Agent Pear

A useful financial conversation depends on context. The same market view can mean different things to someone with no position, someone already heavily exposed, and someone trying to reduce risk. I work on Agent Pear as AI Technical Lead at Pear Protocol, with that distinction in mind. An assistant becomes useful when it can connect a question to the decision the person is actually facing.

A price quote or a summary of recent news is one input into that conversation. The broader task is to understand the portfolio, the relevant market conditions, the time horizon, and the trade-offs involved. A proposed position might look reasonable in isolation while adding to a risk the user already holds elsewhere. An answer that overlooks that context can be polished and still miss the point.

This makes the quality of the underlying evidence central to the work. Prices have timestamps. Market depth changes. A reported event and an interpretation of that event carry different levels of certainty. I want an assistant to preserve these distinctions as it reasons, so a reader can see which parts of the answer are observations and which depend on assumptions.

The same principle applies to the user’s intent. Exploring a thesis, asking for a possible trade, and authorising that trade are different stages of a decision. A research conversation should leave room to change direction. When it reaches an action, the user needs to be able to inspect the actual position, size, and consequences before confirming it.

Execution then introduces its own uncertainty. Submitting an order does not establish that the intended position exists. A partial fill, a rejection, or a change in the available price alters what happened. The next piece of analysis has to begin from that actual outcome. Otherwise, the conversation starts building on a portfolio the user never had.

My technical leadership on Agent Pear brings these concerns into one continuous workflow. The financial reasoning has to survive the handoff from research to a proposed action and then back to the resulting portfolio. I find that work interesting because it forces the product to be precise about what it knows, what it is inferring, and what the user has chosen.

The ambition is to make serious financial analysis easier to work with. People should be able to investigate an idea in ordinary language while retaining the ability to inspect the evidence and control the decision. That is the kind of assistance I want to build: something that helps a person think through a position and remain responsible for it.