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GitLab + Amazon: Platform orchestration on a trusted AI foundation (opens in new tab)

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GitLab Duo Agent Platform and Amazon Bedrock combine GitLab’s software-lifecycle orchestration with AWS’s governed foundation-model infrastructure. Duo coordinates agents across planning, development, security, pipelines, and remediation, while Bedrock provides secure inference within AWS boundaries. The pairing aims to reduce shadow AI, fragmented tooling, unclear data flows, and unplanned cloud spending.

The Enterprise AI Governance Problem

  • Teams often adopt unapproved AI tools, creating unknown prompt and code-data paths.
  • AI tooling and model choices become fragmented across developers and departments.
  • Security teams may lack control over logs, data residency, and access policies.
  • Existing AWS and Amazon Bedrock investments can be underused when teams rely on external point solutions.
  • The proposed division of responsibility is:
    • GitLab Duo Agent Platform: workflow and agent orchestration.
    • Amazon Bedrock: approved models and inference.
    • The organization: IAM, VPC, regional, security, and policy controls.

GitLab Duo Agent Platform as the Control Plane

  • Duo provides specialized agents and flows that operate asynchronously across the software lifecycle.
  • Agents use shared GitLab context, including:
    • Issues
    • Merge requests
    • Pipelines
    • Security findings
  • It extends beyond a single conversational assistant by coordinating multiple agents across continuous workflows.
  • Potential tasks include planning, code development, merge-pipeline work, security scanning, and vulnerability remediation.

Amazon Bedrock as the AI Foundation

  • Bedrock is a managed, serverless foundation-model layer operating within AWS.
  • Customer inputs and outputs are encrypted, not shared with model providers, and not used to train base models.
  • It supports compliance requirements including GDPR, HIPAA, and FedRAMP High.
  • Organizations can use native Bedrock models or import fine-tuned models through Custom Model Import.
  • Bedrock Guardrails can provide content filtering, hallucination detection, and sensitive-data protection.

Deployment Options

The core Duo capabilities remain consistent, but control and infrastructure ownership vary across three patterns:

  • Self-hosted models with Amazon Bedrock

    • Intended for GitLab Self-Managed deployments.
    • Uses a self-hosted AI Gateway.
    • Keeps inference traffic, prompts, logs, and lifecycle data within the organization’s AWS environment.

  • GitLab-operated Bedrock models with GitLab-owned keys

    • Intended for GitLab Self-Managed deployments.
    • Uses GitLab’s hosted AI Gateway.
    • GitLab operates the model layer while the deployment remains self-managed.

  • GitLab.com with GitLab-operated Bedrock models

    • Uses GitLab’s hosted AI Gateway and GitLab-owned keys.
    • Suits organizations that prefer the SaaS GitLab experience while using Bedrock-backed models.

Practical Enterprise Uses

  • Platform teams can standardize models for code suggestions, security analysis, and pipeline remediation.
  • Centralized guardrails and logging reduce independent, unmanaged AI adoption.
  • Security agents can propose and validate fixes directly within GitLab.
  • Routing AI workloads through Bedrock helps organizations align usage with existing AWS agreements and spending commitments.

The recommended approach is to treat GitLab Duo Agent Platform as the orchestration layer and Amazon Bedrock as the governed inference foundation, selecting the deployment model that matches the organization’s compliance, hosting, and control requirements.