Precheck to Rollback Gates for AI Fixes

Introduction to Staged Execution Pattern

Overview of AI‑Generated Interface and Policy Changes

AI‑generated interface and policy changes are configuration artifacts (interface descriptions, VLAN assignments, QoS policies, security ACLs, routing policies) produced by machine‑learning or generative AI systems. Unlike hand‑crafted configs, these artifacts may contain subtle syntactic errors, unintended side‑effects, or hidden dependencies. Because the model optimizes for a learned objective (e.g., traffic‑engineering efficiency) rather than strict operational invariants, the risk of service‑impacting defects is higher than with traditional change‑authoring processes.

Benefits of the Staged Execution Pattern

1. Isolation – change is confined to a well‑defined transaction scope (pre‑check → canary → full rollout).
2. Measurable verification gates – automatic or manual abort before blast‑radius expansion.
3. Bounded rollback authority – rollback limited to the last known‑good configuration and a defined time window.
4. Explicit operator checkpoints – human validation that AI‑generated intent aligns with policy, compliance, and operational expectations.
5. Idempotency clarity – documents where the change can be safely reapplied and where it cannot, preventing hidden drift.

Architecture of Staged Execution Pattern

Prechecks and Validation

The precheck phase validates the AI‑generated artifact before it touches any network element. It consists of:

• Syntactic validation – YANG model conformance, JSON‑Schema validation.
• Semantic validation – policy conflict detection, overlapping ACLs, MTU mismatches.
• Impact analysis – simulated traffic‑engineering using a network‑digital‑twin, resource‑usage forecasting.
• Baseline capture – snapshot of the current running‑config on all target devices for later rollback.
• Policy compliance check – against internal standards, regulatory baselines.

If any precheck fails, the pipeline aborts and raises a ticket for the AI model owners to retrain or adjust the generation prompt.

Canary Releases and Testing

After a successful precheck, the change is applied to a canary set of devices representing a small, statistically significant fraction of the production fleet (typically 1‑5 %). The canary set is chosen to:

• Span multiple hardware platforms, software versions, and geographic sites.
• Carry a mix of traffic profiles (latency‑sensitive, bulk‑transfer).
• Be isolated from critical services via traffic‑shadowing or VRF‑lite separation.

During the canary window, the system continuously monitors:

• Health metrics – interface error counters, CPU/Memory utilization.
• Service KPIs – latency, jitter, packet loss, throughput.
• Policy compliance – ACL hit‑count anomalies, QoS policer violations.

If any metric crosses a pre‑defined threshold, the canary stage triggers an automatic rollback to the baseline captured in prechecks.

Explicit Operator Checkpoints and Approval

Even with automated canary validation, a human‑in‑the‑loop checkpoint is required before promoting the change from canary to full rollout. This checkpoint:

• Presents a diff view of the AI‑generated config against the baseline.
• Shows canary telemetry summaries (graphs, tables, pass/fail criteria).
• Requires an explicit approve or reject action via a signed API call, ChatOps command, or ticket transition.
• Logs the operator’s identity, timestamp, and justification for audit purposes.

Only after approval does the pipeline proceed to the commit boundary, where the change is pushed to the remaining devices in a controlled, paced fashion (e.g., 10 % batches with intervening verification gates).

Implementation of Staged Execution Pattern

Code Examples for Prechecks and Validation

Below is a Python‑based precheck runner that uses pyang for YANG validation and a custom policy engine for semantic checks.

#!/usr/bin/env python3
import json, subprocess, sys, yaml
from pyang import repository, statements

def load_yang_modules(path):
    repo = repository.Repository()
    repo.add_module_path(path)
    return repo

def validate_syntax(config_json, yang_repo):
    # Convert JSON to XML for pyang validation (simplified)
    # In practice, use a proper JSON‑to‑XML converter (e.g., yangson)
    xml = json.dumps(config_json, indent=2)  # placeholder
    try:
        statements.parse(xml, yang_repo)
        return True, None
    except statements.Error as err:
        return False, str(err)

def semantic_policy_check(config_json):
    # Example: detect overlapping IPv4 ACLs
    acls = config_json.get('acl', {}).get('ipv4', [])
    seen = {}
    for acl in acls:
        for ace in acelist := acl.get('aces', []):
            net = f"{ace['src_ip']}/{ace['src_mask']}"
            if net in seen:
                return False, f"Overlapping subnet {net} in ACL {acl['name']}"
            seen[net] = True
    return True, None

def main():
    with open('ai_generated_config.json') as f:
        config = json.load(f)

    yang_repo = load_yang_modules('/opt/yang/modules')
    ok, msg = validate_syntax(config, yang_repo)
    if not ok:
        print(f"SYNTAX FAIL: {msg}", file=sys.stderr)
        sys.exit(1)

    ok, msg = semantic_policy_check(config)
    if not ok:
        print(f"POLICY FAIL: {msg}", file=sys.stderr)
        sys.exit(1)

    # Capture baseline (example using NETCONF via ncclient)
    subprocess.run(
        ["ncclient", "--host", "router01", "--get-config", "--output", "baseline.xml"],
        check=True,
    )
    print("PRECHECK PASSED")
    sys.exit(0)

if __name__ == '__main__':
    main()

The script exits with a non‑zero code on any failure, causing the CI/CD pipeline to halt before any device is touched.

CLI Examples for Canary Releases and Testing

Assuming a containerized orchestration platform (Kubernetes) where each network device is represented by a NetworkDevice custom resource, the canary rollout can be performed with kubectl and a custom rollout controller.

# 1. Label the canary subset (e.g., 3% of devices)
kubectl label networkdevice --selector=region=us-east,role=leaf canary=true --overwrite

# 2. Apply the AI‑generated config in merge mode only to canary devices
#    Read the config into a variable to avoid shell expansion issues
AI_CFG=$(cat ai_generated_config.json)
kubectl patch networkdevice -l canary=true \
  --type='merge' \
  -p="{\"spec\":{\"desiredConfig\":${AI_CFG}}}"

# 3. Run verification jobs (e.g., Prometheus alerts) and wait for a stable window
kubectl wait --for=condition=Ready pod -l app=canary-verifier --timeout=15m

# 4. If verification passes, promote; otherwise, trigger rollback
if kubectl get configmap canary-verification -o jsonpath='{.data.result}' | grep -q PASS; then
  echo "Canary passed – removing canary label and proceeding to batch rollout"
  kubectl label networkdevice --selector=canary=true canary- --overwrite
else
  echo "Canary failed – initiating rollback to baseline"
  BASELINE=$(cat baseline.xml)
  kubectl patch networkdevice -l canary=true \
    --type='merge' \
    -p="{\"spec\":{\"desiredConfig\":${BASELINE}}}"
fi

The canary-verifier pod runs a set of health‑check scripts (interface counters, BGP state, latency probes) and writes a PASS/FAIL result to a ConfigMap.

API Integration for Explicit Operator Checkpoints and Approval

A lightweight approval service exposes a REST endpoint that the pipeline calls after canary validation. The operator interacts via a Slack slash‑command or a ServiceNow approval task.

Approval API (OpenAPI snippet)

openapi: 3.0.1
info:
  title: Operator Approval Service
  version: 1.0.0
paths:
  /approvals/{changeId}:
    post:
      summary: Register operator decision
      parameters:
        - name: changeId
          in: path
          required: true
          schema: {type: string}
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              properties:
                decision:
                  type: string
                  enum: [APPROVE, REJECT]
                justification:
                  type: string
                operator:
                  type: string
      responses:
        '200':
          description: Decision recorded

Pipeline step (using curl)

CHANGE_ID=$(git rev-parse --short HEAD)
RESPONSE=$(curl -s -X POST "https://approval.example.com/approvals/${CHANGE_ID}" \
  -H "Content-Type: application/json" \
  -d '{"decision":"APPROVE","justification":"Canary telemetry within SLA","operator":"priya.raman"}')

if echo "$RESPONSE" | grep -q '"decision":"APPROVE"'; then
  echo "Operator approved – proceeding to full rollout"
else
  echo "Operator rejected or missing justification – aborting"
  exit 1
fi

The service writes the decision to an immutable audit log (e.g., Write‑Once‑Read‑Many storage) and triggers the next stage via a webhook to the CI/CD system.

Troubleshooting and Error Handling

Identifying and Debugging Issues in AI‑Generated Changes

When a precheck or canary stage fails, isolate the failure domain:

1. Collect artifacts – AI‑generated config, validation logs, baseline snapshot, and telemetry from the canary window.
2. Diff analysis – compare the generated config against the baseline and against a known‑good reference config (if available).
3. Policy trace – run the semantic policy engine in verbose mode to output which rule caused the conflict (e.g., overlapping ACL, MTU mismatch).
4. Model introspection – if the failure is semantic (e.g., unintended QoS policy), query the model’s attention weights or feature importance to understand which input prompted the erroneous output.
5. Ticket enrichment – automatically populate a JIRA/ServiceNow ticket with the above data and assign to the AI model owners.

Handling Failures in Canary Releases and Rollbacks

If any verification gate in the canary stage trips:

• Automatic rollback – re‑apply the baseline config captured during prechecks (using the same NETCONF/RESTCONF session).
• Rollback verification – re‑run the same health‑check scripts used in the canary stage to confirm metrics have returned to baseline.
• Rollback window – bounded (e.g., 15 minutes) after which manual intervention is required; prevents an indefinite “stuck‑in‑rollback” state.
• Post‑mortem data – log the rollback attempt, success/failure, and timestamps to the audit store for trend analysis.

Managing Operator Checkpoint and Approval Errors

All operator interactions are logged with user ID, timestamp, IP address, and the exact payload for forensic review.

Scaling and Limitations of Staged Execution Pattern

Horizontal Scaling and Distributed Architecture

The pattern scales horizontally by:

• Sharding the device inventory across multiple validation workers (e.g., using a work‑queue like RabbitMQ or Kafka). Each worker runs the precheck script on a disjoint subset of devices.
• Canary selection using consistent hashing so that adding or removing workers does not reshuffle the entire canary set, maintaining stability.
• Approval service deployed behind a load balancer with multiple stateless instances; state (decision logs) is written to a durable, replicated store (e.g., Cassandra or CockroachDB).

Vertical Scaling and Resource Limitations

Vertical limits arise from:

• Validation resource consumption – YANG validation and policy engine can be CPU‑intensive for large models. Mitigation: cache validation results for unchanged modules, and use just‑in‑time compilation of policy rules.
• Baseline storage – storing a full config snapshot for every device may exceed disk capacity on the validation host. Solution: compress snapshots (e.g., using zstd) and store them in an object store (S3, GCS) with lifecycle policies.
• Telemetry ingestion – canary verification may produce high‑frequency metrics; use a time‑series database with down‑sampling (e.g., Prometheus with remote write to Cortex) to avoid overload.

Bounded Rollback Authority and Limitations

Rollback authority is bounded by:

• Baseline age – the baseline captured in prechecks must be recent enough to reflect the current network state. If the baseline is older than a configurable threshold (e.g., 30 minutes), the system flags a stale baseline and requires a fresh snapshot before proceeding.
• Stateful changes – certain AI‑generated modifications (e.g., dynamic ACLs that reference learned address groups, or QoS policies that install hardware counters) may not be fully reversible by config replace alone. In such cases, the pattern documents that rollback is best‑effort and may require additional procedural steps (e.g., clearing learned state, resetting counters).

End of document.