ESAA: Event Sourcing for Autonomous Agents in LLM-Based Software Engineering
Autonomous agents based on Large Language Models (LLMs) have evolved from reactive assistants to systems capable of planning, executing actions via tools, and iterating over environment observations. However, they remain vulnerable to structural limitations: lack of native state, context degradation over long horizons, and the gap between probabilistic generation and deterministic execution requirements. This paper presents the ESAA (Event Sourcing for Autonomous Agents) architecture, which separates the agent's cognitive intention from the project's state mutation, inspired by the Event Sourcing pattern. In ESAA, agents emit only structured intentions in validated JSON (agent.result or i...
| Authors | Elzo Brito dos Santos Filho |
|---|---|
| Primary category | cs.AI |
| Published | 26 February 2026 |
| Last updated at source | 26 February 2026 |
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