Building an AI agent is no longer the difficult part. The real test begins when that agent is put to work inside an enterprise, with access to company data, customer conversations, business applications, and the ability to take actions on its own.
That has opened up a new set of problems around testing, data, governance, orchestration, and integration. Some of the more interesting work in agentic AI today is happening in these areas, away from the race to simply build more agents. Here are three companies working on different parts of that puzzle.
1. CRMIT Solutions: Making AI Agents Better Decision-Makers
Giving an AI agent the ability to act is one thing. Giving it enough business context to decide what action makes sense is a much harder problem. CRMIT Solutions is approaching agentic AI through Decision Intelligence, connecting enterprise data, business context, AI-driven recommendations, and agents that can turn those decisions into actions. Its AImplifai practice has developed agents across functions including sales, service, healthcare, and enterprise operations.
The company is also working on what happens before those agents are trusted with live decisions. Agent Crucible, developed by its AimplifAI Lab, tests agents against multi-turn conversations, edge cases, and unexpected scenarios to identify hallucinations, logic gaps, and guardrail failures. Together, the two capabilities point to a broader question for enterprise AI: it is no longer enough for an agent to act autonomously; it also needs to make the right call.
2. Snowflake: Giving Agents Better Data to Work With
AI agents may be getting smarter, but poor enterprise data can still make them confidently wrong. Snowflake is tackling the data side of the equation, using its existing position in enterprise data infrastructure to give AI agents access to governed business information and context.
Cortex Agents brings structured and unstructured data together so agents can retrieve information, analyse it, and generate responses grounded in company data. The significance here goes beyond retrieval. Agents expected to make or recommend business decisions need to know which information they can trust and which data they are actually allowed to use.
3. UiPath: Moving From Reasoning to Doing
Knowing what needs to be done and actually doing it are two very different things inside a large organisation. A simple task can involve an ERP platform, an old desktop application, several APIs, an approval from an employee, and a process that was designed years before generative AI existed.
UiPath is using its automation roots to bridge that gap. Its Maestro platform orchestrates AI agents alongside software robots and human workers, allowing each to handle the part of a process it is best suited for. It is a practical take on agentic AI: rather than replacing years of enterprise automation, make agents work with it.
The Hard Part Comes After the Build
The agentic AI market is moving quickly past the novelty of an AI system that can take an action on someone’s behalf. The harder questions are now about what happens when thousands of those actions take place across real organisations, involving real customers, company data, financial systems, and business decisions.
Testing, trusted data, orchestration, governance, interoperability, and distribution may sound less exciting than the agent itself. They could turn out to be the technologies that decide whether agentic AI actually works at enterprise scale.
