Anyone that's gotten into a long chat with an AI model has likely noticed things slow down and results get worse the longer a conversation continues. Many chat interfaces will let people know when they've hit this point but background agents make the issue much less likely to happen.
Across all our AI-first companies, whether coding, engineering simulation, or knowledge work, a subset of people stay in one long chat session with AI models and never bother to create a new session when moving on to a new task. But... why does this matter? Long chat sessions mean lots of context which adds up to more tokens for AI models to process. The more tokens, the more time, the more cost, and eventually, the more degraded results get.
At the heart of this issue is a technical constraint called the context window. The context window refers to the amount of text, measured in tokens, that a large language model can consider or "remember" at one time. It functions as the AI's working memory, determining how long of a conversation an AI model can sustain without losing track of earlier details.
Starting a new chat session creates a new context window which helps a lot with this issue. So to encourage new sessions, many AI products will pop up a warning suggesting people to move on to a new chat when things start to bog down. Here's an example from Anthropic's Claude.
Warning messages like this aren't ideal but the alternative is inadvertently raking up costs and getting worse results when models try to makes sense of a long thread with many different topics. While AI systems can implement selective memory that prioritizes keeping the most relevant parts of the conversation, some things will need to get dropped to keep context windows manageable. And yes, bigger context windows can help but only to a point.
Background agents can help. AI products that make use of background agents encourage people to kick off a different agent for each of their discrete tasks. The mental model of "tell an agent to do something and come back to check its work" naturally guides people toward keeping distinct tasks separate and, as a result, does a lot to mitigate the context window issue.
The interface for our agent workspace for teams, Bench, illustrates this model. There's an input field to start new tasks and a list showing tasks that are still running, tasks awaiting review, and tasks that are complete. In this user interface model people are much more likely to kick off a new agent for each new task they need done.
Does this completely eliminate context window issues? Not entirely because agents can still fill a context window with the information they collect and use. People can also always give more and more instructions to an agent. But we've definitely seen that moving to a background agent UI model impacts how people approach working with AI models. People go from staying in one long chat session covering lots of different topics to firing off new agents for each distinct tasks they want to get done. And that helps a lot with context widow issues.