AI agents are no longer just answering our questions — they’re getting things done.

They’re accessing data, calling tools, creating files, and even supporting real business decisions. But as we give AI more autonomy, there’s one question we can’t ignore:

How do we know we can actually trust what it did?

Today, we’ll look at how we can move from simply observing AI agents to actually proving their actions and outcomes — with evidence that people can review, verify, and trust.

1. AI agents are advancing faster than enterprise oversight
AI agents are increasingly being used to perform real business tasks, from accessing data and calling tools to creating files and making recommendations. However, the challenge is not simply whether these systems generate logs, but whether their final outcomes can be traced back to reliable evidence. The presentation highlights a growing governance gap: Gartner forecast that 40% of enterprise applications could include task-specific AI agents by the end of 2026, while reported AI incidents and concerns around inadequate access controls show why stronger oversight is becoming increasingly important.

2. Stardive proposes an evidence layer for AI agents
Stardive is positioned as an independent audit layer that works alongside existing AI agents rather than requiring organizations to rebuild their systems. Its central concept is an execution receipt that captures and connects an agent’s actions, tool calls, sources, approvals, files, and resulting artifacts. Importantly, missing or weak evidence is intended to remain visible rather than being hidden behind a polished AI-generated explanation. This gives engineers, auditors, risk teams, and business owners a clearer way to understand what happened, verify important claims, and decide whether an AI-generated outcome should be trusted or approved.

3. Moving from AI observability toward provability
The proposed differentiation is the focus on cross-agent, reviewable, and evidence-gated records. Existing platforms already provide observability, evaluation, monitoring, and governance capabilities, while Stardive’s intended niche is a run-level evidence receipt connecting actions, approvals, artifacts, and outcomes for human review. The potential value is faster investigations and clearer sign-offs for users, a control layer spanning different agent technologies for enterprises, and potentially a horizontal infrastructure opportunity as organizations deploy agents from multiple vendors and frameworks. These benefits remain hypotheses to validate through pilots rather than established production results.