XTrace
XTrace is a shared intelligence platform that captures lessons from human and AI agent work, turning them into reusable context for teams. It connects AI spend to shipped code and rework, then distills useful decisions into shareable "Brains" that guide future work. The platform helps engineering and go-to-market teams reduce repeated effort and improve agent guidance.
- Developer Tools
- Artificial Intelligence
- Enterprise Software
- Data & Analytics
- Software Only
Funding
Founders
Product
Problem
Companies using AI agents lack visibility into what their AI investment actually delivers, and lessons learned by one person or agent rarely transfer to the next. Teams repeat work, agents receive unclear guidance, and organizations struggle to connect AI spend to outcomes like shipped code and rework, leaving useful knowledge siloed in individual sessions.
Solution
XTrace provides a platform for shared intelligence that observes AI agent work, distills lessons from successful and corrected sessions, and makes that context available to both people and agents. It tracks AI spend against merged pull requests, rework rates, and human effort, giving teams actionable insight into where AI delivers value. The platform's "Brains" feature organizes decisions, facts, and procedures around accounts, projects, or teams, allowing teammates and agents to build on prior work rather than starting from zero. XTrace also captures corrections and exceptions, turning them into enforceable guidance for future agent workflows.
Target Audience
Engineering teams and go-to-market organizations that use AI agents and need to track ROI, reduce rework, and preserve institutional knowledge across team members and AI tools.
Features
- Session observation that links AI spend, tokens, and human messages to merged work and rework metrics
- Spend-to-delivery analytics that break down AI investment into new features, rework, tests, and exploration
- Brains that compile key topics, decisions, conventions, and lessons learned from multiple sources including agent sessions
- Cross-team comparison tables showing AI spend, cost per merged PR, and human messages per PR to identify best practices
- Session-level detail view showing agent activity, human interventions, context retrieved, and decisions made
- Rulebook enforcement that turns reviewed lessons into guidance and controls for agent workflows