ESAA-Conversational: An Event-Sourced Memory Layer for Continuity, Handoff, and Curation Across Heterogeneous LLM Coding Agents

• URL: http://arxiv.org/abs/2606.23752v1
• Date: Mon, 22 Jun 2026 01:02:24 GMT
• Title: ESAA-Conversational: An Event-Sourced Memory Layer for Continuity, Handoff, and Curation Across Heterogeneous LLM Coding Agents
• Abstract summary: This paper presents emphA-contextal, a domain of EventSourcing Agent Architecture (ESAA)<n>We show that heterogeneous agents can collaborate through a shared log without a direct agent-to-agent channel, while the public distribution preserves privacy by excluding the private conversational history.<n>A self-referential case study with 570 development-lab events shows that heterogeneous agents can collaborate through a shared log without a direct agent-to-agent channel, while the public distribution preserves privacy by excluding the private conversational history.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software developers increasingly work with multiple LLM coding agents, switching among tools such as Codex, Grok, Claude Code, and other assistants as context windows fill, sessions end, or a particular agent becomes better suited to a subtask. Each agent, however, persists its conversation in a private and vendor-specific log. The result is conversational state drift: goals, decisions, open tasks, and rationales established with one agent are not reliably available when another agent takes over. This paper presents \emph{ESAA-Conversational}, a domain specialization of Event-Sourcing Agent Architecture (ESAA)~\cite{esaa} for shared conversational memory across heterogeneous agents. The method treats the visible conversation as a local event store: hooks and watchers capture visible turns, normalize them into an append-only \texttt{activity.jsonl}, and deterministically project read models such as \texttt{handoff.md}, \texttt{state.md}, \texttt{decisions.md}, and \texttt{tasks.json}. Mechanical capture does not require LLM inference; agents use judgment only for explicit curation, recording durable decisions and conversational tasks through domain commands. The public v1.1.0 release implements a PowerShell CLI with \texttt{init}, \texttt{enable-hooks}, \texttt{sync}, \texttt{project}, \texttt{verify}, \texttt{context}, \texttt{decide}, and \texttt{task}; includes \texttt{workspace\_root} isolation and a write-path lockfile; and is distributed as a greenfield package with an empty public log. A self-referential case study with 570 development-lab events shows that heterogeneous agents can collaborate through a shared log without a direct agent-to-agent channel, while the public distribution preserves privacy by excluding the private conversational history.

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