Agents: An optimistic painting - Adam Browne
You can check the Wayback Machine and it's the same definition back over 5 years, which makes sense, or the wayback snapshot is calling the current API.
Most of today's "agents" are semi-autonomous apps that orchestrate LLMs, third-party APIs, and SDKs to achieve a narrowly defined goal. They might edit an IDE’s buffer, issue DOM commands in a browser, or drive other software. Given the same architecture, it could extend into the physical world.
There are parallels with agents and with RTS (Real-time strategy) games; one such example is StarCraft, which is one of the most famous:
In StarCraft, the best players in the world have very high APM (actions per minute) and the lowest action latency. With a ranking chart:
APM has the highest correlation with win-rate of any variable at p=0.65. We also see that the action latency of GrandMasters is the lowest of all the other ranks.
We know from StarCraft the more decisively correct actions you perform per minute will make you better. But there's something we must acknowledge about current Agents: they're built on LLMs, which are constantly evolving. This is both good and bad. On one hand, you have to update your system instructions and get used to the new changes. You also need to scrutinize the model itself. The AI labs already do this with their evaluations and testing, but the user (director) needs to evaluate it for themselves.
