Chatbot is the command line
Agent is the bash script
___ is the GUI (macOS/Windows/GTA 6)
You need Xerox PARC all over again and we have one
It's more like the log is the only user/agent accepted consensus. It has to be the grounding base. Although extending it into an agentic system architecture becomes something not necessarily effective in practice.
That said, there are a _lot_ of "logs for agents" papers that I've read (and unfortunately gotten assigned to review) which are basically "we asked claude to hack on a graph DB and generate a paper".
[0] https://onewill.ai/blog/2026/stealing-50-years-of-database-i...
I’ve come to the same conclusion building my own agents. It simply feels ‘wrong’ that most frameworks will happily mutate your context. You have to explicitly go out of your way to store the original events. I’ve now started storing an event log for my own agents, this is used as the source of truth for deriving all subsequent context.
The great thing about this is that I have finer control over drift in long runs, as I can look back through the conversation/tool history and build context suitable for the current state of the agent. It also allows me to run compactions across the entire event history instead of ‘compactions on top of compactions’ which happens on long runs with checkpoints.
It definitely feels like this will be a bigger issue going forward as we have agents running longer and more complex workflows, I’ve started building a product aimed at addressing this issue in a framework agnostic way. [0]
but wouldn't feeding that log for each request/response iteration must get expensive really fast no?
also "We discuss--without claiming to demonstrate--" wtf? someone had a showerthought and slopped this out in 10mins to see what others thought?
All relevant events that affect the context window are stored in an event log. Forking agents and sessions is simply setting a pointer to the sequence number of another event log.
So if you want to check an implementation of this pattern see: https://github.com/smartcomputer-ai/lightspeed
In Lightspeed, we store all of them as events, thus can always reconstruct the exact state of the loop (e.g. state of open tool calls, compaction decisions in-flight, etc). This makes it possible to run the agent in a durable workflow engine easily.