Stop building blind agents; fix your context problem first

Agentic AI does not fail because the models are weak or the technology is unproven. Agents stumble because you haven’t taught them the realities of your business well enough. And we mean your business—not some mythic, at best, generic “best practice” version of business. Before you throw in the AI towel, invest in context capture.

Most enterprises are deploying AI agents into process chaos—fragmented data, inconsistent workflows, and siloed logic. According to Skan AI Co-founder Manish Garg, that means agents are flying blind. He posits that even if large language models (LLMs) could do 95% of the work we need doing, without the final 5% of enterprise-specific context, they are next to useless.

Cut through best practice BS by observing the reality of work in your organization

Skan AI’s Observation-to-Agent platform watches how humans actually do their work—creating context from every click, screen, and hand-off. It takes that observation and makes it a model of your real process. These models give agents the context they need to operate in reality, often some distance from the idealized workflows of “best practice” BS.

This context layer becomes the foundation for agentic systems to reason, act, and adapt. Consider a physical robot trained to maneuver around a perfectly tidy, pet and human-free home. Then imagine letting it loose in a reality of abandoned bags strewn across hallways, pets scampering around, and people moving freely. That’s the difference between idealized workflows and context-powered workplace reality and between agents working and agents failing.

A map of reality will overcome how little you know about your own processes

Rolling out agents often reveals to enterprise leaders how little they know about how work gets done in their organization. Skan AI’s approach tackles that and provides a map of that reality.

There’s also good news for those fearing a demand for perfect data before making progress with AI. By taking an observation-first approach, you can get started even when data is scattered across dozens of applications.

Cut processing time, even when your data is all over the place

A major group benefits provider applied Skan AI’s observation-first approach to tackle challenges in their claims review process. Eligibility specialists have to determine coverage eligibility, which traditionally involves manually reviewing medical and non-medical claims across a range of applications, including enterprise systems, web apps, Excel files, and scattered SharePoint documents. Each claim presents unique circumstances that can’t be handled with typical rules-based automation. The nearly infinite permutations of coverage meant specialists had to spend hours searching for the right documentation to verify coverage.

Skan AI’s platform captures how specialists perform the work, enabling AI agents to learn from real human expertise to review claims. Those AI agents learn from observing how to navigate multiple systems, and then that context is applied to support the decisions they make—reducing processing time while maintaining accuracy.

The Bottom Line: Don’t build another agent until you can teach them the gritty reality of your organization.

Train agents on the raw reality of your organization. Ask yourself if your agents understand how your business really runs. If the answer is no, don’t deploy another agent. Invest in tech that can observe and record reality to give you the context you need, not idealized guesswork configured in a wall full of post-it notes.