Activity Log 2.0: From black box to open book

Delight.ai has been an early advocate for transparency in AI agent decision-making. Our Activity Log already provided visibility into agent responses, giving teams a foundation for understanding how agents behave. Now we're taking that transparency further with detailed reasoning paths that show exactly which knowledge sources, actionbooks, and tools shaped each response.

Explain AI responses an enhancement to Activity Log and part of Trust OS — delight.ai's suite of features designed to help customers confidently test, deploy, and continuously improve AI agents in production.

How it works

• Enhanced reasoning visibility: Go beyond basic activity logs to see the complete chain of knowledge sources, actionbooks, and tools behind each agent response.
• Confidence indicators: Low-confidence responses are automatically flagged when the agent is less certain, surfacing answers that may need review.
• Evidence-based verification: Trace the exact logic path to confirm agents are pulling from correct sources and following intended workflows.
• From observation to optimization: Move beyond monitoring to actively improving agent performance with detailed insights into decision patterns.
• Built for continuous improvement: Identify knowledge gaps, refine prompts, and optimize agent quality based on concrete evidence rather than assumptions.

Explain AI Responses deepens our commitment to transparency, giving our customers the detailed visibility they need to trust and improve their AI agents. As part of delight.ai's Trust OS, it helps teams verify accuracy, catch issues early, and continuously enhance agent quality with confidence.