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DevNet Professional Is Now CCNP Automation: Current 2026 Certification Guide

Cisco retired the DevNet Professional name on February 3, 2026 and moved the professional automation track into the familiar CCNP framework. The current certification is CCNP Automation. It is not merely a label change: Cisco redesigned the professional track around a new core, 350-901 AUTOCOR v2.0, plus one of two current concentration exams, 300-435 ENAUTO or 300-635 DCNAUTO. Candidates arriving through the legacy DevNet Professional page should therefore build their study plan around the current CCNP Automation structure rather than the former DEVCOR-era exam map.

The broader Cisco certifications portfolio now treats automation as its own associate, professional, and expert discipline. That matters because network automation is no longer framed as “software development near the network.” Cisco’s current blueprint expects practitioners to combine software design, infrastructure as code, validation, operations, platform APIs, and AI-assisted automation with enough infrastructure knowledge to make safe changes.

How CCNP Automation Is Earned in 2026

To earn CCNP Automation, pass the 350-901 AUTOCOR core and one concentration. ENAUTO focuses on enterprise automation and programmability across device and controller-driven environments. DCNAUTO focuses on data center networking automation, infrastructure as code, operations, and AI in automation. Each passed exam also earns a specialist certification, so candidates can demonstrate useful progress before completing the two-exam CCNP.

There is no formal prerequisite. However, professional-level work assumes you are already comfortable with APIs, Python, source control, data formats, testing, and the network or data-center platforms you intend to automate. The old DevNet Associate path now maps to CCNA Automation and can still be a sensible foundation for candidates who need more practice with application development and API fundamentals.

AUTOCOR v2.0 Is Built Around Production Automation Systems

The current core emphasizes complete automation systems rather than isolated scripts. Cisco’s official topics include Python, RESTCONF, Ansible, Terraform, REST APIs, authentication workflows, pagination, error handling, persistent authentication, Git operations, CI/CD pipelines, testing, observability, security, and AI-assisted automation. The practical implication is that a one-off script is not enough; you should be able to explain how code is developed, tested, promoted, observed, and recovered.

Build projects that have a clear lifecycle. Store code in version control, separate configuration from logic, run tests before deployment, log every change, and verify the network state afterward. The PrepAway discussion of DevOps practices is useful because the strongest automation workflows borrow heavily from software delivery: peer review, repeatability, test environments, release control, and measurable feedback.

Infrastructure as Code Requires State and Review Discipline

Infrastructure as code turns configuration into a versioned artifact, but it also creates new failure modes. A template can be syntactically valid while expressing the wrong intent, and a broad deployment can multiply a mistake quickly. Learn how declarative tools model desired state, how plans or diffs reveal proposed changes, and how state files or inventory affect reproducibility.

Terraform is directly relevant to the current automation blueprint, so understanding Terraform workflows can deepen the infrastructure-as-code section. Pair that knowledge with Ansible and API-driven approaches rather than assuming one tool is universally best. The right method depends on target platforms, state model, transaction behavior, existing operational practice, and the need for rollback.

Testing Is What Separates Automation From Fast Configuration

A professional automation engineer should be able to prove that a workflow did the right thing. Write unit tests for data transformations and decision logic, integration tests for API clients, and pre-change/post-change checks for infrastructure state. When a workflow touches routing, VLANs, ACLs, interfaces, or policy, verify the operational result rather than trusting an API success response.

Use lab environments or simulation where practical. Test negative cases deliberately: expired credentials, partial API responses, rate limiting, unreachable devices, malformed data, failed pipeline dependencies, and configuration conflicts. A safe system knows when to stop and escalate instead of continuing through an uncertain state.

APIs and Data Models Must Be Treated as Contracts

Automation breaks when engineers assume an API will always return the same shape or behave like a CLI. Read documentation carefully, understand resource models, HTTP methods, status codes, pagination, token handling, idempotency, and error semantics. Validate the fields you consume and tolerate optional data when the contract allows it.

Model-driven interfaces such as RESTCONF and YANG require the same rigor. A structured data model makes intent easier to validate, but only if you understand the hierarchy and data types. Keep adapters for platform-specific behavior separate from reusable business logic so that one API change does not force a complete rewrite.

Choose ENAUTO or DCNAUTO Based on the Systems You Operate

ENAUTO is the natural concentration for engineers automating enterprise networks, controllers, campus, WAN, and enterprise platform workflows. It also overlaps with CCNP Enterprise for candidates who want both deep network engineering and automation credentials. DCNAUTO is better aligned with data center network automation and pairs naturally with CCNP Data Center.

Do not pick a concentration because you assume one has fewer topics. Pick the environment where you can build real projects. Professional automation becomes durable when you understand both the platform state and the code that manipulates it.

AI and Agentic Automation Need Stronger Guardrails, Not Weaker Ones

AUTOCOR v2.0 explicitly includes AI in automation. Models can summarize telemetry, generate code, classify incidents, or help construct workflows, but generated output must still pass the same engineering controls as human-written changes. The PrepAway discussion of agentic AI is especially relevant when an AI system can call tools or take actions rather than merely suggest text.

Use least-privilege tool permissions, explicit action schemas, dry-run modes, human approval for high-impact changes, audit logs, and bounded retry behavior. Treat model output as untrusted until validated. An agent that can change hundreds of devices should have stricter controls than a junior engineer with a terminal, not fewer.

Build a Portfolio That Demonstrates Safe Operations

Create several small projects instead of one opaque platform. Examples include configuration compliance, inventory normalization, pre-change validation, deployment through a CI pipeline, network-state testing, or a bounded remediation workflow. The PrepAway Cisco DevNet certifications material is useful historical context, while current projects should use the CCNP Automation taxonomy and modern AUTOCOR expectations.

For every project, document the requirement, target systems, data model, credentials, failure modes, tests, rollback method, and observability. If another engineer cannot understand how your automation fails, it is not ready for production. Professional automation is as much about controlled failure as successful execution.

Professional automation also requires dependency management. A workflow may depend on Python packages, container images, external APIs, inventory sources, certificates, or model endpoints. Pin versions where appropriate, record compatibility, and make upgrades intentional. An automation outage caused by an unnoticed library change can be just as disruptive as a bad network configuration.

Observability should be designed into the automation itself. Record what the workflow attempted, which devices or controllers were targeted, which changes were proposed, which were applied, and whether validation succeeded. Metrics such as execution time, API error rate, changed-object count, and retry frequency can reveal degradation before users report a problem.

Security deserves the same treatment. Store secrets outside source code, rotate credentials, scope service accounts narrowly, validate certificates, and prevent logs from exposing tokens or sensitive configuration. A professional automation system should be easier to audit than the manual process it replaces.

Finally, practice peer review. Give another engineer your repository and ask them to explain the workflow without you. If the design depends on undocumented assumptions, hidden environment variables, or tribal knowledge, improve the project before calling it production-ready. Maintainability is part of engineering quality.

Another readiness test is portability. Take a small automation pattern—inventory collection, configuration validation, or pre-change checks—and implement it against two different Cisco platforms or APIs. Keep the reusable logic separate from the platform adapter. This shows whether you understand the automation problem or only one SDK.

Use documentation as part of the workflow. Record setup, dependencies, privileges, expected inputs, output schema, recovery, and examples. A repository that only its author can run is not a strong professional artifact, even if the code itself works.

Practice one controlled production-style change from start to finish: open a branch, modify the automation, run tests, review the diff, deploy to a limited target, validate network state, then merge and document the result. This exercise connects source control, CI/CD, platform knowledge, and operational evidence in one workflow.

Also review how certificates, proxies, DNS, and time affect API clients. Automation failures are sometimes infrastructure failures wearing a software label, so prove reachability and trust before rewriting code that previously worked.

Final Readiness Check

• Use CCNP Automation and AUTOCOR v2.0 terminology for current preparation.
• Know the two current concentrations: ENAUTO and DCNAUTO.
• Build with version control, CI/CD, testing, structured data, secure credentials, and post-change validation.
• Understand both the infrastructure state and the automation code that modifies it.
• Apply strong guardrails to AI-assisted and agentic workflows.

The legacy DevNet Professional name remains valuable as a historical search term, but the current skill target is clearer: design, deploy, and operate automation systems that are reviewable, testable, secure, and safe under failure. If your preparation makes you better at controlling change at scale, it is aligned with CCNP Automation.

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