Kentron AI vs generic AI agent frameworks
General-purpose agent frameworks are built for flexibility: wire up any model, any tool, any chain of steps. That flexibility is also the risk — the framework itself has no opinion on what an agent should be allowed to touch, and no built-in record of what it actually did once it ran.
Where Kentron AI differs
- A single governed gateway checks every model call and every action against policy
- Connectors and skills come from one reviewed registry, not whatever's wired in ad hoc
- Every conversation and action produces an auditable receipt, not just application logs
When a generic framework is still the right choice
If you're prototyping a single agent for internal experimentation, with no sensitive data or systems in reach, a general framework's flexibility is a real advantage. Governance matters once that agent starts touching production data or making decisions that affect customers.