Articles

🔍 API Gateway vs Service Mesh: Beyond the North-South/East-West Myth

Challenges the north–south/east–west myth and reframes the Gateway vs Mesh choice as a purpose and trust-domain problem. Explains deployment/control-plane differences, sidecar-based mTLS and CA boundary constraints, and shows how API Gateways can bridge meshes or provide product-level capabilities. Offers an operational decision framework and concrete trade-offs for choosing or combining both technologies.

🔍 Async Failure Recovery: Queue vs Streaming Channel Strategies

Presents a practical model mapping failure-recovery strategies to channel types: queue vs stream, write-side vs read-side, and shared vs single consumer groups. Demonstrates when resending, releasing, ignoring, delayed retries, or moving messages to an error channel are appropriate, and includes RabbitMQ/Kafka code and Ecotone patterns for idempotency and delayed retries so architects can pick safe, reproducible recovery behaviors rather than applying generic retry rules.

🔍 Authenticating the Machines: When AI Becomes the User of Your API

Positions AI agents as a distinct API consumer class and prescribes integration-grade controls: per-agent ephemeral credentials and least-privilege scopes, behavioral fingerprinting and adaptive rate limits to detect and throttle anomalous or chained agent activity, cryptographic model attestation for provenance, and federated OAuth/OIDC flows for user-delegated AI access. Useful checklist and governance notes for architects preparing enterprise APIs for autonomous AI clients.

🔍 Building an AI Agent Traffic Management Platform: APISIX AI Gateway in Practice

Presents an enterprise case study of extending APISIX into an AI gateway that centralizes LLM inference traffic using an access layer for protocol/auth handling, a governance plugin layer for dynamic routing and circuit breaking, and a scheduling layer combining health checks and real-time load data to route between self-hosted and cloud models. Offers actionable architecture and operational patterns for multi-tenant isolation, stability assurance, and intelligent hybrid-cloud model scheduling.

🔍 Building agentic RAG with PostgreSQL and n8n

Presents a practical agentic RAG integration pattern that consolidates vector storage, chat memory, and tool access inside PostgreSQL and uses n8n as the orchestration/agent loop. Includes table schemas, SQL queries, and a reusable n8n template so architects can replace multi-service RAG stacks with a compact, deterministic Postgres-driven solution.

🔍 Event Streaming is Topping Out

Presents a data-backed market diagnosis: Kafka and streaming are commoditizing as cloud providers and diskless S3-backed Kafka architectures drive 5-10x cost reductions. Confluent’s slowing growth, low-margin stream processing, and many small vendors imply near-term consolidation; architects should expect cheaper streaming, vendor bundling, and re-evaluate when Kafka is necessary versus simpler alternatives.

🔍 Event-Driven Architecture Patterns: Real-World Lessons From IoT Development

Provides an actionable IoT-focused event-driven architecture case study: replaces polling with MQTT pub-sub to cut CPU and latency, details MQTT patterns (QoS, retained messages, last will), describes a lightweight Router→Aggregator→Predictor→Executor stream pipeline for edge devices, shows circuit breaker and drift-detection implementations, and documents a pragmatic model compression path (quantization, pruning, distillation) to deploy ML on constrained hardware.

🔍 Getting Started With the Official MCP Registry API

Practical guide to the Official MCP Registry API: shows the OpenAPI-based server schema, example queries (curl), pagination handling, and how to interpret server metadata (transports, package registries, runtime hints). Includes step-by-step publishing with the mcp-publisher CLI. Useful for teams integrating MCP servers into agent frameworks or automation toolchains because it standardizes discovery and deployment workflows.

🔍 How Agoda’s Multi-Product Booking Engine Powers Seamless Travel Bookings

Presents a production-proven architecture for orchestrating multi-product transactions by dynamically composing per-product booking graphs into a merged graph so itinerary-level operations like payment run once. Key contributions: an asynchronous agent pattern, graph merging rules with an automated Risk Profiler to order confirmations and minimize penalty risk, regional pod sizing and observability (OpenTelemetry, Pyroscope) guidance for scaling to complex, 60-node workflows.

🔍 How Secure Is The World’s Most-Used Banking API?

Synthesis of the newly published security analysis of the Open Banking Account and Transaction API and recent industry research; the article translates academic findings into actionable, protocol-specific mitigations: enforce consent and token binding (nonces, one-time transaction tokens), defend against BOLA by binding resource IDs to scoped tokens, treat gateways as part of the security boundary (certificate rotation, endpoint verification), use operational dashboards for anomaly detection, and strengthen governance and certification cycles.

🔍 I spent 8 hours understanding how Parquet actually stores the data

This article walks through Parquet internals with practical detail: the PAX-like row group/column chunk layout, the distinction between logical and physical types, and how pages, dictionary pages, PLAIN encoding and RLE_DICTIONARY interact with compression to influence storage footprint and read performance. It consolidates implementation-level details that help architects choose encoding and chunking strategies to optimize ETL throughput and analytic query latency.

🔍 NATS JetStream vs RabbitMQ: Choosing the Right Message Broker for Your Event-Driven Architecture

Practitioner case study showing why NATS JetStream was chosen over RabbitMQ for an event-driven accounting migration: author implements an annotation-based retry/DLQ system with exponential backoff on JetStream, documents Helm-based deployment, Prometheus metrics, and operational/resource tradeoffs. Useful for architects needing reliable redelivery semantics and low-operational overhead rather than raw throughput.

🔍 OpenAPI won’t make your APIs AI-ready. But Arazzo can.

Shows how combining OpenAPI with the Arazzo workflow spec and exposing those workflows via MCP servers solves a key integration problem for LLM agents: it collapses multi-call sequences into single business actions, reduces token and error waste, and enables automated, standards-driven agent integrations. Includes Arazzo examples and a product plan to generate MCP servers from OpenAPI+Arazzo.

🔍 Stumbling into AI: Part 6—I’ve been thinking about Agents and MCP all wrong

Presents a pragmatic integration-first view of LLM agents: treat the LLM as the orchestrator that invokes tools via MCP rather than the primary processor of input data. The piece explains how MCP standardizes API access, how agents simplify swapping integrations (analogy to Kafka Connect), and outlines operational trade-offs such as non-determinism and validation when deploying Streaming Agents driven by Kafka events.

🔍 The Hidden Trust Problem in API Formats

Presents governance and trust as first-order concerns when choosing API spec formats, backing the claim with committee composition data and historical examples. Recommends vetting open governance, transparent roadmaps, vendor neutrality, documentation quality, and tooling support to avoid long-term vendor-driven risks.

🔍 Using MCP Tools Inside Workflows

Presents a practical pattern for exposing MCP servers as workflow steps: call JSON-RPC POSTs, initialize and reuse the mcp-session-id header, map tool inputSchema fields to step inputs, and handle outputs by either exposing raw text or parsing into structured fields. Emphasizes benefits for orchestration, debugging, security, and predictable operation when adapting agent-oriented MCP tools to enterprise procedural workflows.

Apache Camel

🔍 Making Apache Camel documentation accessible to LLMs

Apache Camel adopted the llms.txt spec and now produces 5,355+ markdown docs automatically during builds, enabling LLMs and coding assistants to discover and fetch component, language, and guide pages. The implementation uses Antora then Hugo with a Gulp HTML-to-markdown step that strips navigation, preserves semantic content and converts code/tables to GFM, delivering automated, production-ready markdown coverage that other integration projects can replicate to improve AI-driven developer workflows.

Apache Kafka

🔍 Apache Kafka: What 10,000+ Forum Posts Reveal

Data-driven synthesis of 10,000+ Confluent forum posts revealing the most common Kafka production failures and their root cause: configuration complexity. The author quantifies trouble spots (Connect, Schema Registry, auth, KRaft migration, monitoring), explains how interdependent configs and poor error diagnostics propagate failures, and calls out solution categories (pre-flight validation, visual configuration management, explainable observability, education, and abstraction layers).

🔍 Demystifying Confluent’s Schema Registry Wire Format

Explains Confluent Schema Registry wire format at the byte level and demonstrates how to debug and manually handle Avro/Protobuf/JSON payloads when clients lack registry integration. The article supplies xxd/dd parsing examples, explains the magic byte and schema id layout, and includes code snippets to produce and consume registry-compatible messages without a live registry, enabling safer interoperability and forensic debugging in Kafka environments.

🔍 How Kafka Stores Billions of Messages: The Storage Architecture Nobody Explains

This article explains Kafka’s segment-based log layout (segment, index, timeindex files), why segmenting avoids giant-file pitfalls, and how indexes let consumers locate offsets without scanning. It connects these internals to retention, compaction, IO patterns, and sizing decisions, giving architects clear, operational rules for scaling Kafka storage to billions of messages.

🔍 How to choose the right diskless Kafka

This piece provides a technical evaluation of the diskless Kafka trend, focusing on AutoMQ’s approach: replacing broker local storage with object storage while retaining Kafka protocol compatibility via a WAL for low-latency durability, dedicated log/block caches for hot data, and leader-based metadata management. It highlights trade-offs between leader-based and leaderless designs and concrete strategies to minimize cross-AZ network and API costs.

🔍 Kafka Backfill Patterns: A Guide to Accessing Historical Data

Provides a two-phase backfill blueprint and three concrete sourcing patterns for historical Kafka data: use Kafka tiered storage to keep logical logs queryable; run an ETL job that writes cleaned, schema-evolved records to a dedicated backfill topic; or have consumers pull directly from cold storage or via Trino. Includes operational guidance on schema evolution, isolation, throttling, and when each pattern is appropriate for bootstrapping, recovery, or feature enrichment.

🔍 Kafka MCP Server: Building a Real-Time Message Processing Integration

Presents a production-oriented MCP server that exposes Kafka cluster capabilities as standardized tools for MCP-compatible clients, enabling AI and automation workflows to discover topics, inspect Avro schemas from Confluent Schema Registry, generate valid payloads, and produce or consume messages with offset control and optional EntraID authentication. Focuses on the how: MCP tool design, serialization, schema analysis, and operational security, offering a reusable pattern for integrating AI-driven agents with enterprise event streams.

🔍 Kafka Patterns: Ordered Async Processing Per User

Presents a Kafka Streams implementation for ordered asynchronous processing per user by creating virtual per-user queues: an inflight-state KeyValueStore tracks the active request and pending request IDs, a pending-requests topic stores full payloads, and Result handling resumes the next request. Includes Processor API code, AVRO schemas, diagrams, a GitHub POC and production-oriented notes, offering a concrete approach for preserving per-key order in long-running flows.

🔍 Kafka is Coming for Your Job Queue:

Evaluates KIP-932 ‘Queues for Kafka’ and its share-group consumption primitive: how cooperative consumption, per-record ACCEPT/RELEASE/REJECT semantics, broker-side locks, and a __share_group_state topic implement native queuing at the broker. Explains the architectural tradeoffs including loss of zero-copy, added broker state and upgrade incompatibilities, gives CLI/client hints and a strict warning: preview only, not production-ready.

🔍 Kafka’s Long Polling Architecture — Simplicity, Efficiency, and Scale

Examines Kafka’s pull plus long-poll consumer architecture, explaining the technical rationale, trade-offs versus push models (ordering, backpressure, resource isolation), and operational implications. The article distills actionable tuning knobs and patterns for enterprise deployments, giving architects concrete guidance to balance latency, throughput, and fault recovery.

AWS

🔍 Beyond AWS API Gateway Throttling: Fixing Hidden Edge Cases and Bursty Traffic Issues

Identifies a Token Bucket failure mode in AWS API Gateway that surfaces under bursty traffic and causes unexpected throttling. The article explains how to detect the condition and presents concrete mitigation patterns—traffic smoothing, external queuing/buffering, and adding a custom throttling layer—so architects can avoid transient request failures while preserving aggregate throughput.

Azure

🔍 Agent Loop Ignite Update - New Set of AI Features Arrive in Public Preview

Microsoft’s Agent Loop update exposes integration-first AI capabilities for Logic Apps: BYOM through APIM AI Gateway (a single control plane for auth, quotas, observability), MCP-based tool discovery and OBO connectors, Consumption SKU agentic workflows, document-level ACL enforcement for secure RAG, Okta identity support, Teams deployment, and a redesigned designer — together they provide a pragmatic, governed pattern to embed model-agnostic agents into enterprise integration architectures.

🔍 Clone a Consumption Logic App to a Standard Workflow

Microsoft introduces a Clone to Standard feature for Azure Logic Apps that converts Consumption workflows into Standard apps, preserving triggers/actions and carrying over workflow design while requiring rebind of connections and secure parameters. It speeds migrations to single-tenant Standard workflows (local development, built-in connectors, private endpoints) but excludes integration account references, XML/flat-file transforms, EDIFACT/X12, nested workflows and Azure Function calls; useful for architects planning bulk migrations and modernization.

🔍 Enabling API Key Authentication for Logic Apps MCP Servers

Microsoft has enabled ApiKey authentication for Logic Apps MCP servers and documents the host.json authentication node, management REST endpoints (listMcpServers and regenerateMcpServerAccessKey), az rest CLI usage, key expiry and keyType payloads, and how to configure Agent Loop clients to use the X-API-KEY header. This provides a concrete interoperability path for integrating external agent frameworks with Logic Apps, plus operational guidance for key retrieval and rotation.

Boomi

🔍 Boomi AI Agents What Are MCP, ACP, and A2A? AI Agent Protocols Explained

Presents MCP, ACP, and A2A as complementary AI agent integration protocols and maps each to enterprise needs: MCP for tool and data access via JSON-RPC, ACP for local-first internal agent coordination using REST, and A2A for secure cross-company workflows with business-grade auth and governance. Provides implementation patterns, security considerations, debugging and deployment guidance, and notes Boomi’s native MCP support to accelerate enterprise adoption.

Debezium

🔍 CQRS Design Pattern

Concrete guide to implement CQRS with Debezium-driven CDC: explains database-native streaming replication and a Debezium+Kafka Connect approach to replicate Postgres writes to heterogeneous read stores. Includes production-minded details such as REPLICA IDENTITY, permission grants, upsert/delete handling, ExtractNewRecordState SMT usage, and example connector configs for a JDBC sink and QuestDB sink plus a Quarkus demo repository.

Kong

🔍 API Gateway vs. AI Gateway: The Definitive Guide to Modern AI Infrastructure

Kong frames AI gateways as a new integration layer optimized for LLM workloads, emphasizing token-level economics, semantic caching, streaming (SSE/WebSocket) support, content-aware security, and intelligent model routing. It provides a layered architecture and migration guidance showing how an AI gateway complements API gateways to reduce cost, improve streaming UX, and centralize governance.

🔍 Resolving the Kong Consumer Conflict Error: Root Cause, Debugging, and Best Practices

Unique contribution: a focused, enterprise-grade operational pattern for resolving Kong Consumer 409 Conflict errors during Kubernetes/GitOps reconciliations. The article explains the exact sync flow between KongConsumer CRDs, KIC, and the Kong Admin API, demonstrates curl/kubectl diagnostics, provides a Bash script for automated cleanup, and prescribes GitOps prune settings and naming conventions to prevent global username collisions and cache/hybrid-mode drift.

MuleSoft

🔍 Mule SDK: Implementing Server Sent Events (SSE) in MuleSoft

Author presents a Mule SDK connector that fills a MuleSoft gap by implementing Server-Sent Events support: an SSE Server Listener registers clients, Send Custom Event streams messages in a loop, and Disconnect terminates sessions. The post includes flow diagrams, usage with Postman, and a GitHub repo, providing a portable integration pattern to enable real-time streaming (including LLM/agent progress) in enterprise MuleSoft projects.

🔍 MuleSoft A2A: Building a Connected Agent Network

Demonstrates a MuleSoft implementation of the A2A Agent-to-Agent protocol using the Mule A2A Connector (0.4.0-BETA) and the Inference Connector to build an orchestrator, agent registry (hosted agent-card.json), and JSON-RPC-based agent communication. The article outlines the A2A Client/Orchestrator/Agent architecture, Object Store-backed registry, LLM-assisted planning and validation, and recommends WebSocket clients and Anypoint Flex Gateway for real-time updates and governance, providing a practical PoC pattern for enterprises evaluating agent meshes.

🔍 MuleSoft: Building a ReAct Agent for Box Storage

Demonstrates a MuleSoft-based ReAct agent pattern: use A2A Task Listener to receive tasks, enumerate Box MCP tools, call an LLM (Inference connector) to produce a plan, execute tool calls, persist observations to Object Store for memory, and invoke the LLM to replan until completion. Includes diagrams and a GitHub repo—practical, reusable pattern for integrating LLM agents into enterprise integration pipelines.

🔍 Preserving Errors in Parallel Processing With MuleSoft Scatter-Gather

Provides concrete DataWeave patterns and an on-error-propagate approach to surface per-route failures from MuleSoft Scatter-Gather. Shows how to extract error.errorMessage.payload.failures, pluck failing route indices, map them to route names, and produce a structured error payload; also explains differences when routes use an until-successful scope so teams can reliably log and return meaningful composite error details.

RabbitMQ

🔍 Migrating Self-Hosted RabbitMQ to Amazon MQ

Hands-on migration playbook for moving a self-hosted RabbitMQ fleet to Amazon MQ without downtime: automate topology discovery with the RabbitMQ management API, mirror configs into Amazon MQ, run old and new brokers in parallel using shovels to forward messages, migrate consumers before producers, split consumer/producer connections for mixed services, and rebuild monitoring on CloudWatch/Grafana. Practical notes on quorum queue trade-offs, instance sizing, and layered alerting make this a usable enterprise blueprint.

🔍 Three Strategies for Retrying Failed Messages in RabbitMQ — From Simple to Complex

Presents three progressively robust RabbitMQ failure-retry strategies with concrete Java examples and complete RabbitMQ configuration: (1) default immediate requeue (dangerous at scale), (2) fixed-delay via DLX and TTL, and (3) application-driven incremental backoff using multiple TTL queues and topic-exchange routing. Highlights limitations (no per-message TTL in stock RabbitMQ, x-death routing constraints), provides throughput math, and offers a practical implementation pattern for enterprise resilience.

🔍 TLS/SSL certificates in RabbitMQ Part 2

Provides step-by-step configuration for RabbitMQ TLS using CA-signed client certificates: shows creating an ssl.SSLContext with root CA and client cert/key, RabbitMQ server settings verify=verify_peer and fail_if_no_peer_cert, enabling the rabbitmq_auth_mechanism_ssl plugin for EXTERNAL auth, using ExternalCredentials in Pika, and shovel/federation URL parameters (cacertfile, certfile, keyfile, auth_mechanism) to secure inter-cluster transfers. Practical CloudAMQP-specific deployment notes included.

Solace

🔍 AI-Assisted Modeling: How to Import Your Event-Driven Assets with Event Portal MCP Server

Solace outlines an AI-assisted workflow that uses the Event Portal MCP Server and an LLM to analyze a codebase, extract schemas/events, and create application domains, events, schemas and producer/consumer relationships in Event Portal. The article provides setup commands, prompt templates, GitHub links and an approval/monitoring flow, delivering a pragmatic, repeatable pattern for automating enterprise EDA discovery and documentation on Solace Event Portal.

🔍 The Anatomy of Agents in Solace Agent Mesh

Solace describes a configuration-first approach to agentic integration, detailing the agent YAML (identity, system prompt, model config), normalized tool model (built-in, custom Python modules, external servers), lifecycle hooks, and A2A-compliant Agent Cards for discovery. The post is valuable for architects designing event-driven agent compositions, showing how configuration-driven, modular agents enable reusable integration patterns and cross-system tool invocation within an event mesh.

WSO2

🔍 The Definitive Guide to WSO2 Micro Integrator: Architecture, Implementation, and Cloud-Native Operations

A deep, product-focused technical walkthrough of WSO2 Micro Integrator that explains its Synapse/Axis2 foundation, contrasts monolithic ESB tradeoffs, and documents reusable integration patterns (sidecar, centralized ESB, API-centric). The article provides concrete cloud-native deployment guidance, Kubernetes operational considerations, and CI/CD recommendations, making it a practical reference for architects evaluating MI as a lightweight, container-friendly integration runtime.

Mergers & Acquisitions

🤝 liblab joins Postman to complete the API lifecycle

Postman acquires liblab to embed an automated SDK-generation engine into its API lifecycle platform, enabling instant generation, testing, and publication of client SDKs across major languages from a single source of truth. This materially changes how enterprises keep docs, tests, and SDKs synchronized and automates distribution to package registries, reducing friction in API consumption.

Releases

🚀 Apache Camel 4.16

Apache Camel 4.16.0 (GA) delivers practical, enterprise-focused updates: it injects CAMEL_TRACE_ID and CAMEL_SPAN_ID into exchange headers to enable end-to-end route tracing and easier log correlation, enhances Camel JBang route exporting and dependency detection for Java routes, updates Spring Boot compatibility and Java readiness, and adds new components including IBM COS and a post-quantum KEM-based camel-pqc for message encryption. The release provides actionable config examples and an upgrade guide to adopt these capabilities in production.

🚀 Kaoto 2.8

Kaoto 2.8 advances the DataMapper maturity with full xs:extension and xs:restriction support, minOccurs/maxOccurs visualization, improved relative XPath (parent and current()), and safer mapping operations; combined with VS Code walkthroughs, contextual canvas menus, and enhanced component configuration (beans/JDBC pickers), this release materially improves authoring and correctness of Camel route data mappings for enterprise integration workflows.

🚀 Kong Insomnia 12

Kong Insomnia 12 introduces a native MCP client to exercise and inspect MCP protocol interactions and authentication for agentic AI servers, integrated AI-driven mock-server generation from natural language/OpenAPI/URL, and AI-assisted commit message generation. These features directly address integration testing and developer workflow gaps for teams building AI-native services by enabling protocol-level debugging,Instant mocks for complex services, and cleaner git hygiene.

🚀 Microcks 1.13

Microcks 1.13.0 is a minor release that materially improves integration testing by adding OpenTelemetry-based Live Traces for real-time request/match debugging and a QuickJs4J JavaScript engine that enables scripting across JVM and native images. These features lower troubleshooting friction for enterprise mock environments and enable stateful, portable dispatch logic while also delivering multiple protocol and import enhancements.

🚀 WSO2 API Manager 4.6

WSO2 API Manager 4.6.0 introduces an MCP gateway to automatically expose REST APIs as MCP tools for AI agents, unified LLM proxy integrations (AWS Bedrock, Azure AI Foundry, Gemini, Anthropic) and advanced AI guardrails, automated federated discovery for AWS/Azure/Kong/Envoy gateways, integrated Moesif analytics and monetization, plus centralized distributed throttling and tenant-sharing for enterprise scale—practical release features that change how organizations govern and monetize APIs in the AI era.