The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems
📰 ArXiv cs.AI
Learn how to build auditable and forkable agentic systems using event-sourced reactive graphs, where the log is the primary source of truth
Action Steps
- Build an event-sourced reactive graph using a logging layer as the primary source of truth
- Implement a deterministic projection of the log to create a working graph
- Define behaviors as ordinary functions, classes, or LLM-backed reactions to events in the log
- Configure the system to support forking and auditing of agent actions
- Test the system's ability to maintain a consistent state across different forks and audit trails
Who Needs to Know This
AI engineers and researchers can benefit from this approach to create more transparent and flexible agent systems, while product managers can utilize this technology to improve the reliability of their AI-powered products
Key Insight
💡 The log is the primary source of truth, enabling auditable and forkable agentic systems
Share This
💡 Introducing ActiveGraph: a runtime where the log is the agent! Build auditable, forkable agentic systems with event-sourced reactive graphs #AI #AgenticSystems
Key Takeaways
Learn how to build auditable and forkable agentic systems using event-sourced reactive graphs, where the log is the primary source of truth
Full Article
Title: The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems
Abstract:
arXiv:2605.21997v1 Announce Type: new Abstract: Most agent frameworks are built around the language model: a conversation loop comes first, then tools, then rules, and finally a logging layer bolted on for observability, with state persisted as retrievable "memory." We describe ActiveGraph, a runtime that inverts this arrangement. The append-only event log is the source of truth; the working graph is a deterministic projection of that log; and behaviors--ordinary functions, classes, LLM-backed r
Abstract:
arXiv:2605.21997v1 Announce Type: new Abstract: Most agent frameworks are built around the language model: a conversation loop comes first, then tools, then rules, and finally a logging layer bolted on for observability, with state persisted as retrievable "memory." We describe ActiveGraph, a runtime that inverts this arrangement. The append-only event log is the source of truth; the working graph is a deterministic projection of that log; and behaviors--ordinary functions, classes, LLM-backed r
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