AI Agents for Telecom
The Status Quo
The client wanted to create AI-powered tools to help junior engineers diagnose network problems faster. The off-the-shelf tool being used, Wireshark — is a domain-specific tool that takes years of expertise to master.
When a critical service is degrading, that's time the business doesn't have.
Build an AI-powered system that could automate network packet analysis
The system should return a structured answer — in plain language — with enough detail for an expert to validate the findings.
Challenge: LLMs Can't Read Network Traffic
Network traffic is a special kind of data format which CANNOT be directly read by LLMs like Claude and ChatGPT. Unlike other data types like text files or PDFs, this is quite niche — and there are no mainstream AI clients which support it out of the box.
The bottleneck isn't the model — it's the context.
With the right harness, an LLM can convert raw binary data from a network stream in a parseable format which it can then actually reason about.
How the Harness Works
We built a three-layer pipeline that turns a raw data into queryable intelligence:
We used TShark to parse every packets from LIVE network flows. Our harness allows AI agents to access this data via a custom MCP server
We integrated the Teleco's EXISTING Network rules into the Agent. Now AI could securely utlizes proprietary know-how without Human Intervention.
We built a custom dashbord which tracked Raw Traffic flows with Agentic insights. Engineers could directly highlight traffic flows and ASK question in Natural Lanugage.
The harness acts as a translation layer.
It doesn't make the model smarter — it makes the data accessible. It extracts the signal, filters the noise, and structures the insights into a format the AI can reason about. This is the same principle that applies to every domain we work in at harnes.io:
structured context is the unlock.
The Impact: Before vs. After
Before the Harness
- ✕Capture PCAP, open in Wireshark
- ✕Write complex display filters
- ✕Manually trace TCP streams
- ✕Cross-reference protocol RFCs
- ✕Piece together a timeline by hand
- ✕Hours to find root cause
After the Harness
- ✓Upload PCAP to the harness
- ✓Ask a question in plain English
- ✓AI correlates signals across layers
- ✓Get structured RCA + visualizations
- ✓Validate with exact IPs and timestamps
- ✓Minutes to find root cause
The Vision
Network troubleshooting shouldn't require hours of manual filtering. A support engineer should be able to ask "What went wrong?" and get a clear answer — with root cause, evidence, and a path to validation. The harness handles the grunt work so engineers can focus on judgment and action.
That's the power of a well-designed harness. And it's exactly what we built for our telecom client.