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AURA high-level architecture diagram

AURA

AURA is an explainable multi-agent SEO engineer designed to help developers and businesses build search-ready websites earlier in the development lifecycle.

Modern website development has become much faster with AI-assisted tools and “vibe coding” workflows. Developers can now generate and deploy websites quickly, but SEO is often treated as an afterthought. Important foundations such as metadata, semantic HTML, heading structure, image alt text, internal linking, sitemap configuration, schema markup, keyword targeting, and content quality may be missed. As a result, many websites look good and function well, but struggle to rank on search engines and attract organic traffic.

AURA solves this by splitting SEO work across eight specialized AI agents:

Context Agent
Stack Agent
Technical SEO Agent
Content Agent
Competitor Agent
Approval Agent
Auto-Edit Agent
Report Agent

These agents run as Amazon Bedrock Agents using Claude Sonnet 4.6 and are orchestrated by AWS Step Functions.

AURA first understands the business context, then detects the website stack, such as Next.js, React, Astro, WordPress, or static HTML. This allows it to generate framework-aware recommendations instead of generic SEO advice. It audits technical and content issues, compares competitors, assigns confidence scores, and decides whether improvements should be manually reviewed or automatically applied.

Approved fixes can be written back to source files stored in Amazon S3, with backups, validation, and rollback support.

The solution is fully integrated with AWS services. Many developers and businesses already use AWS to host and deploy websites through services such as Amazon S3 and CloudFront. AURA acts as an additional AWS-powered optimisation layer for these websites, helping users improve discoverability and search performance after deployment.

This makes AURA valuable not only as a standalone SEO tool, but also as a potential value-added service within the AWS ecosystem.

How We Used Kiro

The challenge we targeted is that SEO is often neglected during fast website development, and proper SEO support can be expensive for small businesses and developers. AI-assisted coding tools let websites be built and deployed quickly, but SEO foundations and content quality are often missed. Fixing this later usually requires consultants or multiple separate tools, making SEO an afterthought rather than part of the development process.

AURA solves this as an agentic SEO engineer that fits into the development workflow. It connects directly to source code, understands business context and technical stack, identifies issues, and applies approved fixes automatically or with human control. It runs as eight specialized Amazon Bedrock Agents orchestrated by AWS Step Functions, covering business context, stack detection, technical SEO, content analysis, competitor comparison, approval routing, auto-editing, and reporting.

Kiro was the main development environment and shaped the build through spec-driven development. Requirements, design, and task documents defined the project before implementation, turning the idea into a structured architecture with clear agent responsibilities, shared workflow state, and blocking versus non-blocking failure rules. When the deployed system drifted from the design, the spec became the source of truth that brought it back in line.

Kiro’s task execution broke the project into stages, allowing independent tasks such as agent handlers and infrastructure updates to be built in parallel while staying consistent. Steering rules prevented unnecessary architecture changes and made deviations explicit.

AWS MCP integration was especially useful during deployment because it let Kiro inspect AWS resources, logs, IAM permissions, Bedrock agent aliases, and Step Functions behaviour from the development workflow instead of switching between tools manually.

This paid off during live AWS diagnosis. Kiro helped trace AccessDenied failures to stacked issues, including a Bedrock agent alias frozen on an old model, bringing all eight agents online end-to-end.