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Mitigating Parser Differentials and State Desynchronization in Autonomous Multi-Agent Workflows
As autonomous multi-agent systems scale from simple sequential chains to dynamic multi-agent graphs (using frameworks like LangGraph), they encounter critical architectural vulnerabilities and failure vectors:
- Representation Gaps & Parser Differentials:Upstream agents generate semi-structured text, markdown blocks, or malicious/unauthorized payloads (e.g., unexpected arguments or instruction injections) that downstream parsers misinterpret, leading to silent failures or security bypasses.
- Temporal Gaps & State Desynchronization: Asynchronous workers operate at varying speeds, causing stale memory caches, race conditions, and cascading consensus errors.
This repository provides a production-grade, self-healing orchestration template built with LangGraph and Ollama (Llama 3.1). It implements a Canonical Schema Guardrail and Optimistic Concurrency Control (OCC) with Auto-Re-Sync to ensure structural integrity and state consistency across multi-agent executions.
Repository Structure
pipeline.py: The core self-healing multi-agent workflow featuring loop-back error correction, canonical schema validation, and state version locking. experiment.py: An automated benchmark suite testing standard requests, adversarial key injections, and temporal desynchronization. state.py: Shared state type definitions and data contracts.
Getting Started
Prerequisites
Python 3.10+ Ollama installed and running locally with the llama3.1 model.
Installation
- Clone the repository and navigate to the folder: bash cd agent-security-research Install the required dependencies: Bash pip install langgraph langchain-ollama langchain-core Ensure your local Ollama background server is active:Bashollama serve UsageRun the Self-Healing PipelineTo execute the primary multi-agent workflow with automated parser error correction and state re-syncing:Bashpython pipeline.py Run the Security Benchmark SuiteTo run empirical evaluations testing the pipeline against normal inputs, adversarial schema injections, and temporal lag:Bashpython experiment.py
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See what people are saying
README
Mitigating Parser Differentials and State Desynchronization in Autonomous Multi-Agent Workflows
As autonomous multi-agent systems scale from simple sequential chains to dynamic multi-agent graphs (using frameworks like LangGraph), they encounter critical architectural vulnerabilities and failure vectors:
- Representation Gaps & Parser Differentials:Upstream agents generate semi-structured text, markdown blocks, or malicious/unauthorized payloads (e.g., unexpected arguments or instruction injections) that downstream parsers misinterpret, leading to silent failures or security bypasses.
- Temporal Gaps & State Desynchronization: Asynchronous workers operate at varying speeds, causing stale memory caches, race conditions, and cascading consensus errors.
This repository provides a production-grade, self-healing orchestration template built with LangGraph and Ollama (Llama 3.1). It implements a Canonical Schema Guardrail and Optimistic Concurrency Control (OCC) with Auto-Re-Sync to ensure structural integrity and state consistency across multi-agent executions.
Repository Structure
pipeline.py: The core self-healing multi-agent workflow featuring loop-back error correction, canonical schema validation, and state version locking. experiment.py: An automated benchmark suite testing standard requests, adversarial key injections, and temporal desynchronization. state.py: Shared state type definitions and data contracts.
Getting Started
Prerequisites
Python 3.10+ Ollama installed and running locally with the llama3.1 model.
Installation
- Clone the repository and navigate to the folder: bash cd agent-security-research Install the required dependencies: Bash pip install langgraph langchain-ollama langchain-core Ensure your local Ollama background server is active:Bashollama serve UsageRun the Self-Healing PipelineTo execute the primary multi-agent workflow with automated parser error correction and state re-syncing:Bashpython pipeline.py Run the Security Benchmark SuiteTo run empirical evaluations testing the pipeline against normal inputs, adversarial schema injections, and temporal lag:Bashpython experiment.py
Similar repos
Languages
Python
100.0%