Building a production voice agent on Amazon Nova 2.5 Sonic means designing around two hard structural limits before you evaluate anything about voice quality or reasoning accuracy: the bidirectional streaming session caps at eight minutes, and the default account quota allows only twenty concurrent sessions per Region. Neither of these
Migrating to the Responses API for GPT-6.1 isn’t optional if your workload needs reasoning and tool calling together, and it isn’t a rename exercise either. Since GPT-5.4, Chat Completions has only supported tool calling when reasoning effort is set to none, and that restriction carries forward through GPT-6 Astra and
The security control most teams assume they have when running generative AI on AWS, and don’t, is content-level audit logging. CloudTrail logs an InvokeModel call by default: who called it, when, from where, and which model. It never logs the prompt or the response. The feature that actually captures what
The instinct to provision the newest, most powerful GPU instance for an inference workload is usually the wrong instinct, and it’s the most expensive mistake I see teams make on AWS. Using a P5 instance built around H100 GPUs to serve a model that a G6 instance handles comfortably doesn’t
Rolling out GPT-6.1 for agentic coding safely means treating the model like any other change to a production dependency: scope what the agent is allowed to do before you worry about how good its output is, build your own regression evaluation before trusting vendor benchmarks, and stage the rollout because
TiDB vector search earns its keep in one specific scenario: when a query needs to filter on live, frequently-changing relational data and rank by vector similarity in the same transaction. If that’s your actual access pattern, and it is for most multi-tenant RAG and agent memory systems, storing embeddings in
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