Building an Advanced Multi-Agent Incident Response System with AgentScope
Creating a robust incident response system is vital for managing IT incidents effectively. In this tutorial, we will build an advanced multi-agent incident response system using AgentScope, demonstrating how to leverage various agents for routing, triage, analysis, writing, and review tasks.
Setting Up Your Environment
To begin, we must install the required dependencies and set up a reliable environment in Google Colab. Use the following command to install AgentScope:
bash
!pip -q install “agentscope>=0.1.5” pydantic nest_asyncio
Next, we ensure the OpenAI API key is securely loaded and initialize the essential components of AgentScope that will be shared across all agents.
Defining the Internal Runbook
A lightweight internal runbook guides the agents in making informed decisions during incident handling. The runbook can include policies for classification and actions for different incident severities. Here’s an example of a basic runbook structure:
python
RUNBOOK = [
{“id”: “P0”, “title”: “Severity Policy”, “text”: “P0 critical outage, P1 major degradation, P2 minor issue”},
{“id”: “IR1”, “title”: “Incident Triage Checklist”, “text”: “Assess blast radius, timeline, deployments, errors, mitigation”},
{“id”: “SEC7”, “title”: “Phishing Escalation”, “text”: “Disable account, reset sessions, block sender, preserve evidence”},
]
This model allows for easy retrieval and classification based on incidents reported.
Constructing Specialized Agents
We will create multiple specialized agents, each designed to handle a specific aspect of the incident management process. Each agent has a clear responsibility:
- Router Agent: Directs the request based on its nature (triage, analysis, or report).
- Triager Agent: Assesses severity and suggests immediate actions.
- Analyst Agent: Analyzes logs and computes summaries.
- Writer Agent: Drafts concise incident reports.
- Reviewer Agent: Reviews and improves the incident reports.
python
router = ReActAgent(
name=”Router”,
sys_prompt=”Route the request to triage, analysis, or report and output structured JSON only.”,
model=make_model(),
formatter=OpenAIChatFormatter(),
memory=InMemoryMemory(),
)
This setup allows for separate concerns, improving the system’s clarity and maintainability.
Implementing Communication between Agents
To ensure smooth communication between agents, we will introduce a utility function that normalizes outputs. This prevents any format issues during inter-agent interactions.
python
def msg_text(m: Msg) -> str:
blocks = m.get_content_blocks(“text”)
return “\n”.join(str(x) for x in blocks) if isinstance(blocks, list) else str(blocks)
Running the Full Workflow
The final step is orchestrating the workflow. We route the user request to the appropriate agents through a message hub, allowing them to collaborate effectively. Below is how you can execute this workflow:
python
async def run_demo(user_request: str):
route_msg = await router(Msg(“user”, user_request, “user”))
lane = route_msg.metadata.get(“lane”, “unknown”)
if lane == “triage”:
first_response = await triager(Msg(“user”, user_request, “user”))
elif lane == “analysis”:
first_response = await analyst(Msg(“user”, user_request + “\n\nLogs:\n” + LOGS, “user”))
elif lane == “report”:
draft = await writer(Msg(“user”, user_request, “user”))
first_response = await reviewer(Msg(“user”, “Review and improve:\n\n” + msg_text(draft), “user”))
else:
first_response = Msg(“system”, “Could not route request.”, “system”)
Additional collaborative refinement loop can be added here.
Conclusion
In this tutorial, we’ve demonstrated how to design an advanced multi-agent incident response system using AgentScope. Through robust architecture, we can scale our system for future complexities without sacrificing clarity or control. By utilizing specialized agents for each task and fostering collaboration, we pave the way for efficient incident management in real-world scenarios.
Related Keywords:
- Multi-Agent Systems
- Incident Response
- AI Agents
- AgentScope
- OpenAI Integration
- Python Programming
- Workflow Automation
