Large Language Models (LLMs) like ChatGPT, Claude, and tools like Cursor are transforming how developers write and debug code. They autocomplete YAML, summarize logs, and even generate Kubernetes manifests. But for platform engineers, the mission isn’t just about writing code — it’s about governing, securing, and optimizing the systems that run it. And that’s where today’s LLMs fall short.

The Productivity Mirage

It’s tempting to believe that adding an LLM to your workflow instantly makes your platform team more productive. But platform engineering requires precision, auditability, and control, not just good guesses. When managing clusters, pipelines, and cloud environments, every action must be traceable, validated, and compliant with security and regulatory standards. General-purpose LLMs weren’t designed for that. Let’s break down why.

Why LLMs Alone Don’t Deliver Real Platform Productivity

No Real Context

LLMs understand text — not systems. They don’t have live visibility into your clusters, GitOps pipelines, or policy baselines. So while they can generate YAML or Terraform, they can’t tell if it will actually pass admission control or align with organizational standards.

Short Memory, Long Workflows

Most platform tasks are multi-step: diagnose an issue, validate a policy, remediate a misconfiguration, and push a fix. Even the most advanced LLMs struggle to retain multi-turn context across these workflows, forcing engineers to re-prompt and re-upload context repeatedly. That’s not automation — it’s an expensive copy-paste loop.

Blind Confidence Without Validation

LLMs often produce configuration snippets that look right but aren’t right. Without real-time validation, they can propose insecure RBAC roles, invalid resource limits, or misconfigured network policies — all with convincing explanations. Looks right is not works right, especially in production.

No Audit, No Trust

In regulated industries, traceability equals trust. But most LLM-based assistants don’t log what was generated, why, or who approved it. This creates blind spots in compliance, policy provenance, and change management — unacceptable for teams subject to SOC 2, ISO 27001, or PCI DSS audits.

Operational Reality: Cost, Access, and Real-Time Enforcement

Even with modern tools like Claude Code or Cursor, practical challenges remain when scaling LLM use across platform teams:

• Runaway Costs – As prompts grow to include manifests, logs, and cluster state, token consumption — and costs — rise exponentially. Without optimization, AI usage can become unpredictable and financially unsustainable.
• Access Control and Security Boundaries – Developers may have local access to kubectl, but production environments require strict role-based access (RBAC) and governed credentials. LLMs weren’t built to manage enterprise-grade authorization and auditing.
• No Central Coordination – Each user’s AI context lives in isolation. There’s no shared visibility into which recommendations were made, approved, or deployed — creating fragmented automation and governance drift.
• No Real-Time Enforcement – LLMs can suggest policies, but they can’t enforce them at runtime. Only policy engines like Kyverno can apply rules in milliseconds, evaluate thousands of admission requests, and continuously ensure compliance. LLMs can generate policy code — but not guarantee that it’s enforced or up to date.

From Chat to Control: Enter the Nirmata AI Platform Engineering Assistant

At Nirmata, we saw these gaps firsthand while helping enterprises operationalize Kyverno, the CNCF policy-as-code engine trusted by Fortune 50 teams. So we built something different — the Nirmata AI Platform Engineering Assistant — designed from the ground up for the realities of enterprise-scale, regulated platform engineering.

It’s not just another chatbot. It’s an AI system that connects natural language to policy, context, and control.

How Nirmata Bridges the Gap

Challenge	LLM-Only Tools	Nirmata AI Platform Engineering Assistant
Context Awareness	Works from text only	Continuously aware of clusters, namespaces, and policies
Validation & Safety	Outputs unverified YAML	Runs live policy validation through Kyverno
Audit & Compliance	No traceability	Maintains full audit trails and compliance logs
Observability	Can’t see runtime data	Integrates with Kubernetes events and telemetry for accurate insights
Actionability	Static code suggestions	Executes remediations safely via AI agents
Enterprise Security	No governance	Enforces guardrails through Policy-as-Code and RBAC controls
Scalable Enforcement	Suggests, but can’t enforce	Uses Kyverno for low-latency, real-time policy enforcement

Built for the Real World — and Regulated Ones

Financial services, healthcare, and government organizations can’t rely on opaque AI suggestions. They need assistants that are auditable, explainable, and controlled.

That’s why Nirmata’s AI Assistant provides:

• Full audit trails — every policy, recommendation, and change is logged and attributable
• Explainable AI — all suggestions grounded in cluster and policy context
• Policy-governed automation — actions executed within defined guardrails
• Human-in-the-loop approval — every critical recommendation or remediation can require review and sign-off by authorized engineers before execution, ensuring accountability and safety
• Continuous compliance — assurance across Kubernetes, pipelines, and cloud services

This is AI that enterprises can trust, not just chat with.

Agentic Intelligence, Not Just LLMs

The Nirmata Assistant is powered by specialized AI agents — for policy creation, remediation, optimization, and cleanup — all orchestrated through Nirmata Control Hub. These agents collaborate, closing the loop from detection → recommendation → enforcement → verification.

In other words, it’s not just AI that talks. It’s AI that acts — safely, contextually, and in control.

The Path Forward

LLMs have made AI for developers mainstream. But AI for platform engineers demands more:

• Context over code
• Control over creativity
• Audit over automation

At Nirmata, we’re building the bridge — moving platform engineers from chat to control. Because productivity without governance isn’t progress. It’s risk — automated.

• Explore Nirmata Control Hub for automated drift reporting, governance, and remediation.
• Try Nirmata AI Platform Engineering Assistants for policy generation and remediation
• Learn more about Kyverno policies at kyverno.io/docs.