John Pratt
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Case Study - TALOS

#solution-architecture#terraform#AWS#AI#large-language-models

TALOS

Author: John Pratt, AWS Solutions Architect Professional

Intro

When we initially thought of TALOS (Terraform-AI Language Orchestration System), our mandate was clear: empower enterprise-scale teams to treat cloud infrastructure as fluid, self-service code. Rather than wrestle with verbose Terraform modules or wait on central DevOps queues, developers and architects alike should be able to describe desired resources in plain English- whether via a CLI or a lightweight web console- and see compliant Terraform manifests generated and deployed automatically. TALOS bridges that gap by combining AWS Bedrock's generative AI, a secure auth layer with Cognito, and a fully automated CI/CD back end, delivering a frictionless “natural-language → infrastructure” workflow .

Problem

Implementing TALOS meant tackling three core challenges:

  1. Language Understanding at Scale Translating free-form prompts like “Provision a VPC with three public subnets across AZs” into idempotent, secure Terraform code demanded rigorous prompt engineering- and a tightly controlled module library to prevent drift or insecure patterns.

  2. Enterprise-Grade Security Exposing AI-driven IaC generation to end users raised red flags around authentication, authorization, and auditability. We needed a zero-trust approach where every request is validated, every code snippet is traceable, and no one could bypass guardrails.

  3. Seamless Deployment Automation Piping generated code into a pipeline without manual intervention required orchestrating CodePipeline and CodeBuild to plan, review, and apply Terraform changes against production-scale AWS accounts- with deterministic rollbacks on failure.

Solution

The final TALOS architecture (see talos.png) looks like this:

  • User Interfaces: A Node.js CLI and a React web app front end, both authenticating against Amazon Cognito.
  • API Layer: AWS Lambda functions receive prompts, validate incoming JWTs via Cognito, and forward structured requests to AWS Bedrock.
  • AI Engine: Bedrock returns Terraform HCL snippets drawn from our audited module repository. Lambdas then trigger CodePipeline.
  • Deployment Automation: CodePipeline executes Terraform plan in CodeBuild, surfaces diffs for optional review, then runs apply to provision or modify resources. All state is stored in an S3-backed Terraform backend, with locks via DynamoDB.

In practice, a developer types talos create-environment “prod us-east-1 k8s cluster with breach detection” and, within minutes, a new EKS cluster appears - fully compliant with configured security standards.

Why TALOS?

  • Natural-language-driven IaC accelerates cloud adoption by collapsing request-to-deployment cycles from days to minutes.

  • Without strict guardrails, AI-generated code can introduce misconfigurations, security gaps, or unintended dependencies. Drift between modules and prompts must be continuously reconciled.

  • In my experience, you should start by scoping prompts to a well-defined domain (e.g., networking or compute). Invest early in a hardened module catalog and incorporate semantic tests (e.g., Sentinel or custom policy checks). Layer in human-in-the-loop reviews only for high-impact changes, and leverage Cognito groups to segment access by role.

Conclusion

By implementing TALOS, we've empowered our enterprise clients to:

  • Accelerate Delivery: Slash infrastructure provisioning times from days of ticketing and handoffs to sub-hour pipelines.
  • Enforce Compliance: Embed security and governance into each prompt, eliminating configuration drift.
  • Boost Developer Autonomy: Reduce busywork for platform teams and let engineers iterate on environments with confidence.

The result is a harmonious balance of speed, security, and scale- exactly what modern enterprises demand.

Interested in learning more or giving TALOS a try? Get in touch to discuss your Cloud, DevOps, & Infrastructure needs.