AWS launched Dogwood, an open-source policy language that governs sequences of AI agent tool calls rather than evaluating each action independently, extending its Cedar authorization framework to consider historical context and ordering. The language uses temporal conditions to examine prior tool requests and responses, allowing policies like permitting stock sales only if approval occurred within the past hour or preventing transfers exceeding rate limits across concurrent requests. Developers can now express constraints on prerequisites, rate limits, and action sequences, though the reference implementation requires teams to manage trusted timestamps, event authentication, durable storage, and data retention for production use.
AWS redesigned its Kiro coding agent to use the Agent Client Protocol (ACP) instead of proprietary harnesses, allowing developers to choose coding tools and AI agents independently. The architecture consolidates three separate language-specific harnesses (TypeScript, Rust, Python) into a single process communicating through ACP, while AWS extended the protocol with over 50 Kiro-specific methods and a Cedar-based permission model. This standardization mirrors how Language Server Protocol separated editors from language tools, potentially enabling developers to swap agents without changing editors or terminals.
PDI Technologies built PDI Brew, a system where non-technical employees describe internal tools in plain English and receive fully deployed, multi-tenant web applications within seconds using Amazon Bedrock and AWS Lambda. The platform uses two agents—a planning agent that captures user intent as a JSON manifest, and a provisioning agent running on Lambda that orchestrates AWS resource creation deterministically without hallucination. Apps automatically inherit enterprise security (SSO, scoped IAM, HTTPS) and optional governed AI capabilities (chat, summarize, classify) accessible only through a controlled platform gateway with guardrails and audit trails.
Amazon SageMaker Python SDK v3 now integrates generative AI inference recommendations directly into notebooks, allowing users to benchmark endpoints, generate deployment recommendations ranked by cost-performance tradeoff, and deploy optimized configurations without leaving their workflow. The new functionality in version 3.17.0 exposes operations like ModelBuilder.from_jumpstart_config(), start_benchmark(), generate_deployment_recommendations(), and deploy() to automate what previously required manual trial-and-error across instance types and framework settings. Users can now benchmark live endpoints, compare configurations like LMI vs vLLM, and iterate on deployment settings programmatically instead of manually testing multiple combinations.
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