The Data Lakehouse for Agentic AI: A 2026 Architecture Guide
Key takeaways Four agent behaviours break analytics-era assumptions: many narrow reads, traffic in bursts, reads that turn into proposed writes, and callers that are service identi…
Mactores POV · Written by forward-deployed engineers
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
Newest first, across data platform modernization, application and database modernization, and AI agents for apps.
Key takeaways Four agent behaviours break analytics-era assumptions: many narrow reads, traffic in bursts, reads that turn into proposed writes, and callers that are service identi…
Key takeaways Pick a path per workload, not per estate. Rehost, Babelfish, refactor, and re-architect each leave a different license bill behind, and most estates end up using two …
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…
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…
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…
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…
Key takeaways A forward-deployed engineer (FDE) is an engineer who works directly inside a customer’s environment to solve real production problems, write and deploy code, and rema…
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…
Key takeaways Four model properties set the cost curve: tool-calling reliability, output token verbosity, p95 latency across chained calls, and instruction adherence as the context…
Key takeaways Production is rare, not impossible. 30 percent are exploring agentic options, 38 percent piloting, 14 percent ready to deploy, 11 percent running in production. The b…
No Apps & Databases guides on page 1. See all Apps & Databases guides →
No Data Platform guides on page 1. See all Data Platform guides →
No AI Agents guides on page 1. See all AI Agents guides →
No Firm POV guides on page 1. See all Firm POV guides →
No guides on page 1. Back to all guides →
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.
Forward-deployed engineers.
Weeks, not quarters.