Overhearing AI Agents
I take a closer look at a recent study which attempts to introduce a new paradigm when it comes to AI Agents.
In Short
The paper argues that there is an under explored, necessary shift towards passive, multi-human assistance.
Overhearing LLM AI Agents are designed to listen in on conversations or activities involving one or more humans/users.
The paradigm emphasises multi-human interactions as its core distinction from single-user tools like AI copilots.
The foundational concept targets human-to-human conversations where the AI Agent passively monitors ambient activity among multiple participants without joining the dialogue.
This allows it to infer collective intentions and provide subtle aids, like queuing diagrams or retrieving case histories.
However, the taxonomy flexibly includes single-user contexts under certain dimensions, particularly for text-based inputs (monitoring a solo user’s document writing or code editing, similar to copilots).
In essence, while it can apply to one user, the value proposition is in group settings, where direct AI conversation would disrupt flow.
Single-user cases are surveyed but positioned as extensions, not the primary focus.
Are Overhearing AI Agents Really Different from the Co-Pilot Approach?
The paper positions overhearing AI Agents as a distinct paradigm that extends beyond copilots by listening in on human-to-human conversations (or multi-user activities) to provide unobtrusive, contextual assistance, rather than supporting solo user actions.
While both are passive and non-interruptive, overhearing AI Agents address the co-pilot’s key gap: incorporating conversational input from multiple humans without the AI joining the dialogue.
Overhearing complements conversational (direct) and autonomous (planning) agents by making them ambient.
Core Differences
Copilots overhear a single user’s structured input (for example, code or text drafts) in isolation, using it to predict completions.
Overhearing AI Agents process ambient, multi-modal data from group interactions.
For example, audio of a family dinner or video of a cooking session.
To infer collective intentions and intervene only when helpful.
Copilots are foreground suggesters in a one-person workflow.
Overhearing AI Agents prioritise background actions (for example, silently updating a calendar) or subtle foreground cues (haptic notifications on a smartwatch), ensuring they enhance without disrupting group flow.
The paper’s taxonomy highlights this via dimensions like Always Active initiative for real-time group monitoring vs. copilots’ user-initiated triggers.
Copilots work well as with individual creative tasks ( writing aids), but overhearing targets collaborative scenarios like medical consultations or classroom discussions, where direct AI involvement would intrude.
It introduces unique hurdles, such as privacy in always-listening modes or filtering irrelevant inputs to avoid suggestion fatigue, which copilots sidestep in solo settings.
Overhearing complements conversational (direct) and autonomous (planning) agents by making them ambient — an overhearing AI Agent might autonomously plan a meeting summary post-hoc, vetted later, without chat; for example.
Background on AI Agent Approaches
Based on the survey in the provided paper there are three approaches…
The paper grounds these in related work on conversational systems, proactive agents, autonomous agents, and AI copilots.
Direct Conversation (Conversational Agents)
This approach, often called conversational agents, involves users interacting directly with an LLM-based AI Agent via a chat interface, where the AI Agent relies on its planning capabilities to break down complex queries or tasks into steps.
The user provides explicit instructions, and the AI Agent responds iteratively, using multiple rounds of tool calling (querying APIs, retrieving information, or performing calculations) to pursue the goal.
The AI Agent acts as an active participant in the dialogue, much like a chatbot. It leverages the LLM’s reasoning to plan actions — such as sequencing tool calls to gather data or execute subtasks — while grounding responses in the ongoing conversation.
This is inspired by traditional dialogue systems but enhanced by LLMs’ ability to handle open-ended, multi-turn interactions.
Autonomous Agents
Here, AI Agents generate a detailed sequence of events, actions, or a high-level plan based on a user-defined goal, which the user then reviews, edits, and approves before (or during) execution.
This builds on the planning strengths of LLMs but shifts more autonomy to the agent, reducing the need for constant back-and-forth.
The AI Agent uses tool calling to simulate a world model (anticipating outcomes of actions), producing a step-by-step plan or event sequence.
This is often proactive…the AI Agent observes the environment or user input to infer goals, then outputs a vettable artifact ( scripted workflow).
It’s distinct from pure conversation by emphasising delegation — the user hands off the planning, intervening only for oversight.
Autonomous LLM Agents like those in Anthropic’s Claude Code create long-running processes, such as aggregating sources for a report or editing code across files, which users can vet before finalising.
Proactive extensions initiate planning based on environmental cues, like scheduling from calendar data, with the output queued for user approval.
Co-Pilot Approach
The co-pilot paradigm treats the AI as a supportive assistant that provides passive, contextual suggestions during a user’s solo activity, often in creative or technical domains like writing or coding.
It “overhears” the user’s ongoing work and intervenes subtly with completions or ideas, without demanding direct conversation.
Unlike full delegation, copilots focus on augmentation, they monitor user actions in real-time (keystrokes or edits) and suggest incremental aids, like auto-completions or refactors, based on the current context.
This relies on the LLM’s predictive capabilities but operates in a low-interruption mode, often via inline prompts or side panels.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. Language Models, AI Agents, Agentic Apps, Dev Frameworks & Data-Driven Tools shaping tomorrow.
