In infrastructure and networking, “technical debt” is often treated like a moral issue: someone cut corners. In reality, debt is what you incur the moment you build something that has to survive contact with production—bugs, operational overhead, user demand, vendor quirks, security controls, and the constant pressure to evolve. None of that is optional. It is the ongoing cost of running a real system.
If you’ve ever deployed a new WAN design, introduced a security control, rolled out a load balancer, or automated a workflow, you already know the truth: delivery is the easy part; lifecycle is the job.
Why Technical Debt Is Getting Worse (Hint: Generative AI)
Generative AI is accelerating delivery—configs, scripts, policies, dashboards, documentation, even full automation pipelines. That speed is valuable. But speed also makes it easier to accumulate debt faster than we can operationalize what we shipped.
AI does not eliminate technical debt. In many environments, it can compound it—especially when teams treat AI output as “good enough” and move on without the engineering rigor that production demands.
Architect mindset: If you can’t explain it, validate it, operate it, and roll it back—then you didn’t ship a solution. You shipped a future incident.
Debt Exists in Every Layer We Touch
From an IT Network Architect’s perspective, technical debt shows up in predictable places:
- Configuration debt: One-off CLI changes that never made it back into standards, templates, or source control.
- Policy debt: Firewall rules added “temporarily” for a cutover and left indefinitely.
- Design debt: Stretched VLANs, overloaded VRFs, inconsistent routing policy, ad-hoc NAT, unclear segmentation boundaries.
- Operational debt: No runbooks, weak monitoring, tribal knowledge, manual procedures, incomplete incident postmortems.
- Dependency debt: Vendor-specific features you can’t replace easily, aging hardware constraints, legacy protocols still in the path.
The important point is not whether you have debt—you do. The point is whether you’re taking on good debt or bad debt.
Good Debt vs. Bad Debt: The Difference Is Intent and a Payoff Plan
Good debt is strategic (like a mortgage)
You take on a larger obligation because it enables a measurable outcome: speed-to-market, risk reduction, platform enablement, or an operational capability you couldn’t deliver quickly otherwise. You can articulate the payoff path.
Bad debt compounds (like high-interest credit cards)
You take on an obligation without a payoff plan. It grows quietly, drains time, and forces emergency work at the worst possible moment—during outages, audits, major migrations, or security events.
Networks and infrastructure are compound-interest machines. Every exception increases troubleshooting time, increases change risk, and reduces clarity when you’re under pressure.
AI-Driven Technical Debt: New Forms You Need to Watch
Generative AI introduces debt categories that many teams are not tracking yet. Here are the big ones I see emerging:
- Prompt debt: “Magic prompts” that only one engineer understands, not versioned, not peer-reviewed, not reproducible.
- Verification debt: AI output is accepted without test coverage, lab validation, or change-control discipline.
- Integration debt: Quick AI automation bolted into workflows without proper error handling, idempotency, or rollback.
- Security and compliance debt: Sensitive data in prompts, uncontrolled model access, weak governance, unclear audit trails.
- Model drift debt: Outputs change as models update, or as context/data sources change—creating non-deterministic behavior over time.
- Operational ownership debt: “We built it” but nobody owns it—no on-call runbook, no monitoring, no SLOs.
None of these are reasons to avoid AI. They’re reasons to treat AI-assisted delivery like any other production engineering effort.
AI Examples: Where Teams Accidentally Manufacture Debt
Here are concrete scenarios that show up in real IT engineering environments:
1) AI-generated firewall rules and security policy changes
Teams paste requirements into a model and get a nicely formatted rule set back—zones, objects, even naming conventions. The problem isn’t formatting. The problem is hidden assumptions:
- Overly broad source/destination scopes
- Misinterpreted application dependencies
- Missing “deny” posture validation
- Inconsistent logging requirements
Debt outcome: rule bloat, audit pain, and “temporary” allowances that become permanent production exposure.
2) AI-written automation scripts for network changes
Generating a Python script or Ansible playbook is easy. What’s usually missing is what production demands:
- Idempotency (safe to re-run)
- Pre-checks and post-checks (state validation)
- Guardrails (change windows, approvals, scope limits)
- Error handling (partial failure, retries, rollback)
Debt outcome: automation that “works in the happy path” but becomes fragile and avoided—then everything reverts to manual work under pressure.
3) AI-generated infrastructure-as-code (Terraform, templates, pipelines)
AI can produce impressive IaC scaffolding. But teams often skip the hard parts:
- State management strategy and drift detection
- Environment promotion patterns (dev → test → prod)
- Secrets handling and least-privilege service accounts
- Module standards and naming conventions
Debt outcome: “copy/paste IaC” that diverges between environments and becomes unmaintainable.
4) AI-assisted incident response summaries and RCA documents
AI can summarize logs and produce a clean narrative. If the team accepts it without scrutiny:
- Root cause becomes a story instead of evidence
- Corrective actions drift into generic recommendations
- Preventative controls don’t get engineered
Debt outcome: repeated incidents with different symptoms but the same underlying gaps.
5) AI chatbots for internal support (NOC/Service Desk)
Many companies deploy “AI assistants” to reduce ticket volume. If the knowledge base is weak, the bot becomes a confident source of misinformation.
Debt outcome: increased escalations, wasted engineer cycles, and reduced trust in documentation.
When AI Creates “Good Debt” vs. “Bad Debt”
A practical litmus test:
Good AI-enabled debt (strategic)
- Clear objective met: requirements are explicit, constraints are understood, and outputs map to a real control objective.
- Business value delivered: faster provisioning, reduced downtime, reduced toil, improved audit posture, measurable outcomes.
- Human understanding exists: another engineer can review, explain, and operate it without reverse engineering a black box.
Bad AI-enabled debt (compounding)
- Unclear value: “we’re doing it because we can” becomes the only justification.
- Spaghetti implementation: brittle scripts, unclear dependencies, no tests, no rollback.
- Authority over merit: adopted because it was mandated, not because it was validated and operationalized.
A Practical Checklist: How to Keep Debt “Good”
Before shipping an AI-assisted change into production, run it through the same discipline you’d apply to any network change. This checklist is intentionally lightweight, but it catches most of the failure modes:
- Can I describe the desired outcome in one paragraph? If not, you are not ready to automate or implement at scale.
- Is there a rollback plan that someone else could execute? If rollback requires heroics or tribal knowledge, debt is already compounding.
- Did we reduce ambiguity, or increase it? Good architecture reduces special cases over time.
- Is observability built in? Logging, metrics, and alerting aligned to expected failure modes.
- Did we document the “why,” not just the “what”? Future engineers can reverse-engineer what you did. They need to know why.
Governance and Guardrails for AI in IT Engineering
If your company is pushing Generative AI aggressively, the answer is not resistance. The answer is controls. Treat AI like any other powerful engineering tool: define guardrails so it scales safely.
Recommended guardrails
- Source control everything: prompts, templates, scripts, policies, and generated artifacts should be versioned.
- Standard prompt patterns: build a reviewed prompt library with known constraints and expected outputs.
- Validation gates: lab testing, CI checks, linting, policy-as-code, and peer review before production changes.
- Least privilege and secrets hygiene: never paste sensitive credentials; use vault integrations and service accounts.
- Observability requirements: define minimum logging/metrics for any automation or AI-driven workflow.
- Ownership model: assign an operational owner, runbook, and escalation path—no orphaned automations.
- Periodic debt paydown: allocate capacity to refactor and standardize AI-driven tooling as it matures.
Managing Technical Debt Like an Architect (Not a Firefighter)
Debt management is not a one-time cleanup. It’s an engineering practice:
- Create a debt register: even a simple backlog with impact, risk, and remediation options.
- Pay interest intentionally: allocate capacity every cycle for hardening, standardization, and documentation.
- Refactor during migrations: refreshes and topology changes are when debt is cheapest to remove.
- Standardize patterns: golden configs, templates, policy models, naming conventions, automation guardrails.
- Stop adding unowned systems: if nobody owns it, it will decay; if it decays, it becomes an outage.
Closing Thought
Technical debt is not inherently a sign of poor engineering. It’s the shadow every real system casts. The professional move is to treat debt like any other engineered constraint: track it, price it, and decide when it’s worth carrying.
Generative AI makes shipping faster. That’s a competitive advantage. But it also increases the rate at which teams can create unvalidated, unowned, and undocumented changes. If you want AI to scale safely in your environment, you need architecture discipline—not just enthusiasm.