AI-Assisted Network Control and Configuration Architecture

A centralized, network-aware CLI and policy-governed design for managing networks as coordinated systems

Technology Concept Paper

Author: Christopher Soans
Date: September 2026

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Core principle: Configure the network as a coordinated system, not as a collection of individual devices.

Working Paper

Conceptual architecture for discussion and further development

Figure 1. AI-Assisted Network Control and Configuration Architecture

Executive Summary

Modern networks are still commonly administered as collections of individual devices. Engineers may use SSH, vendor-specific command lines, templates, scripts, controllers, and APIs, yet a locally valid change can remain inconsistent with the topology, security model, traffic requirements, redundancy objectives, or approved architecture of the network as a whole.

This paper proposes an AI-assisted network control and configuration architecture built around an independent AI Control/Intelligence Network. Its primary interface is a centrally brokered, streaming terminal environment that preserves the familiar native CLI of each router, switch, firewall, load balancer, or wireless platform. The engineer selects a device from a web topology view or enters its hostname, then works interactively without establishing and authenticating a separate SSH session manually.

During the session, the selected AI-aware device receives authorized context from the central controller: physical and logical topology, its intended role, application traffic requirements, approved standards, security boundaries, redundancy objectives, and relevant operational state. It can therefore provide autocomplete and configuration guidance based not only on valid local syntax, but also on what is appropriate for the network design.

Proposed enhancement: Give engineers the familiarity and control of a native device CLI, enhanced by the network-wide knowledge, standards enforcement, safety checks, and change intelligence of the AI Control/Intelligence Network.

The design is not intended to remove network engineering expertise. It makes that expertise reusable and enforceable. Architects remain responsible for protocol design, security boundaries, failure behavior, standards, and exceptions. AI and automation assume the repetitive work of discovery, configuration generation, dependency analysis, validation, documentation, drift detection, and approved low-risk remediation.

1. Current Operational Problem

Network complexity arises not only from OSPF, IS-IS, BGP, MPLS, SD-WAN, SDN, QoS, wireless, security, and load balancing. Much of it results from operating these technologies through fragmented tools and device-centered workflows.

• A command may be syntactically correct but architecturally wrong.
• Configuration logic is duplicated across vendors, device families, sites, and operational teams.
• Engineers must manually reconstruct dependencies among routing, switching, security, QoS, load balancing, addressing, and applications.
• Standard configurations gradually drift as emergency changes, hardware replacements, and local exceptions accumulate.
• Troubleshooting depends heavily on the engineer knowing where to look and correlating data from many systems.
• Automation often pushes templates efficiently without proving that the network-wide outcome will be correct.

1.1 The limits of SSH and device-local assistance

SSH and the command-line interface remain valuable for recovery and expert diagnostics, but they expose the device rather than the intended service. An AI-aware network operating system could autocomplete valid commands, explain syntax, and reject unsupported options. That is helpful, but its field of view remains local.

Capability	Device-local AI	Network-wide AI controller
Syntax validation	Strong	Strong
Hardware and OS awareness	Strong	Aggregated from all devices
Topology awareness	Limited to neighbors/local tables	End-to-end physical and logical model
Application and traffic context	Partial	Cross-domain and policy-linked
Change impact prediction	Mostly local	Network-wide failure and dependency analysis
Standard enforcement	Local rules	Enterprise baseline, roles, sites, and exceptions

2. Existing Foundations and the Remaining Gap

Structured management mechanisms already provide important building blocks. NETCONF supports remote configuration operations and capabilities such as candidate configuration, validation, confirmed commit, and rollback-on-error. YANG supplies structured data models. gNMI supports structured configuration and operational data. Automation frameworks can render and apply vendor-specific resources, while network-analysis systems can evaluate configuration behavior before deployment.

These mechanisms reduce reliance on manual CLI work, but they do not by themselves create a continuously maintained understanding of business intent, application priority, end-to-end topology, security boundaries, device limitations, and observed behavior. The proposed architecture combines these elements into one governed reasoning and execution system.

3. Proposed Architecture

The AI Control/Intelligence Network is a dedicated management and intelligence plane. It collects state without depending entirely on the production data path and distributes approved control actions through authenticated, least-privilege interfaces.

Component	Primary responsibility
Central management console	Provides device search and topology selection, native streaming terminals, session status, AI guidance, and approval interaction.
Identity and session broker	Authenticates the engineer, verifies per-device authorization, issues short-lived credentials, and preserves individual attribution.
Intent and policy interface	Captures service requirements, security constraints, availability targets, traffic priorities, maintenance rules, and approvals.
Network digital model	Maintains physical topology, logical overlays, routing domains, addressing, applications, dependencies, and intended state.
Capability registry	Records each device model, software release, licenses, interfaces, scale limits, supported features, and known constraints.
Standards repository	Stores mandatory baselines, role templates, site standards, approved equivalents, and time-bounded exceptions.
AI context and planning engine	Supplies device-relevant network knowledge during interactive sessions and produces coordinated plans for optional bulk automation.
Device translators	Produce native CLI, NETCONF/YANG, RESTCONF, gNMI, API, or automation payloads appropriate to each target.
Validation environment	Tests syntax, reachability, routing, security, QoS, redundancy, capacity, failure behavior, and rollback.
Deployment and assurance	Stages changes, monitors telemetry, verifies expected outcomes, and commits or rolls back.

3.1 Central management console and streaming CLI

The normal engineering interface is a secured console on the AI Control/Intelligence Network. After authenticating once, the engineer can type a hostname, search by site or role, or click a device in the topology view. The console then creates a live, bidirectional terminal stream to the selected device. The terminal presents the device's native IOS-style or vendor-specific CLI rather than forcing the engineer to use an unfamiliar abstract language.

• Maintain the responsiveness, command history, modes, prompts, and diagnostic capabilities of a direct SSH session.
• Display the selected hostname, platform, software version, device role, standards profile, compliance state, and active maintenance context.
• Provide command completion that combines native syntax with network design, security policy, topology, and traffic requirements.
• Explain why a recommendation is being made and identify the devices, services, routes, or traffic paths that may be affected.
• Allow the engineer to review, modify, accept, or reject a recommendation according to authorization and risk policy.

3.2 Two complementary levels of intelligence

The controller supplies network-wide design and operational context. Device-local intelligence determines whether and how the proposed command can be realized on that specific hardware and software release. A device must report incompatibility rather than silently omit a requested control.

Control boundary: AI may propose and translate a change, but deployment authority remains governed by risk classification, human approval, maintenance policy, and rollback readiness.

3.3 Brokered identity and accountable access

The console should not distribute a reusable shared administrator password. The engineer authenticates strongly to the central console; an identity broker verifies authorization for the selected device and issues a short-lived credential, certificate, or session token. The device records the engineer's individual identity, while the credential expires when the session ends or after a short policy-defined interval.

This model supports single-console access without sacrificing attribution. Direct emergency access remains available through a controlled break-glass path if the controller or management console is unavailable.

4. Approved Standardized Configuration Management

An authoritative configuration hierarchy should be applied to every managed device. More-specific layers may extend broader standards, but may not weaken mandatory controls without an explicit, recorded exception.

Layer	Examples
Mandatory baseline	AAA, role-based access, secure management protocols, approved encryption, logging, telemetry, NTP, certificate requirements, control-plane protection, configuration retention, and prohibited services.
Device-role standard	Core router, leaf switch, access switch, firewall, load balancer, wireless controller, edge router, DPU/SmartNIC, or backup-fabric node requirements.
Site and environment	Addressing, routing areas, route reflectors, VRFs, WAN circuits, cloud regions, regulatory boundaries, and maintenance windows.
Service-specific intent	Application reachability, segmentation, load balancing, QoS, multicast, resiliency, and observability.
Approved exception	Named justification, owner, compensating control, review date, expiration date, and affected devices.

4.1 Compliance states

• Compliant with the required control.
• Compliant through an approved equivalent control.
• Temporarily exempted with an owner and expiration date.
• Partially compliant and subject to remediation.
• Noncompliant and blocked from deployment or production admission.

A locally accepted command must not override the enterprise baseline. Attempts to enable insecure management, remove required telemetry, weaken control-plane protection, bypass the management VRF, or alter protected routing policy should be rejected or escalated even when the device would accept the syntax.

5. Traffic-Aware Network Engineering

Efficient network design requires an understanding of what traffic represents, not merely how many bits traverse an interface. The controller should combine declared application identity with measured flow behavior, source and destination roles, tenancy, service-level objectives, and security classification.

• Latency and jitter sensitivity
• Loss tolerance and retransmission behavior
• Bandwidth volume and burst characteristics
• Business criticality and recovery priority
• Data sensitivity and regulatory restrictions
• Expected paths during normal operation and failure
• Permitted networks, destinations, and time windows

Traditional markings such as DSCP remain useful enforcement signals, but the controller should validate whether markings match authenticated workload identity and approved policy. It should also confirm that treatment is consistent across every hop rather than configuring QoS independently on each device.

5.1 Dedicated backup-network treatment

Backup traffic illustrates why intelligent QoS cannot replace sound architecture. Backup flows are high-volume, long-lived, bursty, and deadline-driven. Placing them only in a low-priority production queue can protect interactive applications while allowing backup windows to overrun, overlap, or become unpredictable.

Condition	Controller behavior
Normal backup cycle	Keep traffic on the dedicated backup fabric and verify completion forecasts.
Backup approaching deadline	Identify bottlenecks, alert operations, and increase backup-fabric capacity when permitted.
Urgent restore	Elevate recovery traffic within the backup fabric because its business priority has changed.
Backup-fabric failure	Use production only through an approved, rate-limited, time-bounded fallback policy.
Suspected ransomware	Restrict access paths, preserve immutable copies, and require elevated authorization.

Design rule: Separate traffic with fundamentally different volume, timing, security, and failure characteristics; use QoS primarily to manage contention within the appropriate traffic domain.

6. Interactive Network-Aware Configuration Workflow

The primary workflow begins with an engineer selecting a device and working in its native CLI. The AI continuously evaluates the developing command sequence against the device, the approved design, and the current network state. Commands remain under engineer control, but the session behaves like a modern programming environment with contextual completion, validation, explanations, and change-impact feedback.

1. Authenticate the engineer once and verify authorization for the selected device.
2. Create a brokered streaming terminal session and show the device identity, role, software, standards profile, and compliance state.
3. Load only the network context and configuration standards relevant to that device and the engineer's authorized task.
4. Interpret each developing command in its native configuration mode and offer context-aware completion.
5. Identify local prerequisites, companion configuration on other devices, policy deviations, and predicted network-wide effects.
6. Allow routine commands, warn or require confirmation for deviations, and route high-risk changes for approval.
7. Create a candidate change, validation evidence, audit record, and rollback plan before final commit when the platform supports it.
8. Verify the result through telemetry and active tests, then update intended state and compliance records.

6.1 Example service request

Engineer intent: Add an application to the production VRF, provide redundant connectivity through two datacenters, prioritize transactional traffic, deny access to the backup network, and preserve service during a single-link failure.

From this request, the controller may determine that changes are required to VLAN/VXLAN membership, IP addressing, routing advertisement, route policy, firewall rules, load-balancer pools, QoS classification, monitoring, and failure tests. It should expose the entire dependency chain before deployment rather than leaving the engineer to discover it across separate systems.

6.2 Example in-session guidance

Native command context: If an engineer begins configuring BGP-to-OSPF redistribution, the device can warn that the enterprise standard requires an approved route map, route tagging, loop prevention, and a maximum redistributed-prefix threshold. It can show the affected sites and suggest the valid platform-specific command sequence before anything is committed.

6.3 Optional intent compilation and bulk automation

For repetitive or multi-device work, the same controller can offer a second operating mode in which structured or controlled natural-language intent is compiled into coordinated candidate configurations. This mode is optional rather than a replacement for the interactive CLI. It is most suitable for standardized deployments, repeated site builds, device replacement, drift remediation, and approved bulk changes.

7. Validation, Safety, and Security

Because the controller can affect many devices, its authority must be narrower and more rigorously governed than ordinary administrative access. The architecture should assume that generated configuration may be incomplete or wrong until deterministic checks and observed results prove otherwise.

Control	Required behavior
Identity and access	Role-based permissions, strong authentication, separation of request/approval/deployment duties, and just-in-time access.
Change integrity	Signed artifacts, immutable audit records, version history, named approvers, and traceability from intent to device commands.
Pre-deployment testing	Syntax, policy, route leaks, loops, reachability, MTU, QoS, capacity, failure scenarios, and management-path preservation.
Staged execution	Canary devices or sites, bounded batches, health gates, and automatic halt conditions.
Rollback	Precomputed rollback, checkpoints, confirmed commits where supported, and independent recovery access.
Controller resilience	Redundant controllers, protected credential stores, limited blast radius, and continued device operation during controller loss.
AI boundaries	No direct unreviewed execution for high-risk changes; deterministic policy engines override probabilistic recommendations.

7.1 Risk-based authority

• Low risk and reversible: automatic execution may be permitted within approved windows.
• Moderate risk: operations approval and staged rollout are required.
• High risk: network engineering and security approval are required.
• Uncertain evidence or unexpected state: stop, preserve current operation, and escalate rather than guess.

8. Operations and Organizational Model

Role	Primary responsibility
Network architects and engineers	Architecture, protocols, policy, exceptions, complex failures, standards, and approval of high-risk change.
AI control platform	Discovery, correlation, configuration generation, validation, recommendations, documentation, drift detection, and approved remediation.
Technical operations	Guided physical work, cabling and optics checks, routine verification, evidence collection, and execution of preapproved procedures.
Security and governance	Control requirements, approval thresholds, compliance evidence, exception governance, and audit oversight.

This model does not reduce the need for expertise. It redirects expert time from repetitive device administration toward design, resilience, security, capacity, and unusual failure analysis. Technical operations personnel can perform more routine work safely because the system supplies validated instructions and confirms each observation against expected state.

9. Expected Benefits

• Fewer outages caused by syntactically valid but architecturally incorrect changes.
• Consistent security and management baselines across vendors and device roles.
• Reduced configuration drift and faster replacement of noncompliant devices.
• Simpler multi-vendor operations through vendor-neutral intent and device-specific translation.
• More predictable application behavior through end-to-end traffic awareness.
• Safer delegation of routine tasks to technical operations personnel.
• Faster troubleshooting through correlated topology, telemetry, configuration, and change history.
• Better use of engineering expertise and improved scalability of the operations organization.
• Auditable linkage among business requirement, approved design, deployed configuration, and observed result.

10. Limitations and Design Risks

• Incomplete or stale topology data can lead to incorrect conclusions.
• Vendor data models and feature semantics remain inconsistent.
• Encrypted traffic limits payload inspection and increases reliance on trustworthy application identity and metadata.
• A centralized controller can expand blast radius if authorization and staged deployment are weak.
• Natural-language intent can be ambiguous and must be translated into explicit, reviewable constraints.
• Legacy equipment may not support transactional configuration, rollback, modern telemetry, or structured APIs.
• Automation can propagate a flawed standard rapidly; standards therefore require versioning, testing, ownership, and gradual rollout.

These risks favor a hybrid approach: AI for interpretation, correlation, and proposal generation; deterministic validation for invariants; human approval proportional to risk; and independent telemetry for outcome verification.

11. Phased Implementation

Phase	Scope	Outcome
1. Observe	Inventory, topology discovery, configuration collection, telemetry, and drift reporting.	A trustworthy network model without configuration authority.
2. Recommend	Generate proposed changes, explanations, dependency maps, and validation results.	Engineers retain manual deployment while assessing accuracy.
3. Controlled deploy	Automate low-risk changes with approval, canaries, health gates, and rollback.	Measured reduction in routine work and change failure.
4. Closed-loop assurance	Continuously verify intended outcomes and remediate approved deviations.	The operational network remains aligned with approved design.
5. Cross-domain optimization	Coordinate campus, datacenter, WAN, cloud, wireless, security, and application requirements.	Network-wide intent replaces fragmented domain workflows.

12. Conclusion

Network management should progress beyond opening and authenticating separate SSH sessions to individual devices, while preserving the familiar native CLI that experienced engineers use effectively. A device can determine whether a command is syntactically valid; only a network-wide intelligence system can determine whether that command is appropriate for the topology, traffic requirements, security policy, resilience model, and operational priorities.

The proposed AI Control/Intelligence Network would provide a single secured console, broker device access using the engineer's individual identity, and stream an interactive terminal session to the selected device. The device would combine its knowledge of local syntax and hardware with controller-provided knowledge of the overall network, enabling design-aware autocomplete, warnings, recommended configuration lines, and standards enforcement in real time.

Intent compilation and bulk automation remain useful additional modes, but they do not replace the engineer-centered CLI workflow. The objective is to improve the engineer's visibility and precision while keeping configuration understandable and directly controllable.

Final principle: The goal is not autonomous configuration for its own sake. The goal is a network that remains understandable, secure, supportable, and aligned with its intended design throughout its lifecycle.

References

• IETF RFC 6241. Network Configuration Protocol (NETCONF). https://www.rfc-editor.org/info/rfc6241/
• IETF RFC 7950. The YANG 1.1 Data Modeling Language. https://www.rfc-editor.org/info/rfc7950/
• gRPC Network Management Interface (gNMI) Specification. https://openconfig.net/docs/gnmi/gnmi-specification/
• Ansible Documentation. Network Resource Modules. https://docs.ansible.com/projects/ansible/latest/network/user_guide/network_resource_modules.html
• Batfish Documentation. Network configuration analysis and validation. https://batfish.readthedocs.io/en/latest/

Appendix - Network-aware centralized CLI

The engineer would log in once to a management console on the AI Control/Intelligence Network. From there, the engineer could:

• Type a hostname, IP address, device role, or site name.
• Select a device from the topology view.
• Open a live, streaming terminal session to that device.
• Use the familiar native IOS, Junos, EOS, NX-OS, or other vendor CLI.
• Receive autocomplete and configuration guidance informed by the entire network.
• Apply configurations interactively, just as with an ordinary SSH session.

The experience might look like:

AI-NETWORK-CONSOLE> connect DAL-CORE-01

Connecting to DAL-CORE-01...

Platform: Router Model X

Software: Version 17.x

Role: Datacenter core

Standards profile: DC-Core-v4.2

Compliance state: Compliant

DAL-CORE-01# configure terminal

DAL-CORE-01(config)# router ospf 100

DAL-CORE-01(config-router)# redistribute bgp 65001

Before the engineer enters the final command, the system could provide:

AI WARNING:

This redistribution is not permitted by the approved datacenter-core standard.

Potential effects:

- 2,418 BGP prefixes could enter OSPF.

- Redistributed routes would reach three datacenter sites.

- No approved route-map or route-tagging policy is attached.

- A return redistribution point exists on DAL-EDGE-02.

Recommended configuration:

redistribute bgp 65001 subnets route-map BGP-TO-OSPF-APPROVED

Required companion controls:

- Apply route filtering

- Set redistribution tags

- Reject routes already tagged as OSPF-originated

- Validate the route count against the approved threshold

Continue with the recommended configuration? [review/apply/cancel]

© 2026 Christopher Soans. All rights reserved.

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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