microservices

The author reflects on the challenges of managing multiple AI agents as they scale, comparing it to infrastructure issues in microservices and mentioning tools like Lyzr for central management.

The author discusses the challenges of scaling monorepos with AI tools like Codex, noting increased token consumption as codebases grow and suggesting that loosely coupled modules may be needed to handle AI context limits.

Monzo has built a separate backup banking infrastructure called Monzo Stand-in to ensure service continuity during major cloud outages, supporting essential features like card payments and bank transfers.

The article discusses how AI coding agents enable small software teams to work with the scale of large teams by running multiple agents in parallel, emphasizing the importance of code modularity for effective parallelism.

The article introduces two official NuGet packages, Microsoft.Extensions.Resilience and Microsoft.Extensions.Http.Resilience, for building resilient .NET applications with built-in strategies like retries, timeouts, and circuit breakers.

A GitHub repository providing hands-on projects to learn Kubernetes, covering basic to advanced topics like deployments, microservices, and cloud integration.

A discussion on whether monolithic AI systems are sustainable, drawing parallels to software monoliths and suggesting that specialized micro-service-style agents working together might be the future.

Introduces ORCA-bench, a production-fidelity benchmark for evaluating LLM agents on oncall root cause analysis, finding that even frontier agents achieve only 25.3% accuracy on medium-difficulty tasks.

An exploration of what microservices actually are, arguing that their primary value is organizational rather than technical, and discussing the trade-offs involved.

This paper presents EaaS, a cloud-native microservices architecture for scalable AI monitoring that provides conformal prediction, calibration assessment, drift detection, and fairness monitoring with statistical guarantees.

This article draws parallels between reliability in manufacturing and modern software engineering, highlighting principles like redundancy, root cause analysis, and observability to build resilient systems.

Explores the distinction between perfection and over-engineering in software development, arguing that over-engineering is solving the wrong problem while perfection emerges from clear requirements.

This paper studies root cause analysis on real-world telemetry data using the OpenRCA benchmark, showing that existing classical and LLM-based methods fail and proposing a Structured Multi-Agent RCA pipeline that substantially outperforms them. It further reveals through reverse reasoning that the primary bottleneck is reasoning capability rather than data access, and introduces automated rule mining to reduce reliance on manual domain knowledge.

A developer is building 402 MCP servers for AI agents and seeks community feedback on which paid microservices would be most valuable for making agents more useful.

A comprehensive argument that PostgreSQL alone is sufficient for most application needs, including caching, search, job queues, and document storage, before reaching for additional specialized systems.

The author draws parallels between the early microservices hype and current multi-agent system hype, arguing that engineering practices—not better models—may be the key to reliable multi-agent systems.

A discussion about using AI Agent for system design and coding in a microservices environment, highlighting the need for the AI to understand service boundaries and business concepts.

The article discusses in a Q&A format how to enable AI Agents to perform system design and coding in a multi-microservice scenario, focusing on practical experiences with context quality (via monorepo, layered documentation) and validation loops (via contract testing, mock servers).

The post argues that AI agent architecture should shift from monolithic agents that hold all context to a routing model where agents delegate tasks to specialized services, similar to how software evolved from monoliths to microservices.

American Express describes its cell-based architecture for its core payments ecosystem that isolates failures, reduces latency, and scales capacity. The approach groups microservices and databases into independent cells to contain blast radius.