Go

Prerequisite: This is the starting part of the series — no prior part is required. Later parts assume the concepts introduced here. Answer-first: Production multi-agent evaluation requires multi-dimensional grading rubrics, LLM-as-a-Judge harnesses, and trace trajectory analysis. Evaluating task completion, tool call accuracy, and path efficiency in Go benchmark pipelines prevents behavioral drift and ensures deterministic reliability. Evaluating autonomous agent systems presents unique architectural challenges distinct from traditional software testing and static model benchmarking. While standard software unit tests verify deterministic inputs against exact output matches, multi-step LLM agents exhibit non-deterministic reasoning paths, dynamic tool selection, and complex conversation states. Relying solely on final answer accuracy masks hidden regressions such as redundant tool invocations, inefficient context expansion, or subtle prompt injection vulnerabilities. ...

Prerequisite: Familiarity with the concepts introduced in Part 5 — Agent Evals. Review it first if the terminology in this part is unfamiliar. Answer-first: Enterprise agentic systems require stateful Human-in-the-Loop (HITL) interception gateways, architectural guardrails, and OWASP security controls. Suspending autonomous agent workflows before executing high-risk financial or destructive mutations guarantees regulatory compliance and mitigates prompt injection vulnerabilities. Deploying autonomous multi-agent systems into core enterprise operations requires balancing full autonomy with risk governance. Allowing AI agents to execute destructive database updates, financial transactions, or external communications without explicit human oversight exposes systems to prompt injection attacks, model hallucinations, and severe compliance violations. ...

Agent Orchestration Frameworks vs. Vendor-Specific Agent SDKs Answer-First Summary: Enterprise AI architecture requires selecting between open multi-provider frameworks (LangGraph, AutoGen 0.4, CrewAI) for cyclic control flow, persistent state snapshots, and vendor independence, or direct vendor SDKs (OpenAI, Claude SDK, Google ADK) for sub-5ms latency, native prompt caching (90% cost reduction), and zero wrapper overhead. Polyglot production systems integrate Python agent workers with Go core microservices via Dapr sidecars. Executive Verdict & Paradigm Split Modern enterprise software systems deploying LLM agents face a foundational architectural choice between two distinct paradigms: ...

High-Throughput Event-Driven Microservices in Go with NATS JetStream & CQRS Section 1: Architectural Rationale: Why Go + NATS JetStream for Event-Driven Microservices Beyond tens of thousands of transactions per second, synchronous request-response designs start hitting database write contention and cascading latency spikes. Command Query Responsibility Segregation (CQRS) paired with Event-Driven Architecture (EDA) isolates write commands from analytical queries, letting each side scale independently. To overcome these structural boundaries, high-scale engineering organizations adopt Command Query Responsibility Segregation (CQRS) paired with Event-Driven Architecture (EDA). By explicitly separating the write path (commands) from the read path (queries), CQRS allows each side to scale independently according to its access patterns. Commands execute lightweight state mutations against write-optimized engines, emitting immutable domain events into a high-performance message broker. Decoupled consumer workers asynchronously consume these events to populate specialized, read-optimized views (such as Redis key-value pairs, Elasticsearch documents, or PostgreSQL materialized read tables). ...

AWS EKS vs ECS: Architecture, Cost & Use Cases (2026) Answer-first: When deciding between AWS ECS and EKS, choose ECS Fargate for speed and zero control plane costs if you lack Kubernetes expertise. Choose EKS if you require the CNCF ecosystem (ArgoCD, Dapr, KEDA) and have dedicated DevOps engineers to manage the $73/month control plane fee. Feature AWS ECS (Elastic Container Service) AWS EKS (Elastic Kubernetes Service) Control Plane Cost $0 (Free) $73/month (~$876/year) Scalability Good (ASG-based) Excellent (Karpenter ~45s provisioning) Complexity Low (AWS-native) High (Requires Kubernetes expertise) I’ve run both in production. At Vigo Retail, I architected a 21-service Go microservices platform on EKS handling 8,000 RPS peak and 25M+ requests/month. I’ve also managed ECS clusters for smaller AWS-native projects. This guide is what I wish existed before I made those decisions. ...

Composable Banking Architecture: Monolith to Modular Answer-first: The composable banking architecture pattern replaces monolithic core banking systems with modular, independent Packaged Business Capabilities (PBCs). By leveraging Go microservices, Saga orchestration, and the Strangler Fig migration pattern, banks can decouple their legacy ledgers without risky “Big Bang” cutovers. Migration Path from Monolith to Composable Transitioning to a composable core requires a phased approach to mitigate operational risk: API Gateway & Anti-Corruption Layer (ACL): Shield the legacy core behind a gateway and translate modern API requests into legacy formats using an ACL. Shadow Routing: Deploy the new composable service (e.g., a new Go-based ledger) in parallel. Mirror live traffic to it and reconcile the outputs without affecting actual customer balances. Incremental Cutover (Strangler Fig): Once reconciliation achieves 100% parity, route read traffic to the new service, followed by write traffic, effectively “strangling” that specific domain out of the monolith. Legacy core banking systems were designed in a different era. Temenos T24, Finacle, and Flexcube shared one defining assumption: the bank’s entire product catalogue — deposits, lending, payments, trade finance — would live inside a single, tightly coupled application and a single, shared database. That assumption held when banking moved at human speed. It breaks completely when product releases need to go from months to days, when a single fraud engine update must not risk a payments outage, and when engineers on a COBOL codebase are retiring faster than they can be replaced. ...

Banking Microservices in Go: Saga & Event Sourcing 1. Introduction: Deconstructing the Legacy Core Legacy banking platforms like Temenos T24 and Oracle FLEXCUBE were designed as rigid transactional monoliths for batch processing. Digital banking now requires decomposing these into event-driven microservices capable of real-time payments with sub-10ms latency. Migrating to a microservices architecture means dismantling these bottlenecks: Scaling limitations: Monoliths scale vertically (costly hardware), while microservices scale horizontally. Release cycles: Legacy cores require massive, risky quarterly releases. Microservices enable independent deployments. Data locking: Central databases in monoliths create severe lock contention during high-velocity events (like payday processing). By leveraging Go’s highly concurrent runtime and a distributed event-driven architecture, we optimize the system for <10ms database writes at 10,000 TPS, ensuring scalability and fault tolerance. ...

Dapr Workflow Go Tutorial: Orchestrated Saga Pattern Compensation handlers configuration in Dapr to guarantee atomic rollback. How to handle transient workflows when the orchestrator instance restarts mid-transaction. Most Go developers building microservices know the Choreography Saga pattern: service A emits an event, service B reacts, service C reacts to B, and so on. If step C fails, services emit “compensation” events in reverse order. The pattern works elegantly for simple flows, but breaks down as the number of steps grows: debugging a failed saga requires tracing events across five message broker topics, and implementing compensation logic requires every service to understand the full saga’s state. ...

Go pprof in Kubernetes: Remote Profiling & Flame Graphs You’ve instrumented your Go service with net/http/pprof, run go tool pprof locally against the development binary, and spotted the hot path in your flame graph. Then you deploy to Kubernetes and the bottleneck disappears — because the workload profile in Kubernetes differs from local testing (different request mix, connection pool pressure, GC behavior under actual memory pressure, scheduler interference from co-located pods). ...

Golang Goroutine Pool Patterns: errgroup & Backpressure Preventing goroutine leaks in high-concurrency worker pools using errgroup. Writing robust worker pools that propagate context cancellation to all active goroutines. Every Go engineer eventually writes the same mistake: a loop that launches goroutines unconditionally. In a demo with 10 items, this works beautifully. In production with 50,000 incoming webhook events, it spawns 50,000 goroutines simultaneously, exhausts memory, and triggers the OOM killer. Kubernetes restarts the pod. The on-call engineer gets paged at 3 AM. ...

Dapr State Store Consistency Trade-offs Explained Dapr State Store Architecture & Consistency Models In distributed applications, state management remains one of the most complex challenges. When transitioning to a microservices architecture, each service typically requires independent data storage and querying capabilities. This leads to technology fragmentation, where a system might simultaneously use Redis for caching, PostgreSQL for transactional data, and Cassandra for large unstructured data. Dapr (Distributed Application Runtime) emerged to solve this issue through a robust abstraction mechanism. ...

LeaseInVietnam: AI-Powered Expat Rental & B2B Lead Engine Most AI content projects are built around one question: how do I publish more? LeaseInVietnam is built around a different question: how do I make every published piece convert? The system is an autonomous relocation hub targeting expats and digital nomads renting in Southern Vietnam — Ho Chi Minh City, Nha Trang, Phú Quốc. It produces content in American English, publishes daily via GitOps, and routes every reader interaction toward a B2B lead funnel that pays commission on moving services, cleaning bookings, furniture rentals, and legal consultations. ...

Answer-First: Go 1.26 compiler tooling introduces automated //go:fix inline AST transformations, Dapr v1.16.13-rc.1 resolves sidecar stream reconnections during scheduler restarts, and Kratos v2.9.2 hardens Consul metadata cloning to eliminate microservice memory leaks. Tech Radar, April 14, 2026: Safer Code Evolution, Runtime Recovery, and Framework Hardening The selected items for pipeline run 6 form a coherent picture of where mature platform engineering is heading. After fetching and reading the full source content directly from the original URLs, the common theme is clear: strong systems are not defined only by what they can do, but by how safely they evolve, how predictably they recover, and how much accidental complexity they remove from the teams building on top of them. ...

Mastering Event-Driven Architecture with Dapr Pub/Sub in Go In my previous post, we explored how abandoning monolithic architecture in favor of strict Domain-Driven Design (DDD) bounded contexts allowed an e-commerce platform to scale beyond 10,000+ orders per day. However, splitting one big database into 20+ isolated Postgres databases introduces a terrifying new problem: How do we maintain data consistency across disconnected services? The answer is Event-Driven Architecture (EDA). Rather than chaining blocking synchronous HTTP calls across the network — which guarantees a cascading failure if a single service is down — each microservice independently broadcasts out-of-band “Events” through a centralized broker. Services are decoupled from each other’s availability. A brief outage in the Notification service does not cause a checkout failure. ...