## AI agents and tagged weights

**Agentic learners** encode their learnings as **tagged weights**. At Aampe, for example, we start a new customer's agents with 35 timing tags: five three-hour increments (covering waking hours) over seven days of the week. *Each agent updates the weights for each tag based on the way its assigned user responds to messages sent during each window.*

Our agents also maintain tagged weights for channel, value proposition, and other copy choices, but timing actions present a unique challenge: each timing decision must consider *the possibility of not taking any action at all*. Friday night may be a better bet than Wednesday early afternoon, but Friday night might still be a bad bet in and of itself. The more we act on bad bets, the more we annoy users. Push notifications, emails, and other surfaces for user interaction are a gold mine of first-party data. Messaging too much dynamites the mine. No one wants that.

So, *agents need a mechanism for making a go/no-go decision* for every available timing slot. We use a random draw from a beta distribution to do this. The beta distribution takes an alpha and beta parameter, but there's an alternative parameterization that I find more intuitive: it takes a probability and a measure of signal strength.

## The use of probability and signal strength in messaging decisions

Imagine we estimate that a user has a 75% probability of responding on Friday night. If that probability is based on having sent only one message, our probability warrants a lot less confidence than if it's based on 20 messages.

If we take the number of messages as our measure of signal strength, a 0.75 probability based on a signal strength of 1 would correspond to an alpha of 0.75 and a beta of 0.25. Draw 1000 samples from that distribution, and on average they'll approximate 0.75, but the (inter-quartile) range will run from around 0.57 to 0.99. On the other hand, if you have a signal of 20 (alpha=3.75, beta=1.25), those 1000 samples will still average 0.75, but the range will run from around 0.69 to 0.82.

So, when our agents decide whether to message at a particular time, they look up the **probability** (weight) and **signal** of that timing tag in question and use it to draw a sample from the corresponding beta distribution. If the draw is over a certain threshold (default: 0.5), the agent sends the message; otherwise, the agent skips it.

To give you some intuition around how this works, I've plotted out the distribution of weekly messaging frequency that will result from all 35 timing tags having a particular weight and signal strength. *Learned weights** and the **beta distribution** give Aampe agents a straightforward way to individually optimize timing and message frequency at the same time.*