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Architecting AI agents that maintain cognitive capabilities with finite memory through layered memory design, eviction strategies, and adaptive compression techniques.
How to design systems that switch between strong and eventual consistency at runtime using policy, context, and load signals—without breaking user trust.
A practical guide to building low-latency, memory-centric architectures with NUMA-aware partitioning, affinity scheduling, RDMA, and persistent memory tiers. Includes Go/Rust samples for locality-aware routing, hash rings, and benchmarking remote vs. local access.
Learn how to build multi-tenant systems that maintain data isolation while delivering consistent performance at scale.
Learn how to build reliable event-driven systems using the Transactional Outbox Pattern with Debezium for guaranteed event delivery and data consistency.
How enterprises can embed AI agents into their architecture governance frameworks to create self-correcting, policy-aware, and continuously optimized operating models.
Modern distributed systems must handle unpredictable workloads while maintaining stable performance. This article explores latency-aware design patterns that dynamically adapt queue and backpressure strategies at runtime.
How modern enterprise architects can extend digital twin concepts beyond manufacturing into modeling entire organizational ecosystems for predictive and autonomous decision-making.
How to design AI agents that dynamically adjust their reasoning depth, context window usage, and memory recall complexity based on task difficulty.
Introduces event-driven agent architectures, where agents react to asynchronous events (webhooks, IoT triggers, or other agents) rather than synchronous prompts — enabling near-real-time responsiveness.