Who watches the watchmen? A public decision track record for AI agents
Agent-to-agent auditing creates a reputation layer LangSmith doesn't have.

Captures rejected agent decisions, not just executed actions—Langfuse doesn't do this.
Teams running LangGraph or OpenAI Agents in production with compliance requirements
Langfuse · Arize Phoenix · Helicone
Agent-to-agent auditing creates a reputation layer LangSmith doesn't have.
Applies MIT CSAIL research patterns to make AI agent decisions auditable and traceable.
Decision logs and task graphs bring engineering process to chaotic AI coding sessions.
Recording what an agent considered — not just what it executed — is a tidy, concrete insight. GhostTrace already gives record/replay commands, a .ghost.json schema and a --show-phantoms terminal replay so you can inspect rejected actions and the agent's reasoning. The thing that will decide if this takes off is integrations (LangChain/OpenAI Agents/CrewAI) and the promised web/VS Code UIs; without those it's a very useful niche tool, not yet a platform.
Ed25519 signature verification in browser solves agent accountability for disputes.
Prevents AI agents from re-litigating rejected architectural decisions across sessions.