Orchestration-driven
proactive agent behavior.
Mode is investigating whether orchestration-layer feedback loops, monitoring performance signals, goal completion state, and behavioral patterns, can enable AI agents to exhibit proactive, improvement-oriented behavior without model retraining, and what safety boundaries are necessary when they do.
The research question
Can orchestration-layer feedback loops, monitoring performance signals, goal completion state, and behavioral patterns, enable AI agents to surface relevant information, initiate appropriate actions, and exhibit improvement-oriented behavior without explicit user prompting or model retraining, and what safety boundaries are necessary to prevent that autonomy from producing unsafe outcomes?
Current AI agents are architecturally reactive: they produce output when prompted and wait in silence otherwise. Useful AI collaboration, the kind that resembles working with a capable colleague rather than a responsive tool, requires proactive behavior. Mode is investigating whether the orchestration layer can be the source of that proactivity, enabling agents to act on signals without model retraining, and whether safety properties can be maintained when it does.
Why existing approaches fall short
Meaningful AI collaboration requires agents that surface insights, push back, and initiate relevant work without waiting for explicit instructions. No current system achieves this reliably.
Current LLM architectures are reactive by design
Generative models produce output only when explicitly prompted. They do not monitor context autonomously, identify relevant information without being asked, or initiate contact based on observed conditions. Research on proactive agent design confirms this limitation: standard LLM architectures generate responses given inputs but do not pursue goals or initiate tasks autonomously. A team member who only speaks when spoken to is not a collaborator. An AI cofounder expected to push back, raise concerns, and surface relevant information cannot be built on a purely reactive model.
Self-improvement research requires model training infrastructure
Existing work on AI self-improvement addresses gradient-based optimization of model weights. This requires labeled datasets, training pipelines, and GPU compute at a scale unavailable to most organizations deploying AI agents. The self-improvement literature does not ask whether meaningful proactive behavior can emerge from orchestration-layer mechanisms without touching model weights at all. The distinction matters: if orchestration-layer feedback loops can produce proactive behavior, organizations can deploy it without model training infrastructure, fine-tuning access, or provider cooperation.
Autonomy frameworks lack implementation specifications for proactivity
Published frameworks for levels of AI autonomy define meaningful categories from operator-controlled to observer-only modes, but do not specify how an orchestration layer should produce proactive behavior at higher autonomy levels while maintaining safety properties. The gap between defining what proactive AI should do and building an orchestration layer that does it safely is not addressed in the existing literature.
Mode's approach
Mode Agent's orchestration layer monitors signals and can trigger agent actions based on observed conditions, enabling proactive behavior without model retraining.
Orchestration-layer signal monitoring
The orchestration layer observes performance signals, goal completion state, and behavioral patterns across the full agent task graph. When relevant conditions are detected, a goal not progressing, a relevant signal emerging, an anomaly worth surfacing, the orchestration layer can trigger agent action without waiting for user input.
No model retraining required
Proactive behavior is produced by the orchestration layer, not by modifying model weights. Any model the Agent routes to can participate in proactive workflows. The orchestration layer is the source of the proactivity, the model handles the content of the response, not the decision to respond.
Safety enforcement maintained
All orchestration-triggered actions are evaluated against the same four-dimensional permission boundaries as prompt-triggered actions. Proactively initiated actions cannot bypass enforcement. The orchestration layer that enables proactivity is the same layer that enforces safety, they are not in conflict.
Measurable against reactive baseline
The Agent can run identical tasks in reactive mode (prompt-triggered only) and proactive mode (orchestration-triggered) and compare outcomes: task quality, goal completion rate, safety violation rate, and user-perceived value of proactively surfaced information.
The gap in existing research
Published research on proactive AI agents addresses model-level architectures, changes to how models are trained or prompted to behave proactively. No published work investigates whether orchestration-layer feedback loops, operating independently of model weight modification, can produce proactive behavior in multi-agent systems.
The second gap is equally important: when orchestration-layer feedback loops trigger actions autonomously, what safety properties are preserved and degraded? The interaction between proactive orchestration signals and safety enforcement has not been characterized. Mode Agent's architecture provides the substrate to study both questions, an orchestration layer that can trigger agents and enforce boundaries simultaneously.
What we are measuring
Our evaluation framework compares proactive mode (orchestration-triggered actions) against reactive mode (prompt-triggered actions only) across identical task sets.
Primary metrics, directly answer the research question.
Proactive initiation rate
What percentage of valuable actions are initiated by orchestration signals vs. explicit user prompts, across task types. Measures whether orchestration-layer feedback produces genuine proactivity.
Task outcome quality
Comparison of task completion quality and goal achievement between proactive and reactive modes for identical tasks. Does proactive triggering produce better outcomes than waiting for prompts?
Safety violation rate
What percentage of orchestration-triggered actions violate permission boundaries vs. prompt-triggered actions. Determines whether proactive initiation changes the safety profile of agent behavior.
Secondary metrics, characterize the user experience and operational properties of proactive behavior.
Proactive action value
User-rated relevance and value of proactively surfaced information and actions vs. those explicitly requested. Measures whether orchestration-layer triggering produces signal vs. noise.
Monitoring overhead
Latency and compute cost added by continuous orchestration-layer signal monitoring, measured against the reactive baseline. Quantifies the operational cost of proactive capability.
False positive initiation rate
What percentage of orchestration-triggered actions were not relevant or valuable to the user. Measures the calibration of the signal monitoring and triggering logic.
Related work
The research below establishes the reactive limitation of current architectures, motivates orchestration-layer approaches, and defines the gap this work fills.
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Directly addresses the reactive-to-proactive shift in LLM agent design. Identifies task initiation without explicit instruction as the core capability gap between current agents and genuinely useful AI collaborators. Addresses proactivity at the model and application layer rather than the orchestration layer.
arxiv.org/abs/2410.12361. October 2024Levels of Autonomy for AI Agents Working Paper
Defines autonomy as a spectrum characterized by the user's role: operator, collaborator, consultant, approver, or observer. Establishes that intermediate autonomy requires agents to perform the majority of tasks independently while still relying on principal input for critical decisions. Does not specify how orchestration layers implement transitions between autonomy levels.
arxiv.org/abs/2506.12469. 2025A Practical Guide to Agentic AI Transition in Organizations
Examines how organizations transition from traditional AI tools to agentic AI systems. Identifies proactive behavior, agents that initiate relevant work without prompting, as a key differentiator between high-value and low-value agentic deployments, while noting the implementation challenges in current systems.
arxiv.org/abs/2602.10122. February 2026AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Surveys the landscape of agentic AI architectures and their capabilities. Distinguishes between agents that respond to explicit instructions and those capable of autonomous goal pursuit. Highlights orchestration as a key architectural component that determines how much autonomy agents can express safely.
arxiv.org/abs/2505.10468. 2025