Troubleshooting networks is hard. Fragmented tools, institutional knowledge, and escalating complexity make it a time-consuming, high-stakes challenge. But what if we could rethink the process entirely-using AI agents that reason, verify, and collaborate like a team of expert engineers?
This post kicks off a three-part series on Deep Network Troubleshooting, a new approach that applies agentic AI and deep research principles to network diagnostics. In today's post, we introduce the concept and architecture. Next, we'll explore how we ensure reliability and minimize hallucinations. The final post in the series will focus on transparency and observability-critical for building trust in AI-driven operations.
Let's begin with the big idea: what happens when deep research meets deep troubleshooting?
How agentic AI is transforming network troubleshooting
Agentic AI is already reshaping how work gets done across industries-and network automation and operations are no exception. Among all the places it can help, troubleshooting and diagnostics stand out: they are high-value, time-sensitive, and notoriously fragmented across tools, teams, and institutional knowledge.
In this post, I'd like to introduce Deep Network Troubleshooting-an agentic AI solution inspired by the deep research agents popularized by OpenAI, Anthropic, and others, and purpose-built for multivendor network diagnostics. It blends large language model (LLM)-powered autonomy with knowledge-graph reasoning, domain-specific tools, and error-mitigation techniques to accelerate root cause analysis (RCA) while keeping humans in control.
What is deep research AI and why it matters for networking
For the past few months, several leading AI labs and AI frameworks have introduced deep research agentic solutions. While there is no single definition of what deep research is, we could define it as a disciplined, multistep approach to solving complex questions: plan the investigation, search broadly, verify facts, and refine until the evidence aligns. Think of it like a team of AI agents working together-gathering, validating, and synthesizing information-to deliver fast, trustworthy answers.
Figure 1: Deep research option on popular AI platform
If you haven't explored deep research features from platforms like OpenAI, they're worth checking out. These features demonstrate multiple agents collaborating, iterating, and refining their understanding until they reach a well-supported answer.
It's a powerful approach to solving complex problems. And when you see it in action, it naturally raises the question: why not apply this same methodology to network troubleshooting?
Why troubleshooting suits agentic AI
Troubleshooting is, at its core, a structured research task:
- You start with symptoms (alerts, SLO breaches, user tickets).
- Form hypotheses and collect evidence (telemetry, logs, configs, topology).
- Iterate: test → refute → refine-until you land on a root cause and a safe fix.
That loop maps perfectly to multi-agent systems that plan, gather, validate, and summarize-fast and repeatedly-without getting tired or distracted.
Can LLM-powered agents really diagnose network issues?
LLM-powered agents invite fair skepticism: hallucinations, shallow reasoning, weak reliability. The key is to constrain and augment them:
- Tool-centric design: Agents never "guess" device state; they fetch it through authenticated tools (CLI/NETCONF/REST, NMS/APIs, log search, packet captures).
- Grounding in a knowledge graph: The network's entities and relationships (devices, interfaces, Virtual Routing and Forwarding, Border Gateway Protocol sessions, services) provide context and constraints, guiding reasoning and reducing false leads.
- Verification loops: Agents cross-check claims against telemetry and rules; suspect conclusions must be re-proven from independent signals.
- Deterministic guardrails: Policies, playbooks, and safety checks minimize risks with changes unless a human approves.
- Memory and provenance: Every step is logged with evidence and lineage so engineers can audit, reproduce, or challenge a conclusion.
When you put the philosophy debates aside and implement the technology using a careful approach, the results are compelling.
Adapting deep research AI for network operations
Deep research agents excel by orchestrating multiple specialists that:
- Plan a line of inquiry
- Gather and synthesize evidence
- Iterate until confidence is achieved
Deep Network Troubleshooting adapts this pattern to networks.
Meet the agents: Roles in AI-powered network diagnostics
To keep things running smoothly and quickly, modern networks can lean on a mix of smart AI agents-each one handling a specific part of troubleshooting or fixing issues. These are some of the key agents that power this new approach:
- Deep Troubleshooting agent: Interprets problem and identifies hypothesis.
- Hypothesis tester: Evaluates validity of hypothesis.
- Query agents: Reason about a request and draft a plan on how to address it, breaking it down into smaller steps which are then executed autonomously.
- RCA synthesizer: Assembles a clear root cause with evidence, side effects, and confidence.
- Remediation draftsman: Proposes safe actions and rollback plans; routes to approval.
Each agent is LLM-powered, knowledge graph-driven, and runs with embedded safety and reliability mechanisms.
Core architecture pillars of Deep Network Troubleshooting
Let's take a closer look at the key building blocks that make Deep Network Troubleshooting both intelligent and safe. These range from knowledge graphs and LLMs to the tools, safeguards, and human oversight that keep everything grounded.
• Knowledge graph: A continuously updated KG models devices, links, protocols, services, policies, and their temporal changes. It provides:
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- Path and blast-radius reasoning (who's affected and why)
- Policy constraints (what "good" looks like)
- Entity disambiguation (for example, eth1/1 versus Gi0/1) and multivendor normalization.
• Large language models: LLMs are the brains of an agent and determine the agent's ability to reason, plan, and interact with the knowledge graph and tools, to accomplish the goals.
• Domain tools and adapters: Deep Network Troubleshooting relies on a wide range of domain tools and adapters-like connectors for CLI, NETCONF, RESTCONF, streaming telemetry, SNMP, syslog, NMS/ITSM, CMDB, packet brokers, and cloud APIs-to ensure agents only act on facts they can verify directly through trusted sources.
• Error-mitigation techniques: Multiple techniques are used in parallel to minimize the probability of an error. (Stay tuned for more details on this in the next installment of this series.)
• Human-in-the-loop safety: Agents are read-only by default; proposed changes are structured as remediation drafts with diffs, impact analysis, and rollback.
How AI agents improve network operations and MTTR
This is disruptive, transformational-perhaps even scary. But it augments network operations teams beyond what any other technology has enabled so far.
Networks are heterogeneous, multivendor, dynamic, and-whether we like it or not-a significant portion of the data necessary to troubleshoot problems is unstructured. In a setup like this, AI agents can really step up and help network engineers do more-faster, smarter, and with less manual grind.
When something breaks, you might wish you had ten engineers to chase down the root cause. And sure, maybe you do, if you're at a massive organization. But with AI agents, you don't need ten people; you can spin up ten agents, or even a hundred, all working in parallel under the guidance of a single engineer. That's the beauty of software-it lets us rethink how we approach problems, like evaluating dozens of hypotheses at once to zero in on where the issue really started. The consequences of this are tangible:
- Faster MTTR: Agents compress the search space and automate the grind.
- Better signal-to-noise: Findings are anchored in verifiable evidence and graph context.
- Engineer leverage: Focus humans on novel, high-judgment cases; delegate the routine tasks.
- Fleet-wide consistency: Use the same methodical investigation, every time, across vendors.
The vision at Cisco for AI-driven network troubleshooting
Deep Network Troubleshooting exemplifies our investment in practical, safe agentic AI for real networks. It's designed for multivendor environments and built to meet network teams where they are: existing tooling, established change control, and clear audit needs. It represents industry-leading innovation in network diagnostics and, to our knowledge, the industry's first agentic solution with this breadth of applicability in multivendor settings, and it's coming as part of our Crosswork Network Automation solution.
Connect with Cisco to explore AI-powered network diagnostics
If you're exploring how to delegate more diagnostics to software-safely and credibly-we'd love to connect. Deep Network Troubleshooting helps teams move faster, reduce toil, and make every incident a little less...incident-y.
Want to dive deeper? Let's connect, have some fun exploring this technology, and make amazing things happen together. Please join us.
Join the conversation at the Community.
Additional resources
- Autonomous Networks for Service Providers white paper
- Cisco Crosswork Network Automation