Author Archives: Allen Rodriguez
Google Cloud’s current agent platform naming changed materially in April 2026. Vertex AI became part of Gemini Enterprise Agent Platform, Agent Builder moved into that platform, Agent Engine became Agent Runtime, Agent Builder Sessions became Agent Platform Sessions, and Memory Bank became Agent Platform Memory Bank. Agent Builder in Generative AI Leader should be evaluated […]
For Google Cloud Generative AI Leader candidates, Google’s managed agent hosting service has changed names. What many teams still know as Vertex AI Agent Engine is now documented as Agent Runtime on Gemini Enterprise Agent Platform. The rename matters because architecture diagrams, Terraform modules, SDK examples, console labels, and older runbooks can use different generations […]
When preparing for Google Cloud Generative AI Leader, batch inference is the right inference pattern when an application needs predictions for a large body of data but does not need an interactive response for each record. Google’s 2026 platform naming now presents this capability as Gemini Enterprise Agent Platform batch inference in newer documentation, while […]
For readers studying Google Cloud Generative AI Leader, context caching is useful when many generative AI requests repeatedly send the same large body of tokens. A long system instruction, policy manual, codebase excerpt, reference corpus, or media payload can dominate both latency and input cost even though it changes far less often than the user’s […]
For Google Cloud Generative AI Leader candidates, autoscaling a model endpoint sounds simple: add replicas when demand rises and remove them when demand falls. In production, the hard part is deciding what “demand” actually means for the model, how quickly the service can react, and which constraint will fail first. Google’s current documentation places model […]
When preparing for Google Cloud Generative AI Leader, Gemini Enterprise Agent Platform Experiments is Google’s current experiment-tracking surface for comparing model-development runs, parameters, metrics, artifacts, and pipeline executions. In Google’s 2026 naming transition, the capability appears as Experiments on Agent Platform in current documentation, while many SDK calls, notebooks, and team conventions still use the […]
Amazon AWS AIP-C01: Bedrock Model Import Custom Model Import changes the question from “can we train a model?” to “can we operate the imported artifact safely inside Amazon Bedrock?” Teams arrive with weights produced elsewhere, but production readiness depends on architecture support, packaging, storage, permissions, inference interfaces, evaluation, and rollback. The import job is only […]
Amazon AWS AIP-C01: Bedrock Prompt Routers Intelligent prompt routing in Amazon Bedrock turns model choice into a runtime decision. Instead of binding every request to one fixed foundation model, a prompt router can select between models in the same supported family based on the characteristics of the prompt and expected response quality. That flexibility can […]
Amazon AWS AIP-C01: Bedrock Structured Data Retrieval Structured data retrieval in Amazon Bedrock changes retrieval-augmented generation from “find relevant passages” into “generate and execute a query against governed data.” That is a different risk profile. The system must understand schemas, generate valid SQL, run it against a supported query engine, and convert results into context […]
Amazon AWS AIP-C01: Bedrock Tool Use Streaming Streaming changes tool use from a simple request/response exchange into an event-driven protocol. With Amazon Bedrock ConverseStream, applications can receive model output incrementally, including tool-use blocks that arrive as deltas. The user may see text while the system is still assembling a tool request, so parsers, state machines, […]
Amazon AWS AIP-C01: CI/CD for GenAI Applications Generative-AI delivery pipelines have more moving parts than ordinary application deployments. A release can change code, prompts, model identifiers, inference profiles, retrieval indexes, tool schemas, guardrails, evaluation sets, or data-processing behavior. CI/CD must therefore answer a harder question than “did the build pass?”: did the whole AI system […]
Amazon AWS AIP-C01: Cross-Region Inference on Bedrock Cross-Region inference in Amazon Bedrock lets applications use compute in more than one AWS Region through inference profiles. The feature can improve throughput and resilience, but it also changes where prompts and responses may be processed. That makes it both a capacity feature and a data-governance decision. Cross-Region […]
Amazon GuardDuty Malware Protection adds malware scanning to workloads and data paths that traditional threat findings may identify only indirectly. Current GuardDuty capabilities include malware protection for EC2-related EBS volumes and an S3 protection plan for newly uploaded objects, but the two paths have different triggers, permissions, and operational implications. Malware-protection architecture in Amazon AWS […]
Route 53 Resolver endpoints connect VPC DNS resolution with networks outside the VPC. Inbound endpoints let on-premises or other connected networks send queries into Route 53 Resolver, while outbound endpoints let VPC workloads forward selected DNS namespaces toward DNS servers elsewhere. Hybrid DNS in Amazon AWS SAA-C03 must distinguish inbound resolution from outbound forwarding and […]
Amazon S3 Multi-Region Access Points provide a global access layer over buckets in multiple AWS Regions. In active-active configurations, requests can be routed toward the closest active Region; in active-passive designs, failover controls let operators move new traffic from an active Region to a passive Region. Multi-Region Access Point design in Amazon AWS SAA-C03 solves […]
