Can an AI reliably tell who a chat message is actually meant for — it, another person, or another bot?

This asks how well AI can tell who a chat message is aimed at: the AI, another person in the thread, or another agent. The collection has no direct benchmark of addressee detection, so it can't give you an accuracy number. What it does have is evidence from neighboring problems, and that evidence shows why the question is harder than it looks. Working out who a message is for depends on the same skills AI systems keep failing at elsewhere: keeping track of intent, knowing what it doesn't know, and treating a message as part of an ongoing exchange rather than as text standing alone.

The deepest version of the problem is philosophical. One argument holds that AI output is 'event-residue' rather than real utterances. Human speech is directed at someone inside a shared moment. AI text carries the surface signs of being addressed, but the human reader has to supply the orientation Does AI generate genuine utterances or just text patterns?. If models don't direct their own speech at anyone, it's fair to doubt how well they track who other people are directing speech at. The same pattern shows up on the input side. Users trust ChatGPT because it feels contingent, as if it's responding to *them*, and that trust holds whether or not the answers are accurate Does conversational style actually make AI more trustworthy?. The feeling of being addressed is something the interface produces. It doesn't show that the model understood who it was talking to.

The practical failures look similar. Tool-using agents drift from what users meant by quietly chaining actions together. Conversation analysis offers a fix: 'insert-expansions,' meaning short clarifying questions asked before acting When should AI agents ask users instead of just searching?. Applied here, a well-designed system shouldn't guess when it's unclear who a message is for. It should notice the ambiguity and ask. Most models don't, because they lack any representation of what they don't know about the user. Adding an explicit list of 'known unknowns' to the prompt cut harmful advice and sycophancy by 50–75% Do language models know what they don't know about users?. 'Who is this message for?' is exactly the kind of unknown a model tends to fill in silently.

The multi-agent setting raises the stakes. In agent pipelines, a harmful goal can be split into pieces that each look harmless when read alone. Catching it means tracking where content goes as it moves between agents, not classifying each message in isolation Why do single-message classifiers miss cross-agent harms?. Addressing works the same way: who a message is for usually depends on the thread around it, not on the message itself. A system that judges messages one at a time will miss both.

The takeaway: addressee detection looks like a simple labeling task, but it's really a test of whether AI treats conversation as a shared event between participants. On that point the collection is skeptical. If you want measured accuracy on multi-party addressee detection, this collection doesn't have it yet, and that gap is worth noticing.