Knowledge library
Articles
Engineering decisions, implementation notes, and operational field guides.
11 entries
REST vs Events in Financial Services: Choosing the Right Integration Pattern
A practical guide to choosing between synchronous REST APIs and event-driven integration for financial systems, covering commands, queries, workflows, consistency, fan-out, reliability, and operational trade-offs.
API Versioning Without Creating v1, v2, v3 Chaos
A practical enterprise strategy for evolving APIs through compatibility rules, automated change detection, visible deprecations, measured migration windows, and lifecycle governance.
AI Governance Should Be a Guardrail, Not a Gate
Strong AI governance does not require routing every experiment through a review board. Learn how risk tiers, automated controls, approved safe paths, and targeted human review can protect an enterprise without stopping delivery.
How to Build an Enterprise API Governance Model That Developers Don’t Hate
A practical API governance model built on paved roads, automated contract checks, risk-based reviews, and time-bound exceptions—without turning every API change into a committee meeting.
Why Hundreds of AI PoCs Still Don't Create AI Transformation
AI proofs of concept are easy to start and difficult to operationalize. Learn why experimentation rarely compounds into enterprise transformation—and how product ownership, shared platforms, evaluation, governance, and operating metrics turn isolated demos into repeatable business outcomes.
Why Your Microservices Architecture Became a Distributed Monolith
Excessive synchronous service calls can make independently deployed microservices fail and scale as one system. Learn how to identify runtime coupling, contain cascading failures, and incrementally introduce events, local read models, bulkheads, and sagas.
How to Modernize Mainframe Integrations Without Exposing the Mainframe
A practical architecture for modernizing legacy financial systems with domain APIs, anti-corruption layers, asynchronous messaging, adapters, and stable contracts—without leaking mainframe records, protocols, or release constraints to consumers.
Why AI Is Harder to Implement in Japan—and Why Technology Is Rarely the Problem
Japan has the engineering talent and infrastructure to lead in enterprise AI. The harder challenge is organizational: shortening the path from experiment to production while managing risk, imperfect data, approvals, and continuous learning.
Has AI Flipped the Build vs Buy Decision?
An engineer with a good agentic setup can now stand up in a fortnight what used to take a quarter. The demo is real. But "we can build it faster" and "we should build it" are different claims, and the gap is where a lot of money is currently being set on fire. This post works through the five-year TCO arithmetic, a nine-gate decision tree, and the new failure mode nobody has priced yet: owning a system nobody had time to understand.