraints. The fourth is the trust gap, where if an agent makes a decision that nobody can explain, the recommendation may be ignored or nullified by a human in the loop.

To scale adoption, organizations need a trust layer that explains why an action was recommended or taken. Trust isn't a soft requirement - it's what separates pilots from production.

For agentic AI to scale, three layers must be in place, and they must be built in the right order. The first is the context layer, a context fabric that is semantically enriched, connected, governed, and representative of enterprise knowledge.

The second is the execution layer, which includes the agent runtime, orchestration tools, multi-agent coordination, and agent-to-agent trust that allow agents to execute work across workflows and systems.

The third is the control layer: guardrails, human-in-the-loop models, anomaly detection, compliance enforcement, and rules. An agent needs to know not only what it needs to do but also what it isn't permitted to do.

An enterprise AI control plane is all about governance and observability across the agents that an organization builds. It needs to manage identity and permissions, such as what data an agent can access, what tools it can use, what actions it can take, and when it must call in a human.

Auditability is essential. Every decision and action should have a decision trail that traces the context, steps, reasoning process, and action taken. Cost governance also matters because agents can become expensive if they aren't properly controlled.

Agentic AI solutions require the right context to scale. Without it, they will remain stuck in sandbox environments. CTOs must prioritize building the context, execution, and control layers to unlock the full potential of agentic AI.