Feature: Network-AI as MCP coordination layer for multi-agent NeMo workflows

[Jovancoding]

Problem

When NeMo Agent Toolkit workflows involve multiple agents running concurrently (e.g., a LangChain researcher + CrewAI reviewer + custom validator), there is no built-in mechanism to prevent state conflicts between them. If two agents write to the same shared resource simultaneously, you get last-write-wins -- silent data corruption with no error thrown.

Proposed Solution

Network-AI is an open-source (MIT) MCP server that provides atomic state coordination for multi-agent systems. Since NeMo Agent Toolkit already supports MCP clients, Network-AI can be used directly as an MCP tool server -- no code changes needed in NAT.

What Network-AI adds via MCP (22 tools):

Category	Tools	Description
Atomic shared blackboard	blackboard_read, blackboard_write, blackboard_list, blackboard_delete, blackboard_exists	propose, validate, commit with file-system mutex
Permission gating	token_create, token_validate, token_revoke	HMAC-SHA256 scoped tokens
Token budgets	budget_status, budget_spend, budget_reset	Per-agent cost ceilings
Agent lifecycle	agent_list, agent_spawn, agent_stop	Manage agent instances
Audit trail	audit_query	HMAC-signed append-only log
FSM transitions	fsm_transition	State machine governance
Configuration	config_get, config_set	Live orchestrator config

How it works with NeMo Agent Toolkit

Network-AI runs as a stdio or SSE MCP server. NAT agents connect as MCP clients and use the tools to coordinate:

# Start Network-AI MCP server (stdio -- compatible with NAT MCP client)
npx network-ai

Example workflow -- a multi-agent pipeline where agents share state through the blackboard instead of passing raw context:

1. Agent A proposes a state change via blackboard_write with propose, validate, commit
2. Agent B reads the validated state via blackboard_read
3. Both agents check budget before expensive operations via budget_status / budget_spend
4. All writes are permission-gated via token_create / token_validate
5. Full audit trail via audit_query

Why this matters for NAT

• Race condition prevention: NAT profiler can identify bottlenecks, but Network-AI prevents the state corruption that causes them
• Framework-agnostic coordination: Works across all 8 NAT-supported frameworks (LangChain, CrewAI, Agno, AutoGen, etc.) via MCP
• Zero integration code: NAT already has MCP client support -- agents just call the tools
• 1,449 tests across 18 suites, Glama A/A/A score, Socket 100/100

Links

• GitHub: https://github.com/Jovancoding/Network-AI
• npm: https://www.npmjs.com/package/network-ai
• Glama: https://glama.ai/mcp/servers/Jovancoding/network-ai

Happy to contribute an example workflow showing NAT agents coordinating through Network-AI MCP tools if there is interest.