Articles
🔍 10 Tips for Improving Agentic Experience (AX)
Presents ten actionable integration patterns for Agentic Experience (AX): publish machine-readable contracts, run MCP-compatible registries or lightweight MCP gateways, adopt agent-first identity flows and contextual policy engines, expose operational metadata and replayable traces, and automate agent onboarding. The piece translates emerging agent-discovery and runtime safety requirements into concrete API/platform design changes for enterprise deployments.
🔍 Beyond MCP: Scaling AI Enablement for API Landscapes
Provides a practical five-step model for scaling MCP-enabled API landscapes: improve API descriptions and scoring for agent discoverability, implement dynamic runtime tool discovery with search/ranking, expose workflow-aligned endpoints to encapsulate multi-API interactions, and adopt sandboxed/mocked environments for safe testing and controlled production use; valuable operational guidance for integration architects facing MCP/context limits.
🔍 Building IRL: From a $50k AWS Horror Story to Human-Centered AI Governance
Presents IRL (Intelligent Rate Limiting): a production-ready middleware for agentic AI that replaces opaque 429 responses with contrastive, actionable feedback, weighted fair queuing for equitable quotas, carbon-aware workload shifting, and immutable audit logs. Implements GraphQL/Apollo, Redis token buckets, Docker/Kubernetes and provides benchmarks (50k agents, 12.5k req/s, P95 87ms) and source links for enterprise integration use.
🔍 Jepsen’s test report for NATS 2.12.1
Jepsen demonstrates that NATS JetStream 2.12.1 can lose acknowledged writes and enter persistent split-brain when .blk or snapshot files are corrupted or when the default fsync policy defers disk flushes; tests (LXC/Antithesis + LazyFS) produce reproducible data-loss windows and filed issues, recommending changing fsync to always or documenting the risk.
🔍 MCP Elicitation: Human-in-the-Loop for MCP Servers
This tutorial details MCP's elicitation feature (June 18, 2025) and provides protocol JSON-RPC examples plus a complete TypeScript implementation using Mastra, Bun/Hono, and assistant-ui. It shows how servers send JSON Schema-based elicitation/create requests, how clients respond (accept/decline/cancel), and presents a practical promise-store workaround to maintain session continuity over one-way HTTP streams so agents can pause for human confirmation without losing MCP execution state.
🔍 n8n Event-Sourced Workflows: Exactly-Once Integrations with Outbox/Inboxes
Demonstrates how to implement event-sourced workflows in n8n by persisting intents to an event log and using outbox (for outbound side-effects) and inbox (for inbound deduplication) patterns so low-code automations can avoid double-charges and duplicate notifications; valuable for architects needing pragmatic exactly-once behavior on at-least-once platforms.
🔍 Queue Consumer Limits: The Invisible Bottleneck You’re Not Measuring
Identifies a subtle, enterprise-relevant queue failure mode where producer rate exceeds consumer capacity and common throughput/backlog metrics fail to reveal the problem. The piece models the R <= C and R > C regimes, explains why consumers may not consume at expected rates, and emphasizes monitoring consumer pop rate and processing latency as diagnostic signals to detect and mitigate this invisible bottleneck.
🔍 The Top 8 API Specifications to Know in 2025
Up-to-date 2025 survey of eight API specifications mapping legacy, mainstream, and emerging standards. The piece highlights Arazzo for describing multi-call workflows and the Model Context Protocol for enabling LLMs/agents to discover and invoke tools, framing a shift toward AI-aware API design while providing a practical comparative table for architects evaluating standards.
🔍 Why MCP Shouldn’t Wrap an API One-to-One
This timely piece argues against one-to-one MCP wrappers and prescribes treating MCP as an agent SDK: define intent-level methods, combine related API calls into single workflows, use natural-language-friendly verbs, and enforce tight input/output schemas while limiting exposure. The approach reduces agent confusion, guardrails behavior, simplifies orchestration, and lowers maintenance and security risk compared with exposing raw API surfaces.
Apache Camel
🔍 Apache Camel Message Flow: Endpoints, Channels, and Exchange Patterns
Practical guide to Apache Camel message flow that stresses designing endpoints, message channels, and exchange patterns before writing routes; highlights how endpoint choices (sync/async, push/poll), channel models (point-to-point vs pub/sub), and MEP mismatches cause cascading failures and operational complexity, and provides actionable architectural rules (design endpoints first, make exchange expectations explicit) to improve reliability and observability.
Apache Kafka
🔍 Apache Kafka® Client Updates: KIP-848 (GA), asyncio, OAuth, and More
Reports GA of the KIP-848 consumer group rebalance protocol in librdkafka (opt-in via group.protocol=consumer), announces pre-release asyncio AIOProducer/AIOConsumer for Python to integrate with FastAPI/aiohttp without losing throughput, and documents metadata-based OAUTHBEARER authentication (enabling managed identity flows like Azure IMDS) plus added Node.js consumer metrics for better tuning; practical for architects updating client-side security, scaling, and async patterns.
🔍 Benchmarking Diskless Topics: Part 1
Aiven publishes an open, reproducible OMB benchmark for KIP-1150 Diskless topics (Inkless) demonstrating ~>94% reduction in cross-AZ and disk costs for a 1 GiB/s in, 3 GiB/s out 3-AZ workload while quantifying the latency trade-offs (P50 ~650ms, P99 ~1.5s) and operational impacts. The post details S3 PUT/GET and rotation latencies, coordinator/Postgres WAL traffic, JVM heap sizing for Diskless caches, and provides configs and raw data so architects can reproduce and evaluate the object-storage-first Kafka trade-offs for production.
🔍 Exploring Kafka Cruise Control
Detailed, reproducible deep-dive into Kafka Cruise Control: the author shows how to integrate the CruiseControlMetricsReporter into brokers, generate and consume metrics, trigger a rebalance or enable self-healing, and inspects key internals (metric aggregation windows, sampling, anomaly detector, and goal optimizer). Valuable for integration/ops teams solving enterprise-scale cluster balancing and automation.
Explains how to materialize Kafka streams into Apache Iceberg using the Kafka Connect Iceberg Sink Connector: it details connector-as-engine semantics, control-topic coordinated commits for exactly-once semantics, default commit interval tradeoffs (small-file proliferation vs time-to-freeze), fan-in/fan-out routing, schema inference vs registry-backed schemas, and the operational need for compaction/optimization to manage Iceberg metadata and performance.
🔍 Freezing streaming data into Apache Iceberg™ - Part 2: Using Iceberg Topics
Presents hands-on testing and analysis of an emergent Kafka Tiered Storage extension that writes closed-segment records directly to Apache Iceberg tables (Parquet), requiring a Schema Registry, Iceberg catalog (e.g., Nessie) and object storage. Explains how the plugin transforms Avro to Parquet, auto-creates tables, the consumer replay bug observed, limitations (binary keys, no Iceberg lifecycle management, one-tier-per-cluster, non-real-time closed-segment copy) and operational trade-offs for enterprise integration architects.
🔍 Kafka Retention Is Lying to You (Why 24 Hours Became 7 Days)
Identifies a common operational trap: Kafka deletes whole closed log segments, not individual records, so retention.ms is bounded by when segments roll and are eligible for deletion. The piece explains how segment rolling parameters (segment.ms, segment.bytes, log.roll.ms) and cleanup.policy interact with retention.ms to lengthen apparent retention, and it provides actionable tuning/monitoring guidance to control disk usage.
🔍 Why Cluster Rebalancing Counts More Than You Think in Your Apache Kafka® Costs
Analyzes hidden TCO drivers from Kafka cluster rebalancing by quantifying temporary spikes in CPU, network, and disk I/O, engineering and monitoring overhead, prolonged risk windows, and cloud billing impacts. Explains how complexity scales nonlinearly with broker count and offers operational mitigation guidance to reduce disruption and cost.
Azure
🔍 Microsoft BizTalk Server Product Lifecycle Update
Microsoft declares BizTalk Server 2020 the final release, publishing mainstream support through April 11, 2028 and paid extended support through April 9, 2030, and confirming End of Support April 10, 2030. Host Integration Server will be released separately in 2028. The post provides migration guidance, artifact reuse options, Logic Apps as the successor with hybrid and connector strategies, and links to migration tooling and support resources.
Google Cloud
🔍 Announcing MCP support in Apigee: Turn existing APIs into secure and governed agentic tools
Apigee adds MCP support enabling enterprises to expose existing APIs as managed MCP tools: create an MCP proxy (basepath /mcp, target mcp.apigeex.com) with your OpenAPI spec, and Apigee handles MCP servers, transcoding, policy-based auth/quotas, DLP/Model Armor protections, analytics and API Hub cataloging. This delivers a governed, observable path for agentic tool integrations and works with ADK and other agent frameworks.
MuleSoft
🔍 Realities of AsyncAPI Support in MuleSoft
The article examines the gap between the AsyncAPI specification and its current implementation in MuleSoft, highlighting limited protocol support, weak validation, tooling gaps, and architectural choices that can cause silent data loss and reliability risks. It advises using AsyncAPI mainly for documentation and governance while relying on native listeners and manual validation to achieve production-ready, resilient event-driven integrations.
RabbitMQ
🔍 Missed heartbeat closures in RabbitMQ
Provides packet-level, multi-client experiments demonstrating how long processing times and high prefetch can cause missed AMQP heartbeats and connection closures in RabbitMQ. Shows differences between Pika, amqplib, and Bunny, illustrates ACK-batching as a heartbeat substitute, and recommends tuning heartbeat intervals or using TCP keepalives to prevent duplicate processing.
🔍 The RabbitMQ ack bug that ate 2 days and my sanity
Case study traces RabbitMQ PRECONDITION_FAILED unknown delivery tag errors to channel lifecycle and delivery-tag scoping in NestJS/amqplib environments; author reproduces logs, links GitHub/StackOverflow threads, and proposes practical mitigations: per-channel ack tracking (WeakMap), clear-on-channel/connection close, verify channel open before ack, prefetch=1, and cautions that unit mocks can hide reconnection-induced delivery-tag reuse.
SAP
🔍 How to Implement Idempotent Process Call in SAP Integration Suite
Provides a concrete, product-level implementation of idempotency in SAP Integration Suite by demonstrating the Idempotent Process Call step in an iFlow: extracting a unique message ID, toggling Skip Process Call, using the CamelDuplicateMessage header to route duplicates, returning a 4xx via CamelHttpResponseCode, and explaining the tenant idempotent repository (90-day retention). Practical for architects implementing reliable duplicate protection on SAP BTP Integration Suite.
🔍 How to Validate Incoming Messages with XML Validator in SAP IS
Provides enterprise-focused iFlow patterns for SAP Integration Suite XML validation: explains XML Validator configuration (Prevent Exception on Failure on/off), shows how validation results surface (SAP_XmlValidationResult vs CamelExceptionCaught), how to return HTTP 4xx using CamelHttpResponseCode, and how to tag failures with SAP_MessageProcessingLogCustomStatus for monitoring; includes XSD/example payloads and two operational handling approaches.
Mergers & Acquisitions
Confluent announced an all-cash acquisition by IBM (Dec 8, 2025), aiming to combine Confluent’s Kafka-based streaming platform with IBM’s hybrid-enterprise reach to deliver a unified real-time data foundation for cloud, microservices, and AI. Enterprise architects should reassess streaming deployment, vendor lock-in risk, and integration roadmaps as products and support models may consolidate under IBM.
Releases
🚀 Apache APISIX Ingress Controller 2.0
APISIX Ingress Controller 2.0 delivers enterprise-grade Gateway API coverage (TCPRoute, UDPRoute, GRPCRoute, TLSRoute), vendor extensions for advanced policies (GatewayProxy, BackendTrafficPolicy, PluginConfig), multi-data-plane management for isolation/multi-tenancy, and an etcd-free Standalone API-driven mode enabling in-memory config hot-reloads. These changes enable new deployment architectures and smoother migrations to Gateway API in Kubernetes.
Camel K 2.9.0 is a release that adds operator-driven GitOps overlay automation and a Dry Build phase to separate build and runtime (enabling build-cluster/production-cluster separation and CI/CD flows). It also revamps the KEDA trait with automatic component-to-scaler mapping (initial Kafka scaler), adds cross-namespace resource bindings, Kamelets-as-Maven-dependencies, and JVM CA cert init, delivering operator-level automation and CI/CD-friendly build separation for enterprise Kubernetes integration.
Debezium 3.4.0 delivers substantive integration updates: official Kafka 4.1.1 support with classloading guidance, new SMTs (SwapGeometryCoordinates, GeometryFormatTransformer) for cross-database geometry handling, configurable guardrails to limit connector schema memory usage, OpenLineage initialization fixes, and several connector-level improvements (IBMi incremental snapshots, Oracle LogMiner metrics and drop-transaction signal). Practical upgrade notes and configuration examples are provided.
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