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Page 17 of 60
Insights on AI, machine learning, and technology strategy
A refund agent followed most of the process and skipped the gate that mattered. Google's ADK 2.0 points to a better fix: map each workflow step to code, model, person, or stop before the agent runs unattended.
One developer can wire Claude Code to Vertex AI in an afternoon. The tenth developer turns that same setup into questions about identity, spend, and who gets removed on their last day.
A physician review, a payer callback, a flaky API: real AI workflows pause for hours or days. AWS's Lambda durable functions show why that pause needs a written contract, not a restart button, so completed agent work and real money stay safe while the workflow waits.
Google just made the Interactions API the primary way to reach Gemini models and agents. The useful move isn't rewriting everything. It's mapping which Gemini features are still one-shot calls and which have quietly turned into jobs with state, tools, and a clock running.
Google's preview of BigQuery's AI.AGG function lets one line of SQL summarize millions of rows in plain language, sitting in the same SELECT as a COUNT(*). One column is arithmetic. The other is a capped, batched, occasionally-NULL call to Gemini. Here's the provenance card to fill in before either one goes on a dashboard.
Twenty agents without a central gateway can need up to 190 point-to-point connections, per AWS's own math. Its new serverless A2A gateway is one answer. The access matrix underneath it is the artifact worth stealing.
The UK AI Security Institute found that agent benchmarks with fixed compute caps systematically undersell what frontier models can do. If your evaluation has a stop rule, the stop rule is part of the score.
GitHub just let Copilot CLI run in Actions without a personal access token. That closes one risk and opens a quieter one: a workflow that spends organization AI credits with no owner, no cap, and no reviewer.
Copilot in Excel is moving from formula helper to workflow runner. Microsoft's real answer to 'can we trust it' is a worksheet that travels with the file. Here's the smaller packet that makes that answer hold.
AWS just admitted, in its own release notes, that the facts a model-picking meeting needs are scattered across console pages, documentation, and regional API calls. Its fix is a catalog. Yours still needs an owner.
Anthropic says Claude automates 95% of its internal business analytics queries at 95% accuracy. The accuracy came from a maintained metric layer, not from pointing an agent at a warehouse.
AWS is putting $1 billion behind Forward Deployed Engineering teams that embed with customers to build agentic AI fast. The durable question for buyers is not whether the demo works. It is what evidence, ownership, and operating muscle remain after the outside team goes home.
Dive deeper into the subjects that matter to you
Implementation notes for building AI tools around real business data, handoffs, review queues, and safeguards.
Product notes, service updates, and BaristaLabs news that affect how small teams use AI at work.
AI market news translated into workflow decisions, risk boundaries, and practical next steps for small businesses.
Model concepts explained through thresholds, queues, and error costs that small teams can actually manage.
Plain-language guidance for owners and operators choosing one useful, reviewable AI workflow at a time.
Hands-on guides for approval policies, shadow weeks, agent receipts, and other AI workflow controls.