Description
Explore multi-agent architecture as a way to avoid MCP context rot.
Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.
Goals
Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.
Example prompts/responses:
user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...
user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...
Resources
Language Python. The Python ADK is more mature than Golang.
This project is part of:
Hack Week 25
Activity
Comments
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2 months ago by rtsvetkov | Reply
Hi, I work on a similar smaller project. Perhaps it will be interesting to incorporate it in your approach.
It doesn't give direct recommendation, but evaluates which aspect of the problem (in my case bug) is critical for a decision - recommends the question critical for the decision. GenAI-Powered Systemic Bug Evaluation and Management Assistant So, which information piece is missing for a potential decision. It leaves for the admin/developer to provide the info.
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2 months ago by doreilly | Reply
The project as a POC is completed. Two sub-agents were created: one for general health and one for apache.
It takes the trajectory as expected, e.g. ask a question about apache and it will directly invoke the apache sub-agent and not invoke the general health sub-agent. So in theory this could scale out to many specialist sub-agents, and each session would only invoke the relevant ones.
Testing with gemini-2.0-flash and it's quite fast and reliable.
Tested ollama:qwen3:30b, and it worked but very slow. using-ollama_chat-provider
The ADK also supports evaluation. Not sure if it's possible to mock the tool call results.
Reply to rtsvetkov
Hi, I work on a similar smaller project. Perhaps it will be interesting to incorporate it in your approach.
It doesn't give direct recommendation, but evaluates which aspect of the problem (in my case bug) is critical for a decision - recommends the question critical for the decision. GenAI-Powered Systemic Bug Evaluation and Management Assistant So, which information piece is missing for a potential decision. It leaves for the admin/developer to provide the info.
# A First Level Header ## A Second Level Header Use one asterisk to *emphasize* Use two asterisks for **strong emphasis** - Use hyphens - for unordereed - lists This is an [link to example.com](http://example.com/) This is an image  This is a user link @hans This is a project link hw#some-cool-title
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