Raj Murugan
I build production AI agents on AWS
Deep technical posts with real code, real gotchas, real architecture decisions. No demo-grade content.
AWS Solutions Architect & AI Engineer · Sydney · Parallo (SoftwareOne)
17 technical posts · AWS Solutions Architect Professional · AWS Certified AI Practitioner
New: A clean pass rate is not calibration Read →
Start Here
All posts →The AI Architect Roadmap
Eight rungs from foundations to enterprise AI, and an honest map of what this site covers deeply, where it is strong, and where the gaps still are.
Recent Posts
View all →A clean pass rate is not calibration
I built an LLM-as-judge eval on my own blog and got a suspiciously perfect 16/16. Here's the three-round test I ran before trusting that number: single-variable corruption, and a self-consistency check the research says most teams skip.
Your LLM security diagram defends the wrong layer
The LLM security diagram you have seen a dozen times is a threat map. Read as a defence it makes you patch every box at the layer the attacker controls. The fix is one deterministic boundary the diagram leaves out, in code the model never touches.
The LLM is not a security boundary
Designing a production agent over sensitive data: no control makes the flow hole-free. You rank the layers, assume each one leaks, and stack them so no single hole reaches the data. Here is the code that does it.
About
Senior Solutions Architect at Parallo (SoftwareOne, AWS Advanced Partner). I take customers from "we want AWS" or "we want GenAI" to a working, governed deployment, then write about how it actually works and where it breaks.
The work I publish here is the work nobody else writes about: the IAM trust policy that takes an afternoon to debug, the VPC cold start that breaks streaming, the OIDC flow that silently fails for a month. AgentCore production gotchas. CDK patterns that survive a real deployment. Cost decisions that show up on the bill, not in the demo.
Featured Work
Production-ready Customer Service AI Agent on Amazon Bedrock AgentCore. Every deployment gotcha documented inline. The companion repo to the 6-part AgentCore series.
An MCP server that turns ADRs, incident reports, and runbooks into a queryable org-knowledge surface for AWS DevOps Agent. Four tools, Bedrock Knowledge Base, frontmatter-filtered chunks. From "agent that reads your docs" to "agent that knows your org."
Three Claude projects with Socratic tutoring, a weekly Cowork routine, three differentiated emails. Open-source spec, full design history including the v1-to-v2 architecture pivot. A personal build whose failure modes mirrored enterprise AI patterns.
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