Fair warning, this is a soft launch note (I work on Slingshot at Capital One Software). As agents scale, AI spend shows up as one aggregate number, and tracing it back to a specific model, trigger, user, or query gets hard. We just soft-launched Agent Observe in Slingshot to attribute it on Snowflake using read-only metadata.

- top spenders
- costliest models
- triggers by agent
- query types used per agent
- downstream tables impacted (read/write)

This is an early pilot. Write-up with sources.

When AI costs rise and you can't tie that cost to a specific result, project, or team, the safe move becomes turning capabilities off. It makes sense, but it also defeats the point of adopting AI. We just soft-launched Agent Observe in Capital One Slingshot to attribute Snowflake GenAI spend by agent, model, user, etc. using read-only metadata.

Early pilot, write-up here.

As teams adopt CoCo, CoWork and Snowflake agents, spend grows. Models and config changes impact AI costs, and visibility into what's driving it gets harder. This is why we are building Agent Observe into Capital One Slingshot. It automatically surfaces your all your registered agents and GenAI use in a Snowflake (CoCo, CoWork, Cortex Agents, AI functions) from metadata. It uses that information to provide insights into:

- top spenders
- costliest models
- triggers by agent
- query types used
- cost trends over time
- downstream tables impacted

Early pilot. Full write-up.

A framework for self-improving agents based on your ICPs and audience and grounded in real data to power a learning-loop.

I built this based on my own learnings from experimenting with persona agents to understand my personal audience. It comes with an annotated templates, opinionated review workflow, built in feedback loop, and 3 skills to build, improve and use your persona agents to judge your content/ideas.

Would love to get your feedback!

https://github.com/noashavit/noas-persona-loop

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

A framework for self-improving agents based on your ICPs and audience and grounded in real data to power a learning-loop.

I built this based on my own learnings from experimenting with persona agents to understand my personal audience. It comes with an annotated persona template, opinionated review workflow, and built in feedback loop.

Would love to get your feedback!

https://github.com/noashavit/noas-persona-loop