Original Summary

Hey everyone, I've been experimenting heavily with multi-agent workflows using LangGraph and Llama 3.1 (via Ollama)locally. While building, I ran into two massive architectural issues that standard prompts couldn't fix: Parser Differentials: Agents sometimes format markdown or inject unwanted keys into JSON tool calls, causing parser bypasses. State Desynchronization:Asynchronous processing caused worker memory caches to go stale, leading to race conditions. To solve this, I built a lightweight, self-healing pipeline featuring: A Canonical Schema Guardrail that validates strict dictionary schemas and routes errors back to the orchestrator for self-correction loops. An Optimistic Concurrency Control (OCC) node that auto-re-syncs stale agent states instead of crashing. I wrote a benchmark script (experiment.py) that tests normal requests, adversarial key injections, and temporal lag, and it passes cleanly. If you're building multi-agent systems and want to check out the code or run it locally, here is the repo: 🔗 [ https://github.com/2002bish/agent-security-research.git\](https://github.com/2002bish/agent-security-research.git) Feedback, critique, or pull requests are very welcome!   submitted by   /u/unorthodox_43 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/2 22:13:05