Articles
🔍 6 Big Risks of Letting AI Make API Requests
Identifies six cross-cutting enterprise risks from letting AI agents directly execute API calls: runaway costs, contract-hallucinations, broken authorization, non-deterministic critical paths, opaque failure attribution, and data leakage. It prescribes a supervised-execution architecture: strict rate/token budgets, capability schemas and validation, OPA/ABAC policy enforcement, deterministic orchestration points, agent decision logs, and tracing to contain cost, secure data boundaries, and restore reliable observability.
🔍 Adopting MCP in the Enterprise: What IT Admins Should Watch For
This article maps MCP (an open protocol for LLM-to-tool integration) into enterprise architecture: it prescribes on-demand tool exposure, centralized MCP gateways with RBAC and audit trails, context stores to avoid fragmentation, caching and parallelized calls for performance, and secret-injection patterns to keep credentials out of model context. Useful for architects planning production-grade, governed AI integrations.
🔍 Architecting the Asynchronous Agent: A Guide to MCP Tasks
Analyzes the MCP Tasks primitive and its wire-level JSON-RPC (tasks/list,get,cancel,result) to enable durable, long-running agent work. Explains Task FSM, request-augmentation pattern, server-side state, TTL and pollInterval semantics, and production guidance on durable stores, backpressure, and agent orchestration to avoid timeouts and token bloat.
🔍 Building an AI Agent Traffic Management Platform: APISIX AI Gateway in Practice
Describes an enterprise deployment of APISIX AI Gateway to unify LLM traffic control, combining health-check–driven on-prem-to-cloud fallback, token-volume rate limiting per consumer and model, and plugin-based request/response filtering. Includes architecture diagrams and operational curl configuration snippets that show how to implement seamless fallback, quota enforcement, and multi-tenant isolation for production-grade AI traffic management.
🔍 Choosing the Right MCP Gateway for Your AI Infrastructure
This deep comparative guide explains how MCP gateways centralize AI-agent integrations to solve the N×M connection problem. It evaluates major open-source and commercial gateways, contrasts architectures (proxy vs orchestration vs registry), highlights performance/security trade-offs, and prescribes criteria (semantic routing, TBAC/OBO, streaming, observability) for choosing a production-ready gateway.
🔍 Dynamic Mocking with JavaScript Dispatchers in Microcks
Microcks 1.13 introduces JavaScript dispatchers that let scripts inspect headers, URI parameters, and payloads to select or transform mock responses. The post includes a Docker Compose quickstart, runnable JS examples (Accept header and path-parameter dispatching), and server-side logging guidance, enabling richer, more realistic contract testing and integration simulations.
🔍 Exploring UCP: Google's Universal Commerce Protocol
Explains Google UCP, a new open protocol for agentic commerce, detailing discovery via /.well-known/ucp, capability negotiation, checkout session lifecycle, payment token handling, and signed order webhooks. Includes concrete JSON schemas and runnable samples demonstrating how platforms, merchants, credentials providers, and PSPs interoperate, giving integration teams practical guidance for implementing multi-party, agent-driven checkout flows.
🔍 From DNS Errors to 404s: Debugging a Redirect Bug in a Go API Gateway
Identifies a non-obvious production bug where Go's http.Client automatically followed redirects inside an API Gateway, causing DNS failures, duplicate backend calls, and hidden routing errors; demonstrates fixing it by setting CheckRedirect to return http.ErrUseLastResponse so gateways forward redirect responses, and highlights trailing-slash mismatches between frameworks (Gin vs Laravel) that can create 404s.
🔍 Harnessing the Power (and Taming the Risks) of Vibe Coding in API Development
Introduces 'vibe coding' as an emergent AI-assisted workflow for API development and details practical risks (technical debt, auth/authorization flaws, missing rate limiting, weak TLS choices) plus mitigation: verify all AI output, embed security-by-design (OAuth, rate limiting, bot defenses, proper TLS), refactor for maintainability, and preserve design rationale to avoid black-box systems.
🔍 How Identity Guides Agentic AI Use of APIs
Frames agentic AI as creating non-deterministic API traffic requiring new identity modalities: adopt machine identity (SPIFFE/SPIRE), move from RBAC to ABAC/PBAC, enforce identity-driven delegation/federation controls, and pair these with zero-trust and governance to constrain and audit agentic flows.
🔍 How to expose APIs to LLMs without breaking security
Presents a pragmatic, enterprise-focused blueprint for exposing APIs to LLM-driven agents: replace static keys with OAuth2 and short-lived scoped tokens, enforce JWT/contextual policies at an API gateway, inject secrets at runtime, and apply zero-trust microsegmentation and runtime monitoring to mitigate prompt injection, credential leakage, and automated exfiltration.
🔍 JSON vs CPU: The War on Branch Prediction
Shows that JSON's data-dependent control flow causes branch predictor saturation, making JSON (de)serialization a major CPU sink in high-throughput REST services; analyzes pipeline misprediction costs on modern x86 cores and advocates branchless, SIMD-based parsing (mechanical sympathy) as an implementation strategy to cut CPU usage and improve API throughput.
🔍 Managing n8n Multi-User OAuth Without the Headaches
Practical guide demonstrating how to offload per-user OAuth to an MCP Gateway: initialize an MCP session, pass tenant_id per webhook, use mcp-session-id to maintain session continuity, and call tools/createDoc and tools/sendMail via JSON-RPC so a single n8n workflow can act on behalf of many authenticated users. This pattern reduces credential sprawl and is applicable to other workflow platforms.
🔍 Map Your API Landscape To Prevent Agentic AI Disaster
Presents a practical approach for preventing agentic-AI-driven incidents by treating API surface mapping as an integration-first security control: inventory APIs, classify capabilities, enforce capability gating and least-privilege access, and instrument observability and rate-limiting so autonomous agents cannot discover and misuse dangerous endpoints. The article ties standard integration controls to the unique threat vectors of autonomous agents and provides architecture-level mitigations.
🔍 MCP Resources: The Overlooked Primitive (and Why That's a Problem)
Presents MCP Resources as an application-controlled primitive that improves token efficiency and security compared to model-controlled Tools; documents resources/list, resources/read, templates, subscriptions, annotations, and an example TypeScript server, and recommends host UX and server adoption patterns (including experimental state-storage via resources).
🔍 MCP Today and Tomorrow: Composability, Context, and the Road Ahead
Examines the Model Context Protocol as the emerging standard for LLM-to-tool integration, detailing client/server primitives, stateful transports, dynamic context techniques, and the MCP gateway pattern for discovery, security, and orchestration; highlights protocol improvements (secure auth flows) and platforms like Peta that provide gateway, vault, and policy layers to simplify production deployments.
🔍 MCP Tool Schema Bloat: The Hidden Token Tax (and How to Fix It)
Examines MCP tool schema token bloat and offers server-, protocol-, and host-layer fixes: concise schemas and JSON $ref deduplication on servers; lazy tool hydration and progressive-disclosure protocols to fetch schemas on demand; and host-side semantic search, category/usage-based loading, and gateways/proxies. Provides empirical token-savings, references to GH proposals and Anthropic/Claude implementations, and instrumentation guidance for prioritizing optimizations.
🔍 MCP vs Traditional API Calls in Production: Promises, Pitfalls, and Proper Use
Provides a practitioner-focused evaluation of MCP (Model Context Protocol) as a tool-invocation layer for LLMs, explaining why MCP should augment (not replace) deterministic API calls. Covers non-determinism, cascading failures, error-recovery, latency/cost tradeoffs, and operational patterns (gateways, structured tool schemas, observability, loop detection) to make MCP safe and practical in production.
🔍 On rebuilding read models, Dead-Letter Queues and Why Letting Go is Sometimes the Answer
Describes a Postgres-compatible approach to avoid silent misses during read-model rebuilds: when inline projections skip due to a rebuilding flag, write a skip record into a dedicated system messages table inside the same transaction; after marking the projection active, drain and archive skip records using transaction-visibility checks, avoiding heavy locking while guaranteeing eventual projection of skipped events.
🔍 OWASP Top Ten 2025: Key Security Risks for APIs and Applications
Summarizes OWASP Top Ten 2025 with an API/integration lens: SSRF folded into Broken Access Control, new Software Supply Chain Failures and Mishandling of Exceptional Conditions categories, and a shift toward root-cause security. Advises embedding secure design, provenance checks, identity-first architectures, and observability into CI/CD to mitigate high-impact vulnerabilities.
🔍 Seamless Payments for AI Agents Are the Next Big Innovation
Presents a composable agent-payments stack: MCP for budget/context, AP2 for structured payment intents and receipts, and x402 to embed payment flows into HTTP (402 responses) enabling cryptographically verifiable, provider-agnostic, per-request settlements. Useful for integration architects designing programmatic payment rails, micropayments, and auditable agent-to-API commerce.
🔍 Securing MCP Gateways: Risks, Vulnerabilities, and Best Practices
Analyzes MCP gateways as a centralized integration layer for AI tooling and details specific, enterprise-grade mitigations: treat model context and injected credentials as sensitive, use vaulted short-lived tokens and per-tool scoping, enforce input/output sanitization and prompt firewalls, implement strict auth (mutual TLS/OAuth/HMAC) and RBAC, lock down filesystem/network egress, and add unified, tamper-resistant audit logs to detect exfiltration or misuse.
🔍 The AI Evolution of Graph Search at Netflix
Describes a production LLM-to-GraphSearch pipeline: field-level and controlled-vocabulary RAG narrow LLM context, instructions produce Graph Search Filter DSL, AST parsing plus metadata validation catch hallucinations, and AST-to-UI mapping/@mentions resolve ambiguity. This is a practical pattern for integrating LLMs with federated GraphQL search.
Introduces "Tool Bloat" as a measurable integration problem in MCP-based agents (context rot, latency, cost) and prescribes concrete architecture patterns: simplify tool schemas, unbundle into focused toolsets, use progressive/dynamic discovery or a search-tool to load schemas on demand, employ Skills for unpackable context, and delegate to specialist sub-agents via A2A to keep active context lean and reliable.
Demonstrates a reusable integration pattern: expose specialized LLM agents as MCP tools and compose them via JSON-RPC/SSE so callers interact with a single, controlled interface. The post includes runnable Python code for a three-service setup (raw directory, info agent, HR agent), shows transport and failure-handling choices, enforces data filtering/formatting at the agent boundary, and provides integration tests, offering practical guidance for engineers building agent-based microservices.
AWS
🔍 AWS Serverless Payload Limits Expand to 1 MB: What It Means for Event-Driven Architectures
AWS raised payload limits to 1 MB for SQS, EventBridge, and asynchronous Lambda; the article explains how this reduces event fragmentation and S3 claim-check usage but increases per-event billable units and Lambda memory/processing costs. It provides concrete billing math (64 KB metering and async 256 KB base chunk rules) and recommends when to keep S3 references versus passing richer events directly.
Apache Camel
🔍 Routing Isn’t Just Plumbing: Decision-Making in Apache Camel
Presents practical, architecture‑level guidance for modeling decision logic in Apache Camel routes: keep decisions in routes not processors, apply EIP patterns (splitter, aggregator, recipient list, routing slip) deliberately, and treat retries and DLQs as explicit routing outcomes. Emphasizes observability at decision points and offers operational anti patterns and fixes to reduce hidden technical debt.
🔍 Simple LLM Integration with Camel OpenAI Component
Apache Camel 4.17's camel-openai component provides native integration with OpenAI-compatible chat-completion APIs; the post demonstrates enforcing structured LLM output via JSON Schema inside Camel routes, streaming and conversation memory support, and a practical PII-redaction route (YAML/Java examples and config). It presents an actionable integration pattern for architects to reliably incorporate LLMs into enterprise flows.
Apache Kafka
🔍 How a Tier‑1 Bank Tuned Apache Kafka® for Ultra‑Low‑Latency Trading
Confluent details how a Tier‑1 bank engineered sub-5ms p99 end-to-end Kafka latency for trading by enforcing a "lowest baseline first" methodology: single-partition with four replicas across two DCs to preserve order, exhaustive JMX+infrastructure instrumentation to diagnose tail events, reproducible OpenMessaging Benchmark tests, and targeted broker/producer/consumer configuration tuning to sustain 1.6M msgs/sec with <5KB messages. The key takeaway is that tail-focused instrumentation plus architecture choices enable order-preserving, ultra-low-latency Kafka at scale.
Provides an in-depth, production-focused examination of Kafka KRaft broker internals: how brokers bootstrap from controllers, format meta.properties, replicate __cluster_metadata, apply snapshots, and transition through Raft states. Includes concrete CLI commands, startup logs, and protocol-level Fetch/FetchSnapshot examples that clarify recovery and registration behaviors critical for migrating and operating enterprise Kafka clusters.
🔍 KRaft Kafka Storage Internals: A Look at Storage and Metadata Directories
Presents a hands-on deep dive into KRaft storage internals: disk formatting and meta.properties, the __cluster_metadata partition layout, checkpoint and snapshot lifecycle, quorum-state and leader/no-op behaviors, plus kafka-dump-log outputs showing REGISTER_CONTROLLER/BROKER/TOPIC records. Practical for architects planning KRaft migration and metadata management.
🔍 Priority Lanes in Kafka Streams: Preventing Head-of-Line Blocking
Presents a practical Kafka Streams pattern: branch priority messages into a fast-lane topic after setting their event timestamp earlier, letting Streams' partition-selection prefer those partitions and reduce SLA-sensitive latency. The post covers topology, partitioning/keys, monitoring, trade-offs, and links to a full GitHub PoC; useful when per-key ordering within priority levels is acceptable and measured benchmarks are not required.
Demonstrates using OpenTelemetry+Grafana traces to detect Kafka phenomena such as data loss, duplication from restarts or consumer rebalances, dead-letter routing, and stream aggregation. Provides reproducible test cases, exact span and resource attributes to match (topic, partition, offset, consumer.group), and a method to distinguish poison-pill crashes from rebalances by comparing span resource attributes and logs; also discusses querying and storage trade-offs for production tracing.
🔍 When (Not) to Use Queues for Kafka?
Explains Apache Kafka 4.2's Queues for Kafka (QfK) and the new Share Groups that provide point-to-point delivery within Kafka. Covers operational use cases (task queues, worker pools, dynamic scaling), current limitations (no strict ordering, no exactly-once transactions, no legacy protocol support), and platform consolidation implications for enterprise integration.
Azure
🔍 API Management: Preparing for the Retirement of Trusted Service Connectivity (March 2026)
Microsoft will retire Trusted Service Connectivity for Azure API Management on March 15, 2026; this article details how to identify APIM dependencies on trusted service access, recommends migration to Private Endpoints, IP filtering, or the Network Security Perimeter, and supplies the exact REST PATCH and customProperties key (Microsoft.WindowsAzure.ApiManagement.Gateway.ManagedIdentity.DisableOverPrivilegedAccess = True) to disable the old behavior.
🔍 Inside Logic Apps Standard: How Azure Tables Store Your Workflows
Provides an engineer-facing breakdown of how Logic Apps Standard persists workflows in Azure Table Storage: MurmurHash-based LA and WF identifiers, separation of base (app-level) and flow (workflow-level) tables, date-stamped action tables for partitioned scaling, and key schema fields (runs, actions, variables) with implications for CU-based scaling and lifecycle isolation.
🔍 Inside Logic Apps Standard: Understanding Compute Units (CU) for Storage Scaling
Details how Logic Apps Standard horizontally scales storage using Compute Units (CU00–CU31), mapping each CU to its own storage account and routing runs via RunId-CU suffix; covers host.json Runtime.ScaleUnitsCount configuration, which tables remain in CU00 vs per-CU action tables, and practical implications for avoiding storage throttling under high-throughput workloads.
🔍 Introducing Unit Test Agent Profiles for Logic Apps & Data Maps
Introduces Copilot custom agent profiles that discover Logic Apps workflows and Data Maps, generate Speckit-style specs, produce typed mocks and schema-aligned test data, and scaffold MSTest (net8.0) suites using the Automated Test SDK; enables spec-first, repeatable unit testing and project-wide batch operations for enterprise Logic Apps integration projects.
Google Cloud
🔍 Deploying and Securing MCP Servers with Google Cloud Run and Apigee
Presents a practical, enterprise-grade pattern for hosting MCP servers: front Cloud Run-deployed MCP containers with Apigee to enforce authentication (API keys/OAuth/JWT), map API Products to allowed_tools for tool-level access control, filter tools/list responses, and centralize secrets in Secret Manager. The article includes an end-to-end flow and a Shopify demo showing tiered API Products, quotas, and how Apigee’s service account authenticates to Cloud Run to prevent bypass, making MCP adoption scalable, discoverable, and governed.
🔍 Testing Google Pub/Sub Locally? Microcks Now Supports the Emulator!
Microcks adds support for the Google Pub/Sub emulator by detecting PUBSUB_EMULATOR_HOST in the Async Minion, enabling local publishing of mock messages without changing AsyncAPI contracts. The blog provides a reproducible Docker Compose stack, AsyncAPI binding examples, log evidence, and a Python subscriber script, making it a practical pattern for teams wanting fast, offline event-driven testing with Microcks.
MuleSoft
🔍 Automate Hybrid Deployment of Mule Applications Using Declarative CI/CD Tools
MuleSoft introduces Anypoint Controller, a Kubernetes Operator exposing Mule applications as a CRD to enable declarative, GitOps workflows; the guide shows installing the controller via Helm, creating secrets, and using Terraform (kubernetes_manifest) to manage MuleApplication CRs so teams can use ArgoCD/FluxCD or Terraform for traceable, automated deployments and drift reconciliation.
🔍 How to Catalog Agents Automatically With Agent Scanners
MuleSoft demonstrates Agent Scanners, an Anypoint feature that automatically discovers AI agents across cloud providers and imports them into an Agent Registry for centralized governance, observability, and agent-to-agent orchestration. The post explains enterprise benefits (policy enforcement via Flex Gateway, routing with Agent Broker) and provides a practical AWS Bedrock scanner setup with permissions and UI steps, making it immediately actionable for integration teams.
🔍 MuleSoft Vibes: MUnit Tests is Generally Available
MuleSoft Vibes: MUnit Tests introduces generative AI that creates complete MUnit test suites and metadata-driven mock/event payloads to maximize Mule flow coverage and standardize team testing. As a GA release aimed at enterprise teams, it reduces manual test creation, keeps tests aligned as flows evolve, and integrates testing into the agentic development workflow, improving velocity and confidence for regulated production environments.
🔍 Trusted Agent Identity: Delegated Access Control for MuleSoft Agent Fabric
Presents a production pattern for propagating user identity across distributed agent networks by centralizing OAuth2/OIDC token exchange and on-behalf-of flows at Flex Gateway. The article details gateway policies for credential injection and in-task authorization, explains U2S/OBO/S2S flows with a fintech example, and shows how this enforces zero-trust, auditability, and risk-adaptive authentication across agents.
🔍 Using External Secret Managers With Runtime Fabric
Demonstrates how Anypoint Controller (RTF 3.0.1) enables external secret management for hybrid Mule apps: includes Helm flags to enable the controller, MuleApplication CRD examples that mount Kubernetes Secrets or use the secrets-store CSI driver (secretProviderClass) to surface Vault/AWS Secrets Manager values into pods. Provides stepwise, engineer-focused steps to centralize secrets and support runtime rotation in enterprise RTF deployments.
SAP
🔍 Graph on Integration Suite: Simplifying Multi-API Data Retrieval with Unified Business Data Graph
SAP Graph (BDGs) in Integration Suite exposes a unified business data graph and GraphQL/OData V4 endpoints that let you combine multiple OData APIs via mirrored entities. The post gives step-by-step setup (entitlements, roles, destinations, service keys), shows creating BDGs and testing with Graph Navigator/Postman, and demonstrates replacing multi-call enrichment (SalesOrder + BusinessPartner) with a single query.
🔍 Using Migration Assessment to Plan Your SAP Process Orchestration to SAP Integration Suite Migration
Presents a practical migration-assessment methodology for moving from SAP Process Orchestration to SAP Integration Suite: how to inventory and classify PO artifacts, score migration risk/priority, and map capabilities to Integration Suite equivalents; includes SAP-specific tooling and planning checkpoints to produce an enterprise migration roadmap.
TIBCO
🔍 Standardizing Decisions: The Convergence of DMN and TIBCO BusinessEvents 6.4
TIBCO BusinessEvents 6.4 (LTS) adds native DMN execution with a DMN importer/exporter that maps DMN inputs/outputs to BusinessEvents Concepts/Events, and uses Virtual Rule Functions to invoke decision tables from CEP rules; combined with GraalVM and Java 25 support, the release operationalizes standards-based decisioning inside a high-throughput event processing engine, enabling interoperability and separation of business logic from event-processing code.
Releases
Apache Camel 4.17 delivers enterprise-focused integration of LLMs and observability: Spring AI + OpenAI components (chat, embeddings, vector store and function-calling) and new vector connectors (Chroma, AWS S3 vectors) enable RAG and agentic flows inside Camel routes; camel-opentelemetry-metrics exposes route metrics; JBang remote debugging and live route loading plus JUnit6 modules improve developer productivity and upgradeability.
AsyncAPI 3.1.0 extends the spec with a ROS 2 protocol binding and updates core tooling (spec JSON Schema v6.11.1, parser, converter, and Studio) so teams can validate, parse, convert, and author 3.1.0 documents; this minor release widens AsyncAPI’s interoperability surface to robotics middleware and provides immediate, production-ready tooling support.
Mosquitto 2.1.0 delivers several enterprise-relevant advances: broker-created MQTT v5 topic aliases, improved bridge v5 options, a built-in websockets stack (removing libwebsockets dependency), PROXY protocol support, TLS keylog for packet decryption in Wireshark, expanded plugin APIs/events, and an idle-performance rewrite that can cut wakeups by ~100x. Together these changes boost scalability, observability, and extensibility for high-scale MQTT deployments.
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