Run the software that changed.
Agents need a practical way to exercise an application, not just inspect the source they produced.
vitron.ai Why Vitron
AI agents can generate and modify software at increasing speed. That makes it more important to execute the result, test real behavior, evaluate outcomes, and give the agent evidence for its next step.
The gap
Run the software. Check the behavior. Learn from failures.Give each agent iteration evidence for the next change.
Agents need a practical way to exercise an application, not just inspect the source they produced.
Tests and evaluations help distinguish a plausible implementation from one that works for the task.
Structured results make it easier for an agent or engineer to decide what to inspect, accept, or change.
Infrastructure supporting the loop
Connect your agent. Control execution. Review the results. Alethia provides these capabilities throughout the loop.
Connect through MCP to exercise and verify a running web application.
Return run outcomes and evidence to inform the next development step.
Apply policy checks, local-origin boundaries, and human approval for high-risk actions.
Review signed run records, policy outcomes, and session replay.
Explore timing, token counts, and reproduction instructions.
Find your infrastructure fit.Review execution, controls, and evidence for your agent workflow.
Keep control over execution.Understand policy checks, human approval, and local boundaries.