Articles
OpenAPI v3.2.0 (GA minor release) adds several specification-level capabilities that shift API modelling and tooling: multipurpose nested Tags with kind for taxonomy-driven rendering and codegen, a formal query HTTP method and querystring Schema Object for richer idempotent queries, explicit streaming media types with itemSchema for event/stream handling, and OAuth2 Device Authorization plus oauth2MetadataUrl for improved discovery.
🔍 A Year’s Evolution of AI and APIs: A World Apart
Kristen Womack outlines how agentic AI and MCP are enabling models to maintain context, make function calls, and orchestrate APIs; the piece provides practical, integration-focused guidance—audit API metadata and OpenAPI semantics, ensure context/state management, implement robust auth/error handling, and guard against data-quality and over-privilege risks—to prepare enterprise APIs for agent-driven automation.
🔍 API Gateway Evolution: How 6 New Platforms Reinvent Traffic Management for Scalable Microservices
Presents a practical comparative analysis of six modern API gateway platforms and the concrete traffic-management techniques they use: hybrid token buckets synchronized via gossip, reactive rate limiting tied to circuit breakers, AI-augmented anomaly-aware throttling, and latency/locality-aware routing. Includes benchmark figures and config snippets to help architects choose patterns and platforms for 100k+ RPS, resilient auth flows, and graceful deployments.
🔍 API gateway shapes: back then, now, and beyond
The article introduces eight practical API gateway shapes (e.g., agent-assisted, localhost-en-masse, edge/device, universal gateway) that address modern constraints: local-first development, LLM-driven endpoints, multi-cloud/CI deployments, and shift-left security/observability. It focuses on how gateways can offload auth, rate limiting, and traffic transformation earlier in the lifecycle and enable consistent routing and security across ephemeral and distributed service endpoints, providing architects a concrete taxonomy to design flexible gateway deployments.
🔍 Everything You Need to Know About MCP Support and API Enablement
Gravitee presents a practical integration pattern for Model Context Protocol (MCP): its MCP Tool Server auto-generates machine-readable API descriptions from OpenAPI, exposes an MCP entrypoint on a v4 proxy, and enables agent discovery and runtime use (e.g., via SSE) while enforcing auth, rate limits, logging, and observability at the gateway. The piece is useful for architects wanting a deployable path to safely expose REST APIs as tools for AI agents without backend changes.
🔍 How LLMs Are Breaking the API Contract (And Why That Matters)
Examines how LLM hallucinations, prompt injection, and agentic pipelines break API contracts and proposes concrete API-side mitigations: maintain machine-readable OpenAPI specs and canonical naming for reliable retrieval, enforce agent RBAC and rate limits, add friction for sensitive operations, implement zero trust for plugins, and add observability/audit trails so architects can detect and contain errant AI-driven calls.
🔍 MCP API Gateway Explained: Protocols, Caching, and Remote Server Integration
This vendor-authored technical guide explains how an MCP API Gateway intermediates AI clients and MCP servers by translating transports (stdio vs HTTP/SSE), enforcing auth/authorization, routing JSON-RPC methods to appropriate backends, and applying targeted caching (favoring read-heavy resources, avoiding side-effect calls). It provides concrete protocol-level guidance and operational patterns for building resilient, scalable MCP gateway deployments.
🔍 Review of 10 Managed MCP Platforms
Practical vendor roundup of 10 managed MCP platforms that helps integration teams weigh trade-offs across authentication/governance, observability, connector breadth, hosting model and pricing. Useful as an early market map for architects evaluating MCP-based agentic integrations; highlights which vendors favor turnkey governance, deep tool catalogs, or self-hosted control and where custom engineering will be required.
🔍 Streaming Data with REST APIs
Provides actionable patterns for streaming data over REST: server-side chunked responses and JSONL/NDJSON (RFC 7464) for line-delimited JSON, SSE for event streams, and Accept-based content negotiation; includes an Express.js example and discusses documenting streams in OpenAPI v3.2, making it useful for architects needing memory-efficient, real-time REST solutions.
🔍 The New API Economy With LLMs
Presents language-as-API as an integration shift and offers practical patterns: use prompt templates and function-calling/agent patterns to translate intents into deterministic API calls, implement guardrails and a lightweight failure-detection/validation model to catch hallucinations, enforce access controls, and adopt LLMOps for monitoring and iterative prompt/version management.
Timely proposal advocating an official OpenAPI registry to solve API discoverability by offering a trusted catalog; the author analyzes MCP's new registry as a model and recommends concrete implementation options (Apigee Registry with gRPC, Apicurio, Scalar with GitHub Actions) and extension patterns so producers can publish authoritative OpenAPI docs and consumers can search and onboard quickly.
🔍 The Perils of Platform Plumbing (and What to Do About It)
Argues that successful internal developer platforms often become invisible and subject to reactive funding cuts; offers a pragmatic survival playbook: translate technical work into business narratives, instrument outcome metrics (time saved, cost avoided, risk reduced), and align platform initiatives to stakeholder goals. Illustrated by an OpenAPI 3.0 case showing how to turn an engineering upgrade into measurable business impact.
🔍 Why Your Legacy APIs Are a Roadblock for AI Agents
Identifies the concrete technical gaps that make legacy APIs unsuitable for agentic AI - insufficient semantic metadata, fragile auth flows, synchronous brittle endpoints and sparse telemetry - and recommends practical integration patterns (lightweight adapter/wrapper layers, semantic contract augmentation, event-driven or streaming endpoints, and improved observability/auth flows) to make APIs discoverable, idempotent and safely automatable by AI agents.
Apache Kafka
🔍 7 Kafka Levers for 1M msg/s Stability
Provides a production-focused checklist of seven tuning levers to achieve predictable 1M msg/s in Kafka: it translates message-rate targets into network/disk I/O realities, prescribes partitioning and batching strategies, acks/ISR and replica considerations, broker I/O and OS/network tweaks, consumer parallelism patterns, and quota controls, emphasizing trade-offs and offering practical math and config examples to stabilize throughput in enterprise environments.
🔍 A deep dive into Apache Kafka's KRaft protocol
This Red Hat Developer deep dive dissects Kafka's KRaft implementation (Kafka 4.1.0), showing how the controller quorum manages metadata via a single __cluster_metadata partition, the pull-based Fetch/FetchSnapshot flows for followers/observers, Vote and LeaderChange semantics, snapshotting and log truncation, dynamic voter change rules, and metadata.version feature gating. Includes commands, log dumps, and metrics useful for architects designing or operating KRaft-based clusters.
🔍 Best Practices for Validating Apache Kafka® Disaster Recovery and High Availability
A hands-on operational playbook for validating Kafka HA and DR readiness: the article explains the trade-offs of unclean leader election, prescribes replication and producer settings (replication factor, min.insync.replicas, acks=all), details key JMX metrics to monitor leader-election health, emphasizes NTP/time sync, and outlines failure simulation and measurement techniques to validate RPO/RTO in multi-region deployments.
🔍 Diskless 2.0: Unified, Zero-Copy Apache Kafka
Aiven outlines Diskless 2.0: a unified Tiered Diskless design that writes once to object storage (WAL), materializes read-optimized local segments from that WAL, and uses Tiered Storage as the optimiser so Tiered and Diskless share one format. The approach enables zero-copy topic flips, replica-led fetches, bounded metadata, and preserves Iceberg and existing plugins while delivering a practical migration path and concrete cost/latency tradeoffs.
🔍 Getting Started with Iceberg Topics for Apache Kafka®: A Beginner's Guide
Hands-on walkthrough to write Kafka topics directly to Apache Iceberg using the Remote Storage Manager plugin: shows how to build the plugin, run a Docker Compose stack (Kafka, Karapace, MinIO, Iceberg REST catalog, Spark), create Iceberg-enabled topics, produce Avro records, and query Iceberg tables. Practical POC steps and troubleshooting make this a useful guide for architects evaluating unified streaming-to-analytics architectures.
🔍 How to Size Your Kafka Tiered Storage Deployment
Provides a comprehensive, numerical sizing methodology for Kafka with KIP-405 tiered storage: it models throughput, replication, IOPS, disk free-space, S3 PUT/GET costs, and local retention to show how tiered storage flips design tradeoffs (fewer brokers, shift from HDD to gp3 SSDs, and much lower monthly storage costs). Actionable assumptions and worked examples make this a practical guide for enterprise architects planning tiered Kafka deployments.
🔍 Kafka at Scale: Why ACLs Fail and Roles Win in 2025
Provides a concise, operational plan for migrating thousands of Kafka clients to authenticated, role-based access in production. The article explains why static ACLs and baked-in token roles break at scale, and shows how phased migration, enhanced observability, and local permission caching solve latency spikes, permission-denied storms, and revocation requirements, making the approach directly applicable to enterprise Kafka deployments.
Camunda
🔍 Camunda and the Model Context Protocol: A Practical Overview
Camunda demonstrates native Model Context Protocol support and MCP connectors that expose enterprise tools to LLM agents and map them into BPMN ad-hoc subprocesses. The post details the MCP server/client architecture, connector behavior, discovery and permission patterns, and a test-drive example, offering a practical pattern to standardize and securely orchestrate AI agents across enterprise systems.
Google Cloud
🔍 Apigee Logger Shared Flow - Implementation Guide
Implementation guide for an Apigee shared-flow logger that implements dual-destination structured logging to ELK and Spark/Kafka, JS-based sensitive-data masking, asynchronous sends, and FlowCallout integration. Includes KVM configuration, deployment commands, policy/resource structure, and request/response schemas so architects can adopt a production-ready Apigee logging pipeline with masking and error handling.
MuleSoft
🔍 An Expert Analysis of MuleSoft’s New Usage-Based Licensing Model
Provides a technical, practitioner-focused analysis of MuleSoft's new usage-based licensing by explaining the exact billing metrics (Mule Flows, Mule Messages, Data Throughput), the Starter and Advanced package buckets, and the Anypoint usage tooling. Highlights concrete migration steps, cost-projection methods, and the architectural risk that per-flow billing can incentivize consolidation into monoliths unless governance and usage monitoring are enforced.
🔍 Pragmatic Kafka Error Handling with MuleSoft: Retries, DLQs, and Real‑World Constraints
Practical, production-grade pattern for Kafka error handling with MuleSoft: use main/retry/DLQ topics, schedule controlled retry sweeps, and prevent reprocessing by comparing message creation timestamps to an executionTimestamp. The article details Mule flows (single-message consumption, ackMode choices, transactional ALWAYS_JOIN publishes), header enrichment for observability, idempotent producer settings, log filtering, and operational knobs; demo code provided.
🔍 Rethinking API Design for Agentic AI
Presents a practical architectural shift for enterprise APIs to become agent-ready by prioritizing clarity, contextual enrichment, and semantic consistency. Recommends consolidating fine-grained microservice endpoints into goal-oriented APIs, exposing enriched responses (confidence, rationale), implementing a domain data layer (data products/data mesh), and applying a semantic layer such as Model Context Protocol so agents can act without heavy orchestration.
SAP
🔍 Message Filtering with Standard & Custom Headers in SAP Integration Suite
Step‑by‑step guide to make SAP Integration Suite Message Monitor business-friendly by setting standard MPL headers (SAP_ApplicationID, SAP_Sender, SAP_Receiver, SAP_MessageType) and adding custom header properties via a Groovy script. Demonstrates extracting payload values with XPath/Content Modifier, storing identifiers, and configuring Message Monitor filters so architects can quickly search and troubleshoot by business keys rather than only Message/Correlation IDs.
Solace
🔍 Introducing Kafka Wireline Proxy for Solace
Solace launches an open-source Kafka Wireline Proxy that implements the Kafka wire protocol and translates it to Solace SMF, enabling Kafka producers and consumers to operate against a Solace event mesh without deploying Kafka. It supports key-based partitioning, consumer groups, schema registry compatibility, configurable ack modes, TLS/mTLS, and Kubernetes-ready deployment, reducing integration and operational overhead while enabling multi-protocol interoperability.
Mergers & Acquisitions
🤝 Kong Acquires OpenMeter for API Metering and Billing
Kong's acquisition of OpenMeter integrates an open-source metering/billing engine into Kong Konnect to provide token- and call-level usage tracking and usage-based billing for high-frequency API consumption (notably LLMs). This adds built-in cost attribution and monetization plumbing to an enterprise API management platform, addressing scaling, telemetry and billing needs as AI-agent API traffic grows.
Releases
Apache Kafka 4.1.0 (GA) introduces multiple enterprise-facing changes: a new Streams rebalance protocol (moves task assignment/coordinator logic), plugin Monitorable metrics API, support for running multiple connector plugin versions, consumer.close(CloseOptions) to control group leave, OAuth jwt-bearer grant, transactional-ID filtering, and producer deadlock protection. These KIPs change client/Streams/Connect behavior and require following the provided upgrade notes to avoid runtime rebalances or metric/name changes.
Apache Pulsar 4.1 (GA) introduces 19 PIPs and several enterprise-grade changes: client integration with third-party schema registries, a broker cache eviction strategy tuned by expected read counts, compaction performance improvements that skip payload parsing, time-windowed message consumption in the CLI, and blue-green cluster migration support. The release also enforces Java 17 for the Java client and patches multiple CVEs, making it a high-priority operational and upgrade item for enterprise streaming architectures.
Camel K 2.8.0 (GA) delivers practical runtime capabilities for enterprise Kubernetes integrations: explicit Git branch/tag/commit deployment, easy init/sidecar container configuration, service trait ports for exposing arbitrary TCP/UDP services, a JVM agent trait to inject instrumentation jars (example: OpenTelemetry), and pipe-level dependency management. These additions remove common operational friction for GitOps, observability, and native service exposure in production Camel deployments.
Kaoto 2.7.0 (GA) expands the visual integration editor with comprehensive JSON DataMapper support (schema tree view, JSON to/from XML mappings and multi-root handling), updates the embedded Camel catalog to 4.14.0, and improves developer workflow via VS Code pom dependency auto-insertion plus canvas enhancements (drag/reorder across containers, step duplication, full undo/redo). These changes reduce mapping friction and streamline Camel route authoring.
KrakenD EE v2.11 is a GA minor release that embeds a unified ai/llm integration layer for OpenAI, Google Gemini, Anthropic and Mistral to simplify auth and prompt templating, and introduces a Conditional Routing component that enables dynamic request routing and server-side aggregation based on request content or metadata. Additional enterprise updates include automatic AWS SigV4 signing and an OpenAPI import merger, providing practical in-gateway solutions for LLM orchestration and finer-grained backend traffic control.
NATS Server 2.12 (GA) adds several enterprise-grade capabilities: atomic batch publish for all-or-nothing stream writes, server-side delayed message scheduling for job/timer patterns, distributed counters implemented with CRDTs (configurable sourcing/aggregation), a prioritized pull policy for faster cross-region failover, mirror promotion for DR, and offline assets to protect data on downgrades. These features change how teams design correctness, scheduling, and cross-region streaming topologies.
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