Always Future Agents

I’ve been interested in software agents since I came across Graham Glass’ software ‘ObjectSpace Voyager’ in 1998. The idea behind agents is software that can act on its own on behalf of it’s “owner”, much like a human agent in the sports or entertainment field.

If you’ve ever used something like Spotlight, you’ve used a local agent. Spotlight works away in the background indexing the files on your computer so that it can answer questions like “Where did I put that presentation where I mentioned ‘Bitcoin futures’?”

There are quite a few “local agents” that are useful. But what if it’s someone else’s presentation that you are looking for? What if it’s on their laptop? To be truly useful to their owners, agents must be capable of being distributed.

In 1998 Object-Oriented was all the rage, but distributed software was still a mess. If there was a lot of money riding on it, you could use CORBA. I was significantly techy back then, and even I had trouble with CORBA. Java had Remote Procedure Calls (RPC) calls by which objects could message other objects, but the whole edifice of distributed computing was fragile. There was no platform on which you could write distributed communicating agents.

Then along came Voyager. At a stroke, Voyager let you turn a Java object into an agent that could communicate with other agents wherever they were. More amazing still was that an agent running on Voyager on your machine could “hop” to another device and execute there. It took my breath away.

Sadly, it was also useless. Almost nobody else had heard of Voyager or seemed to see its potential. There was nowhere for your agents to go and nothing much for them to do if they got there. I could never see how to make real use of it. I think this reality started to bite because Voyager pivoted and became a good, boring web application server.

But for a brief moment, I saw a beautiful future of agents communicating with each other to help their users solve problems (yes, Tron made a big impression on me as a child)!

Though it faded over the years, I’ve never entirely lost that vision. It sits as an, as yet, unexplored part of Mentat. In Mentat, scripts are a first-class citizen, and I want to make it easy to create agent scripts that perform functions on my behalf. The distribution will be achieved using TupleSpaces (an overlooked concept in distributed computing).

A simple but powerful use-case could be finding answers to questions. Imagine something like this:

  • You pose a question and post it.
  • One of your agents sends the question metadata to one or more shared tuple spaces.
  • My agents are waiting for tuples matching things I am interested in.
  • One of my agents spots your question and, realising it (a) matches my interests and (b) meets my priority requirements, ‘takes’ it.
  • It presents your question to me along with the related resources that I have on hand.
  • I select from among those resources to compose my answer.
  • My agent posts my answer back into the tuple space.
  • Your agent spots an answer and collects it to present it, and potentially others, to you at an appropriate moment.

Sounds a bit like posting a question to a web forum, right? Yes, but the differences have the potential to be transformative.

  • You don’t have to decide where to put your question; your agent can do that. Depending on your preferences, it might put it in many spaces and with different metadata depending on the space.
  • I don’t have to look for your question; my agent decides if it’s something I will want to respond to. Or ignore. Or maybe just file it away for some other purpose.
  • My agent can assemble resources on my behalf to make it easier to answer that question.
  • You don’t have to look for replies; your agent will assemble them. Potentially using a quality filter (oh, Matt replied, you’ll want/not want to see that) and potentially digesting answers. Your agent might just as well say, “You’re busy right now, but I suspect you’ll want to see Matt’s question; I will present it at another time”.

Since our agents are software under our control, we can determine how they work and improve them over time to better use our knowledge and attention.

For example, your agent might not be tasked with trying to answer my questions but simply to reflect, “Matt seems to be interested in topic X right now”. Indeed your agent might notify you not about my questions but with analysis of my questions. This could go in all kinds of directions.

I have many of the pieces of this infrastructure in place but can’t make progress right now. I really wish I could find someone to collaborate with on this platform. Hopefully, I still have a few years to get back to it and turn it into something real that people can use to solve problems.

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