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Networking and Security AI Agents: Expectations vs Reality, Benchmarks and Solutions

AI agents promise fewer tickets, faster root cause, and lighter config work; reality varies by model, tools, and guardrails. This guide shows what exists and how to deploy safely.

Categorized in: AI News General IT and Development
Published on: Sep 25, 2025
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Networking and Security AI Agents: Expectations vs Reality, Benchmarks and Solutions

Developer AI Agents for Network and Security: Expectations vs Reality

AI agents promise fewer tickets, faster root cause, and less manual config work. The reality is improving fast, but outcomes depend on the model, tooling, and guardrails you choose.

This guide breaks down what AI agents are, what engineers expect, what exists today, and how to deploy them safely across networking and security use cases.

What Is an AI Agent?

An AI agent is autonomous software that perceives, reasons, and acts to reach a goal. It can use tools like web search, APIs, code execution, and file ops to work across systems.

In networking and security, agents can monitor devices, troubleshoot issues, analyze policies, and respond to threats with minimal human input-ideally with clear plans and auditable actions.

Design and Architecture: Decisions to Make Early

  • Choose the AI framework and orchestration pattern (single agent vs multi-agent, planner-executor, or toolformer-style).
  • Select the model(s): general LLM, domain-tuned model, or a thinking/reasoning model for long chains of actions.
  • Define tools: CLI, gRPC/REST, config stores, ticketing, observability, and file operations with strict permissions.
  • Gather and test connections: databases, APIs, and MCP servers you plan to use.
  • Decide guardrails: allowlists, read-only defaults, dry runs, approvals, and full audit logging.

Models for AI Agents

The LLM is the "brain" of the agent. Benchmarking helps, but many public leaderboards skip networking and security tasks.

  • Networking: Network Operational Knowledge (NOK) benchmarking for LLMs.
  • Security: CTIBench and domain-tuned options like Foundation-Sec-8B (open-weight, cybersecurity-focused).

If you need multi-step planning, consider models optimized for reasoning and tool use.

Reasoning and "Thinking" Models

  • Gemini 2.5 Pro (Deep Think)
  • GPT-5 Thinking
  • Claude Sonnet 4.0 Thinking
  • Claude Opus 4.1 Thinking
  • Qwen3-235B-A22B-Thinking

Match the model to the job: planning-heavy agents for troubleshooting and change plans; concise models for quick checks and report generation.

What Engineers Expect vs What Exists

Engineers ranked the most wanted AI agents on Cisco DevNet Code Exchange (100 votes, Sep 2025):

  • Configuration automation: 37%
  • Network monitoring: 32%
  • Threat and vulnerability: 22%
  • Code generation: 9%

Takeaway: the priority is operational agents that remove toil-configuration and monitoring. Security-focused agents also draw interest, while pure code-writing ranks lowest.

What's Available Today

Open-source: Agents that accept natural language and convert it into safe CLI or gRPC/REST actions. Some run end-to-end diagnostics: validate intent, plan per-device steps, execute across fleets, assess findings, and produce memory-augmented reports.

Explore open-source networking and security agents here: DevNet Code Exchange. A dedicated section for MCP servers is coming.

Commercial: Cisco AI Assistant supports product-aware analysis, policy insights, report generation, and notifications. Cisco is also building AI Canvas-an integrated space for telemetry, AI insights, and collaboration across IT domains.

Other vendors include AI Network Engineers by Nanites AI, DevAI, Selector AI's Copilot for Network Automation, and Aviz AI Agents. Many claim multivendor coverage.

What Agents Can Do Right Now

  • Verify end-to-end pings and paths automatically, then attach evidence.
  • Check compliance and generate on-demand audit reports.
  • Validate firewall rules and policy intent with change summaries.
  • Provide Level 1 support automation (triage, context gathering, suggested fixes).
  • Inventory insights (devices, OS versions, hardware SKUs, ASICs, MACs, optics).
  • Correlate metrics and predict capacity/performance risks.

Security Concerns-and How to Reduce Risk

Accuracy is improving, but security and compliance concerns are rising due to jailbreaks, prompt injection, and model/data poisoning. Typical failure modes include faulty tool selection, hallucinations, and acting on bad upstream data from APIs/CLI.

  • Use least privilege: allowlist tools, scoped credentials, network segmentation, and execution sandboxes.
  • Require read-only reconnaissance first; escalate to changes only with approvals or change windows.
  • Validate intent and plans: deterministic planning, dry runs, and human-in-the-loop for high-impact changes.
  • Add guardrails: content filters, policy checkers, and structured tool responses.
  • Pick models suited to security and operations; consider domain-tuned weights.
  • Log everything: prompts, tool calls, outputs, diffs, and final actions for audit.
  • Red team your agent against known risks. See the OWASP Top 10 for LLM Applications.

Minimum Viable Agent: A Practical Plan

  • Start with one high-value workflow: config checks, compliance drift, or incident triage.
  • Select a reasoning-capable model and define strict tools (read-only first).
  • Wire up device access via CLI/gRPC/REST and observability data.
  • Design prompts with clear goals, constraints, and stop conditions.
  • Add guardrails, dry runs, and approval gates.
  • Test on a lab or canary segment; measure accuracy, safety, and time saved.
  • Deploy through ChatOps or an API; monitor logs and iterate.

Resources

The bottom line: prioritize a narrow, valuable workflow, enforce strict guardrails, and measure outcomes. That's how AI agents move from hype to dependable day-to-day utility in networks and security.