Mactores POV · Written by forward-deployed engineers

AWS modernization and AI agents, written from production.

Guides, architecture patterns and cost analysis from an agent-native AWS modernization firm — data platforms moved onto S3 and Iceberg, Oracle and SQL Server retired on Aurora, AI agents running against real operations. Every piece comes out of a program that shipped. No vendor framework. No prophecy. No hedging.

Latest

Latest AWS modernization and AI agent guides

Newest first, across data platform modernization, application and database modernization, and AI agents for apps.

AI Agents

What Is RAG, and Why the Data Layer Decides Everything

Key takeaways Most “hallucinations” are retrieval failures. In 70 percent of the engagements Mactores reviewed, the correct passage was never retrieved. Pipeline quality decides ac…

Ammar Nizami 19 min read
Data Platform

Data Modernization in 2026-27: Three Pillars, One Engine

For most enterprises running on AWS, data modernization has stopped being discretionary. Regulatory reporting, customer-facing AI agents, and forecasting models all depend on datas…

Ammar Nizami 25 min read
Data Platform

AWS Data Lake: Architecture, Services & Build Guide

Key takeaways Governance and architecture decide whether a lake succeeds. Gartner expects 80% of D&A governance initiatives to fail by 2027, so design for governance from day o…

Ammar Nizami 23 min read
Data Platform

Data Center Modernization Beyond Lift-and-Shift

Key takeaways Each path reaches a governed dataset on a different timeline: rehost takes 4 to 6 months and often needs a follow-on project, replatform takes 6 to 10 weeks, and rede…

Ammar Nizami 18 min read
AI Agents

Enterprise AI Strategy: The Three-Pillar Framework

Key takeaways Three pillars divide the work by responsibility: the evidence layer (what systems may read), the action layer (what they may change), and the execution layer (what de…

Gautham B 18 min read

Let's deliver

Reading about it is the cheap part.

Every guide on this page came out of a program that reached production. If one of them describes the problem on your roadmap, the next step is a conversation with the engineers who shipped it — not a discovery deck.

Fixed date. Fixed fee.

Mactores absorbs overage cost on delays inside our control. The clause is standard in our SOW.

Forward-deployed engineers.

Every FDE has shipped production agentic AI on AWS. They own architecture, judgment and cutover.

Weeks, not quarters.

Aedeon, the agent platform we deploy, absorbs the discovery and validation work that used to run long.