Cisco DevNet to Automation: How the Skills Evolved
Cisco DevNet changed networking by making software skills part of the mainstream network-engineering conversation. The important story in 2026, however, is not simply that DevNet arrived. Cisco has now moved the certification track into the familiar CCNA, CCNP, and CCIE naming structure under the Automation family.
On February 3, 2026, DevNet Associate, DevNet Professional, and DevNet Expert transitioned to CCNA Automation, CCNP Automation, and CCIE Automation. Cisco also mapped active holders into the corresponding new certification level. The branding changed, but the core professional idea remains: modern infrastructure engineers need to understand APIs, software, data models, automation, security, and application behavior as well as networks.
Cisco launched the broader DevNet developer program in 2014 as networks were becoming increasingly programmable. Controllers, APIs, cloud services, software-defined networking, and large-scale automation were changing the job. A network engineer could no longer assume that every change would be made manually through a device CLI.
DevNet created a place where infrastructure professionals could learn software concepts without pretending they were becoming application developers overnight. That bridge between networking and development remains the most important part of its legacy.
The new naming makes automation a first-class Cisco career track. CCNA Automation sits at the associate level, CCNP Automation develops deeper professional capability, and CCIE Automation represents expert-level design and execution. The change also aligns automation with the naming conventions used across Cisco’s other major certification families.
For candidates coming from older material, the rule is simple: treat “DevNet” as historical program context and use the current Automation certification names when describing an active target. The article on the DevNet Associate to CCNA Automation transition explains the associate-level change in detail.
Automation begins with interfaces that software can use. Candidates should understand REST concepts, authentication, HTTP methods, status codes, JSON, pagination, error handling, and how to read API documentation. The objective is not to memorize endpoint paths. It is to understand how a program safely requests state or asks a platform to change state.
Practice with read-only calls first. Retrieve inventory, interfaces, clients, or policies. Then move to bounded changes and verify the result. This builds the habit of treating automation as controlled operations rather than as script execution.
Python remains common in network automation because it is approachable and works well with APIs, structured data, testing, and automation libraries. Learn enough language fundamentals to write understandable programs, but do not turn certification preparation into a general computer-science degree.
The most relevant exercises are operational: parse JSON, compare desired and observed state, handle a failed request, loop through devices safely, create a report, and stop when a validation check fails.
Modern network automation is not limited to REST. Structured models such as YANG and interfaces such as NETCONF and RESTCONF help separate configuration intent from device-specific CLI syntax. The benefit is scale and consistency, especially when a workflow must operate across many systems.
A candidate should understand why models exist, how structured data is represented, what a configuration transaction changes, and how to verify the resulting state. Memorizing a protocol definition without being able to explain the operational value is not enough.
Once configuration is represented in code or data files, Git becomes part of the change-control system. Branches, reviews, diffs, commits, and rollback history help teams understand what changed and reduce the risk of undocumented automation.
Testing belongs in the same workflow. A script should validate inputs and handle errors. A pipeline can run syntax checks or policy tests before a production change. These practices are what turn a useful script into maintainable engineering.
Automation accounts can have broad reach, so credentials, permissions, logging, and scope are critical. Use least privilege, protect secrets, validate untrusted data, and record enough execution detail to support an audit or incident review.
The safest automation is also predictable. A workflow should know which devices it can touch, what constitutes success, what should cause it to stop, and how operators can recover from a partial failure.
Cisco automation candidates encounter infrastructure and application interfaces across networking, security, collaboration, data center, and cloud-adjacent workflows. The specific platforms can evolve, but the durable skill is learning how to discover an API, understand its data model, authenticate, make a request, interpret the response, and connect the result to an operational objective.
A useful project might gather interface state from a lab, compare it with an intended baseline, open a ticket for exceptions, and generate a validation report. Another might use an API to build a lab topology, apply a change, then test reachability. Keep the projects small enough that you can explain every design choice.
For a broader current sequence, the Cisco Automation and AI infrastructure path places the associate, professional, and related AI-infrastructure skills in context.
DevNet succeeded because it normalized a hybrid professional identity. Network engineers could become competent software users and automation designers without abandoning networking depth; developers could learn enough infrastructure to build safer operational tools.
The current Automation certifications continue that idea under clearer names. If you are reading older DevNet material, keep the technical lessons but map the credential names and exam objectives to today’s Cisco program. The durable career advantage is not the old badge name. It is the ability to connect software intent, network behavior, and operational evidence.
