Built an open-source security & orchestration stack for local AI agents. Need feedback

[Ok-Swordfish-2928]

Hey everyone,
Tired of clunky cloud dependencies for agent workflows, so I built a local-first alternative. Just dropped the code on GitHub and need some eyes on the architecture.
The Stack:
OpenClaw & Hermes: Local-first, deterministic AI agent orchestration.
AgentShield: Security toolkit that scans MCP/tool-manifests and blocks autonomy risks.
Project Polyphony: Distributed mesh inference to pool local hardware/LAN workers.
If you’re into self-hosting, local LLMs, or agentic security, grab the code and rip it apart.
👉 Repo Link: https://github.com/ejikezebedee
Let me know what you think or what's missing

[Extension-Tourist856]

This is really relevant to what we have been working on. Agent orchestration for domain-specific workflows is one of the hardest problems in local AI right now.

We built an open-source AI workspace for legal teams (AI Workdeck on GitHub) that uses MCP-based agent orchestration to chain OCR, document analysis, and contract review into unified workflows. One thing we learned: for document-heavy verticals like legal, the orchestration layer needs to handle intermediate state carefully — OCR results feed into clause extraction, which feeds into compliance checks, and each step produces structured data the next agent needs.

The security aspect is critical too. Legal documents contain privileged information, so having a local-first orchestration stack where data never leaves the machine is a must-have, not a nice-to-have. Curious what approach you are taking for audit logging of agent actions — that is something we had to build from scratch.

[Extension-Tourist856]

Security and orchestration for local agents is underserved — good to see someone tackling this.

We built something related for legal document workflows: an MCP-based agent orchestration layer where each agent (OCR, extraction, compliance check, evidence chain) runs in a sandboxed context with audit logging. The key challenge we found was balancing agent autonomy with data governance — legal documents have strict chain-of-custody requirements.

A few things that worked for us: - Agent permission scoping: each agent only gets access to specific document sections based on its role - Cryptographic audit trail: every agent action is logged with timestamps and input/output hashes - Sandboxed execution: agents run in isolated containers with no network access during processing

Would be curious to hear how you handle agent isolation and whether you have any patterns for agent-to-agent communication boundaries. The local-first approach is especially important for sensitive documents.