Sharing a different Research Architecture for AI Agents to inspect and tackle known bottlenecks for running autonomous agents. Feedback?

Reddit r/AI_Agents Papers
research ai-agents transparency runtime inspectability architecture

Summary

A new research architecture for AI agents is introduced, centered on a transparent runtime where every interaction becomes a replayable execution trail with full inspectability, including plan, execute, observe, verify, and remember stages.

This is a research project based on experiments done on another project which has implementations and ideas that became research papers, all links in comments. A Transparent AI Language Runtime where every AI interaction becomes a replay-able execution trail. Plan. Execute. Observe. Verify. Remember. Nothing is hidden. Everything is inspectable. A fresh, standalone backend where every user turn is a replayable trail: InputEnvelope -> context (provenance per item) -> gate (pause/edit) -> plan -> act/observe loop (tools, MCP, skills) -> verify (revision route) -> respond -> memory commit (notes + facts + synopsis) Every transition = one typed event in an append-only log TrailProjection: timeline + node/edge graph, per inspection level No hidden state: what the gate shows is exactly what the model sees, and the whole run reconstructs from the event log alone. #ai #harness #harness_engineering #ai_runtime #runtime #ai_agent #agent #llm #language_runtime #transparent_ai_agent
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