Build vs buy AI agents is the usual way to frame the decision, and for a network team it leaves out a third choice. You can build the agent yourself on a framework. You can buy an AI agent platform and build inside it. Or you can have the agent built, tested and delivered to you.
This post compares five options across those three choices, with the pros and cons of each before the verdict. Every fact about another product links to the page it comes from, and every page was checked in October 2026.
Quick answer: five ways to get a network engineering agent
| Option | Examples | Who builds the agent | Who tests it | Always-on telemetry | Who owns the outcome | Best for |
|---|---|---|---|---|---|---|
| Build it yourself on a framework | LangGraph, CrewAI, Claude Agent SDK | Your engineers | Your engineers, in a lab you build | ❌ Your existing tools | You | Teams with agent engineers who want to own the agent's code |
| Agent platform built for networks | Selector Foundry, Itential FlowAI, Aviz Agent SDK, Cisco AI Canvas, NetBox Agents | Your engineers in most cases, in the platform's builder | The platform's test tools and your team | ✅ On the monitoring platforms | You | Teams that want agents inside a platform they already run |
| Horizontal enterprise agent platform | Microsoft Copilot Studio, ServiceNow AI Agent Studio | Your team, in a general builder | Your team | ❌ | You | Business and service desk workflows |
| General AI agent development firm | Consultancies and system integrators | The firm | Varies by contract | ❌ | Varies by contract | Agent work across many departments |
| NetPilot, built and delivered | NetPilot | NetPilot, or your team in Agent Studio | NetPilot, in a lab on real network OSes (the platform runs 14+) | ❌ Reads from the monitoring you run | NetPilot, with a money-back milestone | A working agent for a network workflow |
Bottom line: Most agent platforms sell a builder and leave the build to you. NetPilot owns the build and the outcome, with a guarantee. Build when you have agent engineers and want to own the agent's code. Buy a platform when you want agents inside the monitoring or service platform you already run. When you need a working agent for a network workflow and nobody is free to build it, NetPilot builds the agent, the harness and the tools, tests it on real network operating systems (the platform runs 14+), and delivers the working result.
What you are choosing between
A working network agent is three things, plus the proof that it works:
- The agent. The instructions that tell the model how your team does the job: the checks to run, the order to run them in, and what to hand back.
- The harness. Everything around the model that keeps it on task: the steps it may take, the approvals it waits for, and the tests that prove each change.
- The tools. How the agent reaches your inventory, your config repos, your ticketing and your devices.
- The test. A run on something that behaves like your network, before the agent acts on the real one.
Build, buy or delivered is a question of who does each piece.
| Piece of work | Build it yourself | Buy an agent platform | Have it delivered |
|---|---|---|---|
| The agent's instructions | You | You | The vendor, from your runbook |
| The harness | You | The platform | The vendor |
| The tools | You | The platform's connectors, plus your own | The vendor |
| The test before it acts | You | The platform's test tools, plus your own | The vendor |
| The outcome | You | You | The vendor |
Where agentic NetOps fits
Agentic NetOps is the analyst name for this category. NTT DATA defines it this way: "Agentic NetOps is the use of autonomous, goal-driven AI agents to manage networks. It usually includes a human-in-the-loop and, as trust in AI-delivered operational outcomes grows, further levels of autonomy are possible."
Gartner uses the term in its forecasts. The same NTT DATA page quotes one: "By 2030, 50% of organizations will use agentic NetOps with minimal human involvement, up from nearly 0% in 2025." Itential's summary of Gartner's Predicts 2026 report (dated 4 December 2025) adds that organizations that fail to adopt agentic NetOps face a 25% overspend on network management by 2030.
Vendors use the term for their products. Selector calls Foundry "the Agentic NetOps platform on Selector's full-stack observability and AIOps foundation" on its product page. Cisco uses its own word, AgenticOps.
Two more sources show where teams stand today:
- Cisco and Omdia's 2026 report surveyed more than 1,000 IT and network operations leaders. It reports that three-quarters of organizations have deployed AI for network operations, that 51% run agentic AI in production, and that 69% demand detailed causal tracing for what an agent does.
- EMA's research (March 2026) found that "only a small percentage of IT professionals fully trust the AI tools managing their networks."
So agentic NetOps names the work, also written as agentic AI for network operations. Build, buy or delivered is how you get an agent that does the work. Trust is the stated blocker, which makes "who tests the agent" as important as "who builds it".
Option 1: Build it yourself on a framework
What it is: your engineers write the agent in code on an agent framework. LangGraph describes itself as "an agent runtime and low-level orchestration framework" and is "an MIT-licensed open-source library". CrewAI is a framework for multi-agent systems "with guardrails, memory, knowledge, and observability baked in". The Claude Agent SDK gives a developer "the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript."
Pros:
- Full control of the agent's code. You choose the model, the prompts, the tools and where the agent runs. With an open-source framework such as LangGraph or CrewAI, that includes the framework itself. With the Claude Agent SDK you control your application, prompts and tools, and the agent loop underneath is Anthropic's.
- No platform license for the framework. LangGraph is free to use under the MIT license.
- The skills stay in your team. Retool's build vs buy guide argues that custom agents "help build your organization's AI development muscle for the future."
- It has been done. Microsoft's own network team built an agent in-house. Microsoft reports that it saves engineers 20 to 25 minutes of searching per successful prompt and has cut live incidents by 10%.
Cons:
- You assemble all four pieces. The framework gives you the loop and some of the harness: LangGraph has approval controls and memory, CrewAI has guardrails, memory and observability. Wiring them into a network workflow, the network tools, the domain tests and the test lab are your project.
- It takes longer than planned. Dust, which sells an agent platform, writes in its build vs buy post: "Companies that choose to build typically underestimate the timeline by 6-12 months."
- The stack moves under you. Microsoft's team started with "a conversational agent built on Semantic Kernel and Azure OpenAI" and says it later "switched to a declarative-agent model" as Microsoft's own tooling changed.
- It needs two skill sets. The builders have to know routing protocols and vendor CLIs, and also agent engineering.
Best for: teams with agent engineers on staff, a platform group to host the result, and a reason to own the code.
Option 2: Buy an agent platform built for networks
What it is: a network automation platform or observability platform that now includes an agent builder. Your team builds its agents inside it, on the platform's data.
- Selector Foundry. Network World (September 23, 2026) describes it as "a development and runtime environment that lets network operations teams build, test, version, and govern their own AI agents inside the company's platform." Customers commit agents to their own Git repository, and an agent is replayed against the customer's historical incident data before it goes live.
- Itential FlowAI. Itential's page puts it this way: "Build, run, and operate FlowAgents that reason through goals and act on real infrastructure". On governance it lists "Three layers of control: who can build agents, what tools each agent can call, and who can execute them."
- Aviz AI agents and Agent SDK. Aviz ships pre-built agents on its Network Copilot product, plus an SDK. Its page says: "Build custom workflows in Python and deploy to Network Copilot in minutes." Its platform page says connectors bring in config, telemetry, flows, logs and tickets. Aviz also offers to do the build. The same product page says: "Don't want to build? Our engineers will, designing and deploying custom agents for your specific challenges."
- Cisco AI Canvas. Cisco presents AI Canvas as the workspace for AgenticOps, connected to the Cisco products a team runs, with custom agents built in Cloud Control Studio. Cisco's getting-started guide says Cloud Control is generally available in the United States only, as of October 2026.
- NetBox Agents. In public preview since August 27, 2026. NetBox Labs says a team "describes a recurring task in natural language, attaches the context it should consider, picks its toolkits, and sets what kicks it off." The same post lists three pre-built agents, for reporting, troubleshooting and IPAM. You set the approval policy by action type, and a change can arrive as a branch diff you review.
Pros:
- Always-on telemetry and monitoring. On the observability platforms, the agents sit on data the platform already collects around the clock. This is the platform's core strength.
- Agents governed inside a platform you already run. Access control, approvals and audit come from the platform. Your team learns no new place to work.
- The vendor's installed base. If your network already runs on that vendor's gear or records, the agent starts with that context.
- Some testing and review tools come with it. Selector replays an agent against past incidents. NetBox shows a change as a branch diff before anything merges, which is a review and approval control, not a test of the agent or of network behavior.
Cons:
- In most cases the build stays with your team. Most of these products hand you a builder, and for a workflow no pre-built agent covers, someone on your team still writes the agent, wires the tools and proves it works. Two vendors in this list ship pre-built agents for common jobs: Aviz, and NetBox Labs with reporting, troubleshooting and IPAM agents. Aviz also offers to build custom agents for you.
- Strongest on the platform's own data. A workflow that crosses several systems needs extra integration work.
- You pay for a license, and the outcome is yours to deliver.
Best for: teams that buy monitoring or orchestration first and want their agents in the same place.
Option 3: Buy a horizontal enterprise agent platform
What it is: a general agent builder your company may already license. Microsoft calls Copilot Studio "a platform for creating and managing custom agents, workflows, and apps with no code", with "1500+ pre-built data connectors, and MCP servers". ServiceNow's documentation describes AI Agent Studio as an application inside ServiceNow for creating, managing and testing AI agents and agentic workflows.
Pros:
- Your company may already run it. If it does, security review, identity and governance are in place, and IT knows the product.
- No code, on Copilot Studio. Someone who is not a developer can build an agent.
- Strong on business and service desk workflows. Tickets, approvals and employee requests are home ground.
Cons:
- A general builder. The network knowledge, the device tools and the test lab are yours to add.
- The build stays with your team, the same as Option 2.
- Network engineers still do the build. Microsoft's network agent, which later moved onto Microsoft 365 Copilot, leaned on its own network engineers through the first versions, according to the same Microsoft article.
Best for: service desk and business workflows, and network teams that want a ticket-facing agent next to the systems IT already runs.
Option 4: Hire a general AI agent development firm
What it is: a consultancy, an AI agent development company or a system integrator that builds agents to order.
Pros:
- Someone else does the build. Your engineers stay on their day job.
- Broad AI engineering skills. These firms build agents for many kinds of work, so they suit a company that wants agents across several departments.
- Flexible terms. The contract decides who owns the code and where it runs.
Cons:
- Network depth varies. A generalist team may not have run BGP or read a vendor CLI. Ask who on the project has.
- Testing varies. Ask which network operating systems the agent is tested on, and in which lab.
- Outcome terms vary. Time-and-materials work bills for hours. Ask what happens when the agent does not do the job.
Best for: companies that need agents for many departments, with networking as one workflow among them.
Option 5: NetPilot, built and delivered
What it is: Name any network engineering agent. NetPilot builds the agent, the harness and the tools, tests it on real network operating systems, and delivers the working result. This is a vertical AI agent approach: the builder, the lab and the people doing the work are all network-specific.
NetPilot offers both paths. Your team builds its own agents in Agent Studio, or NetPilot builds and delivers one for you. Agent building is not something only the platforms above offer. Delivery works in five steps:
- Scope the workflow on a call, and agree a success milestone.
- Build the agent with the harness and the tools.
- Test it on real network operating systems, in a lab that mirrors your network.
- Deliver it in Agent Studio, where your team runs and extends it.
- Stand behind it. If the agreed milestone is not delivered, you get your money back for that milestone.
Pros:
- NetPilot does the build. Your engineers describe the workflow and check the result.
- Tested before it acts. The agent runs in a lab on the real network operating systems your workflow uses (the platform runs 14+), and your engineers can SSH into any lab device to check its work by hand.
- Any vendor, any system. The agent's tools connect to the systems you run over API, MCP or SSH.
- Lab and live network. A delivered agent works in the lab and on your live network, read-only or with changes, through the approval steps your team sets.
- A guaranteed outcome. The success milestone is agreed up front.
- You can still build your own. In Agent Studio an agent has its own instructions, model and effort level, and a chosen set of tools. The Agent Studio guide walks through it.
Cons:
- No always-on telemetry. NetPilot is not a monitoring platform. A delivered agent reads from the monitoring, inventory and ticketing systems you already run.
- Not inside your existing platform. Agents live in Agent Studio. If you want agents governed inside your monitoring or service platform, on its telemetry and the vendor's installed base, Option 2 or 3 fits better.
- Less code-level control than a framework build. Your team runs and extends the agent in Agent Studio. If you want to own and host the code, Option 1 fits better.
- A newer offer. Agent Studio went live in September 2026.
- Delivery starts with a call. A delivered agent is scoped per workflow, so there is no instant download.
Best for: network teams that need a working agent for a real workflow and have nobody free to build it, and teams that want to build their own agents next to a real lab.
Detailed comparison
| Build on a framework | Agent platform for networks | Horizontal agent platform | General AI agent firm | NetPilot | |
|---|---|---|---|---|---|
| Primary use case | Teams with agent engineers on staff | Monitoring-first network operations | Business and service desk workflows | Agents across many departments | Custom agents for network engineering, enterprise change validation, rapid labs (POC, research) |
| Who builds the agent | Your engineers | Your engineers in most cases, in the builder | Your team, in a general builder | ✅ The firm | ✅ NetPilot, or your team in Agent Studio |
| Tested on real network OSes before it acts | Only if you build the lab too | Depends on the platform's test tools | Yours to arrange | Varies by contract | ✅ In a lab on the OSes your workflow uses (14+ available) |
| Always-on telemetry and monitoring | ❌ Your existing tools | ✅ On the monitoring platforms (Selector, Cisco, Aviz). Itential FlowAI orchestrates and NetBox Agents act on records, both next to your monitoring | ❌ | ❌ | ❌ Reads from the monitoring you run |
| Governed inside a platform you already run | ❌ New code to host | ✅ If you run that platform | ✅ If you already run Copilot Studio or ServiceNow AI agents | ❌ | ❌ Agents live in Agent Studio |
| Control of the agent's code | ✅ Full control | ❌ Within the platform's limits | ❌ Within the platform's limits | Varies by contract | ❌ You run and extend it in Agent Studio |
| Network depth | As deep as your team | Deep on the data the platform holds | Yours to add | Varies by firm | ✅ Built by network engineers, multi-vendor |
| Outcome guarantee | ❌ It is your project | A license, the outcome is yours | A license, the outcome is yours | Varies by contract | ✅ Money-back success milestone |
| Where the agents run | ✅ Wherever you host them | Inside the vendor's platform | Inside the vendor's platform | Varies by contract | NetPilot cloud or your own environment |
NetPilot loses three rows here. Agent platforms win always-on telemetry and governance inside a platform you already run. A framework build wins control of the agent's code.
The verdict: tiers by how much of the work each option takes on
Tier means how much of the work, from a runbook to a tested agent, the option takes off your team. It is not a quality score, and in its own lane each option is the right pick. The tiers are as of October 2026.
- S tier, the build and the outcome: NetPilot, built and delivered. It takes on all four pieces of work and puts a money-back milestone on the result. It also covers both paths: build your own in Agent Studio, or have the agent built for you.
- A tier, a builder on network data: agent platforms built for networks. Selector Foundry, Itential FlowAI, Aviz, Cisco AI Canvas and NetBox Agents give your team a builder, governance and the platform's data. They are the pick when always-on telemetry and one governed platform matter most.
- A tier, full control: build it yourself on a framework. LangGraph, CrewAI and the Claude Agent SDK give you everything except the work. They are the pick when owning the code matters most.
- B tier, general purpose: horizontal agent platforms and general AI agent firms. Good at business workflows and broad programs. With a horizontal platform the network knowledge and the test lab come from you. With a firm, both vary by firm and contract, so check for network specialists and a lab before you sign.
Best option for each team
| If you are... | Pick | Why | Also useful |
|---|---|---|---|
| A team with agent engineers that wants to own the code | Build on LangGraph, CrewAI or the Claude Agent SDK | Full control of the agent's code and the host. Any model with LangGraph or CrewAI, Claude models with the Claude Agent SDK | NetPilot's MCP server, so your agent can build and validate labs on demand |
| A NOC that works in its monitoring platform all day | That platform's builder: Selector Foundry, Cisco AI Canvas or Aviz | The agents run on telemetry the platform already holds | A delivered agent that reads from the platform for engineering tasks |
| A team whose changes and records already live in Itential or NetBox | Itential FlowAI, or NetBox Agents once it leaves preview (the vendor recommends non-production NetBox instances during the preview) | Approvals and audit stay in the platform you run | A lab on real network OSes to rehearse the change first |
| A service desk that wants ticket and employee workflows | Microsoft Copilot Studio or ServiceNow AI Agent Studio | No code, on the platform IT already governs | A network agent for the tickets that need device work |
| A company that wants agents across many departments | A general AI agent development firm | One contract for many kinds of work | A network specialist for the network workflows |
| A network team with a real workflow and nobody free to build | NetPilot, built and delivered | The build, the test on real network OSes and a money-back milestone | Your monitoring platform, as a data source for the agent |
| A network team whose engineers can describe the workflow in a prompt | Build your own in NetPilot Agent Studio | Instructions, model and tools in one editor, next to a real lab | The step-by-step guide |
Which should you choose?
- You have agent engineers and want to own the code. Build on a framework. Budget time for the harness, the tools and a test lab.
- You want agents on your monitoring data, inside the platform your NOC already runs. Buy that platform and build there.
- Your company already runs Copilot Studio or ServiceNow's AI agents and the workflow is ticket-shaped. Use the horizontal platform you already have.
- You need agents for many departments. Talk to a general firm, and ask the three questions in Option 4.
- You need a working agent for a network engineering workflow and nobody is free to build it. Have NetPilot build and deliver it. Common requests are pre-change validation, network research and testing, peering and interconnection, provisioning and service turn-up, troubleshooting and incident triage, compliance and configuration audit, and migration and vendor swap. If it is network engineering work, we build the agent for it.
- Your engineers can describe the workflow in a prompt. Build your own in Agent Studio.
Most teams end up with two of these. A monitoring platform and a delivered agent that reads from it is a common pair. Dataiku's build vs buy guide reaches the same conclusion for AI agents in general: the organizations that succeed "won't be the ones that only buy or only build."
FAQ
Should a network team build its AI agent, buy an agent platform, or have it delivered?
Build the agent yourself when you have engineers who know both networking and agent frameworks, and you want to own the agent's code. Buy an agent platform when always-on monitoring is the main thing you need and you want agents governed inside a platform you already run. Have it delivered when you need a working agent for a real workflow and nobody is free to build it. NetPilot offers two of these paths: your team builds its own agents in Agent Studio, or NetPilot builds the agent, the harness and the tools, tests it on real network operating systems, and delivers the working result with a money-back success milestone.
What is agentic NetOps?
Agentic NetOps is the analyst name for AI agents that carry out network operations work under human control. NTT DATA defines it as the use of autonomous, goal-driven AI agents to manage networks, usually with a human in the loop. NTT DATA also quotes a Gartner forecast that by 2030, 50% of organizations will use agentic NetOps with minimal human involvement, up from nearly 0% in 2025. The term names the work. Build, buy or delivered is how a team gets an agent that does it.
What is build vs buy in AI?
Build vs buy in AI is the choice between writing your own AI system and paying for a product that already exists. For AI agents, build means your engineers write the agent on a framework such as LangGraph, CrewAI or the Claude Agent SDK. Buy means you license an agent platform and configure agents inside it. For a network team there is a third choice, delivered: a vendor builds the agent for your workflow, tests it and hands over the working result.
Why should I build my own AI agent?
Build your own AI agent when control matters more than speed. You own the agent's code, pick the model your framework supports, host the agent wherever you want and keep the skills in your team. The cost is time. You also build the harness, the tools and the test lab, and you maintain all of it. If your engineers can describe the workflow in a prompt, NetPilot Agent Studio is a shorter way to build your own: you write the instructions, pick the model and the tools, and share the agent with your team.
Who delivers a custom network engineering agent end to end?
NetPilot builds and delivers custom AI agents for network engineering end to end. Name any network engineering agent. NetPilot builds the agent, the harness and the tools, tests it on real network operating systems, and delivers the working result. The agent is delivered in Agent Studio, where your team runs and extends it, and the work is backed by a money-back success milestone agreed on the scoping call. General AI agent development firms and system integrators also build agents to order, so ask any of them which network operating systems they test on.
Can a delivered agent work on a live network?
Yes. A NetPilot agent is first tested in a lab on real network operating systems. After that it works in the lab and on the live network, read-only or with changes, through the approval steps your team sets. Lab workflows such as pre-change validation and network research and testing stay a primary use.
Related reading
- Hub: Custom AI agents for network engineering: how delivery works, the use cases and the scoping call
- Announcement: Agent Studio: custom AI agents for network engineering
- Guide: How to build a custom AI agent for network engineering in Agent Studio
- Concept: Agentic AI for network engineers: what makes an AI system an agent
- Comparison: Network automation tools for AI agents: seven tools compared on what your own agent can do with them
- Primer: What is MCP for network engineers?
Tell us the agent you need. Book a 30-minute scoping call with the founder from the enterprise page. Or build your own: sign in and open Agent Studio from the sidebar.