AWS rebuilt OpenSearch Serverless for spiky agent workloads

What changed: AWS announced the next generation of Amazon OpenSearch Serverless, describing it as a search and vector engine designed for AI agents, with scale-to-zero, resource creation in seconds, and autoscaling up to 20 times faster than the previous generation. AWS says it also integrates with Vercel, Kiro, Claude Code, Cursor, and Codex workflows so developers can provision search backends from the tools they already use.

Why it matters: Many useful agents need to search company documents, product catalogs, logs, or customer records before acting. For founders and builders, this lowers the infrastructure work needed to add reliable retrieval to an agent without paying for peak capacity all day.

Try/watch: If your agent currently calls a database or vector store directly, benchmark OpenSearch Serverless on one retrieval-heavy workflow and compare cost, latency, and setup effort.

Workday and Google Cloud put HR and finance agents inside Gemini Enterprise

What changed: Workday and Google Cloud expanded their partnership so Workday’s Sana Self-Service Agent is available in Gemini Enterprise, with Gemini becoming the default AI model for Sana for Workday. The integration is aimed at HR and finance tasks such as checking time-off balances, updating personal information, viewing payslips, approving timesheets, reviewing goals, and getting expense-policy guidance.

Why it matters: This is a practical example of agents moving into everyday business software rather than sitting in a separate chatbot. Operators should expect buyers to ask whether agents can respect existing permissions, approvals, and employee data rules before they are allowed to touch HR or finance workflows.

Try/watch: Map your top five employee self-service requests and decide which ones are safe for an agent to answer, which need manager approval, and which should stay human-only.

Salesforce’s Informatica push focuses on the data agents are allowed to use

What changed: Salesforce said Informatica now exposes data-management capabilities as reusable services that AI agents can call, including governed access to data quality, metadata, integration, and master-data management functions. It also introduced agents for data quality, metadata enrichment, and data stewardship, with some features generally available and others planned for later rollout.

Why it matters: Agents are only useful when they act on clean, current, permitted data. For consultants and small-business operators, the takeaway is simple: before buying a flashy agent, check whether your customer, finance, inventory, or employee records are clean enough for automation.

Try/watch: Pick one agent use case and list the data it would need, who owns that data, and what happens if the data is duplicated, stale, or restricted.

CoreWeave packages training, monitoring, and improvement for production agents

What changed: CoreWeave launched unified agentic AI capabilities that connect reinforcement learning, production inference, agent monitoring through Weights & Biases Weave, and autonomous improvement tools. The company says the goal is to help teams use real production behavior to find failures, evaluate fixes, and improve agents after launch.

Why it matters: The hard part of agents is not the demo; it is keeping them reliable once customers, employees, and edge cases hit them. Buyers should ask vendors how they observe agent failures, prevent regressions, and improve behavior over time.

Try/watch: Before deploying an agent to customers, define three failure signals you will monitor: wrong answer, wrong action, and failure to ask for human help.