Add blog post: Running multi-agent orchestration frameworks on AKS - #5880
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Add blog post: Running multi-agent orchestration frameworks on AKS#5880sabbour wants to merge 1 commit into
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Pull request overview
Adds a new Docusaurus community blog post under website/blog/ that explains practical deployment, scaling, networking, state, security, and cost patterns for running multi-agent orchestration frameworks on Azure Kubernetes Service (AKS).
Changes:
- Introduces a new post: “Running Multi-Agent Orchestration Frameworks on AKS” with front matter (title/date/description/authors/tags).
- Documents recommended Kubernetes patterns for multi-agent workloads (per-role Deployments/Services/Jobs, KEDA-driven scaling, service mesh + egress controls, externalized state, workload identity, and cost controls).
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| Multi-agent AI systems are moving fast from experimental notebooks into production services. Instead of a single model call, these systems coordinate several specialized agents—planners, researchers, coders, critics—that collaborate to complete a task. That shift changes the infrastructure problem: you're no longer serving one stateless model endpoint, you're operating a distributed system with its own scaling, networking, state, and security requirements. Kubernetes, and Azure Kubernetes Service (AKS) in particular, is a natural home for that kind of workload. | ||
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| This post walks through the patterns that matter most when running a multi-agent orchestration framework on AKS: how to deploy, scale, network, secure, and pay for the system. |
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| This post walks through the patterns that matter most when running a multi-agent orchestration framework on AKS: how to deploy, scale, network, secure, and pay for the system. | ||
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| ## What multi-agent orchestration looks like in practice |
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| - **Assign distinct managed identities per agent role** using AKS workload identity, so a compromised tool-executing agent doesn't inherit the planner's permissions. | ||
| - **Sandbox any agent that executes generated code or shell commands.** Give it a restrictive security context, a read-only root filesystem, dropped capabilities, and ideally stronger isolation such as gVisor or confidential containers—never run it in the same container as the reasoning logic. | ||
| - **Store API keys and credentials in a secret store**, such as Azure Key Vault via the CSI Secret Store driver, rather than baking them into images or environment variables. |
sabbour
marked this pull request as draft
July 28, 2026 20:22
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Please ignore. This is part of a test. |
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Adds a new community blog post, "Running multi-agent orchestration frameworks on AKS," under
website/blog/following the existingYYYY-MM-DD-slug/index.mddirectory + frontmatter convention (title/date/description/authors/tags,<!-- truncate -->fold).The post is a vendor-neutral, practitioner-focused walkthrough of the patterns that matter when running multi-agent AI orchestration frameworks (AutoGen, CrewAI, LangGraph, Semantic Kernel, etc.) on Azure Kubernetes Service:
Author key (
brian-redmond) and tags (ai,agent,scaling,best-practices) are validated against the repo'sauthors.yml/tags.yml.Opening for review.