AI + Networking
Agentic Network Debugging
An AI network troubleshooter you talk to in plain English: a Google ADK agent with seven networking tools that diagnoses issues and recommends next steps.
The Challenge
Traditional network debugging demands the right command, the right flags, and the right sequence. Engineers lose time recalling syntax and digging through man pages instead of reasoning about the actual fault. The goal was to let anyone describe a problem, such as "I cannot access example.com" or "check if port 22 is open", and get a real diagnosis back.
Engineering Thought Process
Rather than a script that runs every check, the design centers on a NetworkTroubleshooterAgent that reasons about which tools a specific symptom warrants. It plans, calls only the relevant tools, analyses the output, and returns a root cause plus next steps, flagging alternative possibilities when confidence is low. Selective tool use keeps runs fast and avoids noisy, irrelevant scans.
Technology Selection
Google Agent Development Kit (ADK)
Provides the agent loop, tool-calling, and reasoning orchestration without hand-rolling a planner.
Python networking tooling
Each diagnostic is a clean, reusable function the agent can call: easy to test and extend.
Structured tool outputs
Consistent results (RTT, packet loss, open ports, hops) let the agent reason reliably instead of parsing prose.
Implementation
Seven tools are integrated directly into the agent: (1) IP Address Resolver: resolves a domain to all its IPs, usually the first diagnostic step; (2) Ping: reachability with round-trip time and packet-loss percentage; (3) Traceroute: hop-by-hop path mapping to locate slowdowns or blocks; (4) Port Scanner: scans a port range and reports open vs closed; (5) TCP Connectivity Check: lightweight three-way-handshake test for a single port; (6) DNS NS Lookup: verifies resolution to separate DNS faults from server faults; (7) Routing Table: default gateway, routes, and egress interface for gateway and routing misconfigurations. The agent selects the subset that matches the reported symptom, then summarises findings in plain language.
Challenges
- Teaching the agent to choose a minimal, relevant tool set instead of running everything
- Turning raw command output into structured, reasoning-friendly results
- Communicating confidence: naming the most likely cause while surfacing alternatives
Business Impact
7
networking tools built into the agent
Plain English
no flags or command syntax required
↓
time to root cause for common faults
Lessons Learned
Agents are most useful in operations when they narrow the search space. The value is not running more checks: it is knowing which three checks matter for this symptom and explaining why.
Engineering Reflection
Next steps: safe execution boundaries for scans, packet-capture analysis, and a feedback loop so engineer corrections sharpen the agent's diagnostic reasoning over time.