Without major advances, conversational AI risks falling short of human-level intelligence

The research identifies three primary dimensions as foundational to future conversational agents: Reasoning, Monitor, and Control. Reasoning focuses on an agent’s ability to engage in logical, systematic thinking, allowing for structured planning, complex decision-making, and coherent problem-solving across multi-turn conversations. It encompasses both general reasoning, like Chain-of-Thought prompting and Self-Refinement, and agentic reasoning, which merges thought with actions such as tool use and real-world interaction.

Conversational AI agents are stepping beyond traditional chatbots into more sophisticated realms of multi-turn reasoning, real-time tool usage, and self-awareness. With these advancements come new challenges and possibilities, requiring a comprehensive reassessment of what capabilities next-generation agents must embody to move closer to human-level intelligence.

A recent survey paper, titled "A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions," published on arXiv (April 2025), offers a structured and critical roadmap for developing future conversational AI systems that are scalable, adaptive, and proactive.

What are the essential capabilities defining next-generation conversational agents?

The research identifies three primary dimensions as foundational to future conversational agents: Reasoning, Monitor, and Control. Reasoning focuses on an agent’s ability to engage in logical, systematic thinking, allowing for structured planning, complex decision-making, and coherent problem-solving across multi-turn conversations. It encompasses both general reasoning, like Chain-of-Thought prompting and Self-Refinement, and agentic reasoning, which merges thought with actions such as tool use and real-world interaction.

Monitoring highlights self-awareness and interaction tracking. Self-awareness enables agents to recognize their limitations, refine decisions, and correct mistakes dynamically, while interaction monitoring ensures that agents continuously track user goals, preferences, emotions, and contextual changes over conversations. It creates truly adaptive, personalized experiences instead of static interactions.

Control emphasizes the use of external tools and adherence to predefined user policies. Agents must not only select and execute appropriate tools (like APIs or databases) but also rigorously follow operational policies and user constraints over complex task sequences. Together, these capabilities push agents toward autonomy, resilience, and ethical responsibility in real-world applications.

What major challenges are hindering conversational agents today?

While the capabilities framework sets a clear goalpost, the study underscores serious challenges still obstructing progress. First, long-term multi-turn reasoning and context retention remain fragile. Even the best models today often lose track of user objectives over prolonged interactions, leading to inconsistencies, policy violations, or irrelevant actions.

Second, self-evolution capabilities are largely aspirational. Though some early work uses reinforcement learning to fine-tune agent behaviors during deployment, achieving reliable online self-correction without catastrophic drift remains elusive. Third, evaluation benchmarks are insufficient. Current static offline tests do not adequately capture the dynamic, adaptive nature of real-world conversations, leaving a gap between lab metrics and user experiences.

Moreover, challenges in tool utilization, such as improper function selection or execution errors, highlight weak metacognitive awareness in agents. Policy adherence deteriorates over lengthy dialogues, as agents struggle to recall complex rule sets across multiple conversational turns. Personalization techniques remain shallow, often limited to basic memory retrieval without deep emotional modeling or dynamic preference adaptation.

What future directions are critical for transforming conversational AI?

To bridge these gaps, the research outlines a bold future research roadmap. Priorities include developing memory-augmented architectures and in-context learning techniques to sustain multi-turn reasoning across long dialogues. Strengthening agents' self-evolution mechanisms through real-time reward modeling and online policy updates is considered vital for scalable deployments.

Evaluation methods must evolve toward online, interactive testing that measures not only task success but also user satisfaction, cognitive load, and conversational efficiency. New benchmarks should capture real-world dynamics, shifting beyond simple scripted dialogues.

Collaborative and multi-agent task frameworks are proposed as key to scaling complex task completions, where multiple agents coordinate to achieve goals beyond the capability of a single model. Future agents should also focus on deeper, more dynamic personalization, learning user preferences, emotions, and behavioral patterns with minimal demonstrations, not just static memory.

Lastly, a strong emphasis is placed on developing proactive agents. Rather than passively responding to queries, proactive agents should anticipate user needs, take initiative, and engage in dialogue planning. Coupled with advances in multimodal capabilities, such as integrating speech, vision, and environmental cues, these directions could unlock new realms of human-like communication.