Agent != Conversational

In the rapidly evolving landscape of AI agents, a curious misconception has taken root: the notion that “agentic” inherently means “conversational.” This conflation is understandable given the prevalence of chat-based demos that dominate social media and product launches, but it represents a fundamental misunderstanding of what agents are actually designed to accomplish.

The Conversational Trap

When most people think of AI agents today, they envision a chat interface where they type commands and the agent responds. This pattern has become so ubiquitous that many equate agentic capabilities with the ability to have a sophisticated conversation. Popular demos further reinforce this association - we watch videos of people instructing agents through chat to perform complex sequences of actions.

But this conversational paradigm, while intuitive and accessible, introduces significant inefficiencies:

1. Conversational overhead: Dialogue requires constant back-and-forth communication. Certain steps which require multiple back and forth conversations may be better handled by a simple 1-2 click interface.

2. Humans Need Cues: Humans have become heavely dependant on visual cues, and conversational interfaces don’t provide these cues. The way most systems solve that now is by showing example questions we can ask an agent.

3. Textual Context Overload: Conversational interfaces eventually create a large amount of textual content some maybe interfaced with visual cues, but with the decline of attention spans across humanity higher textual content is not a good idea.

Reclaiming the True Purpose of Agents

The primary goal of agentic systems isn’t to chat - it’s to deliver extraordinary efficiency gains. The most successful agents should aim for 300%+ improvements in productivity and effectiveness. This level of transformation simply cannot be achieved if we remain fixated on conversational interfaces as the default mode of interaction.

Conversations work well for certain use cases, not all. High-value applications, conversational interfaces introduce unnecessary friction.

Alternative Agent Interaction Models

Forward-thinking teams should be exploring more efficient interaction paradigms:

Ambient Agents

Ambient agents operate in the background, continuously monitoring context and intervening only when necessary. Rather than waiting for explicit commands, they observe user behavior, anticipate needs, and take appropriate actions with minimal disruption to workflow. (/posts/evolving-ai-agent-ux)

These agents excel in environments where:

• Tasks follow predictable patterns
• Interventions should be minimally disruptive
• Context can be readily observed

Voice-First Agents

Voice interfaces offer a promising middle ground between conversational and ambient models. They maintain the intuitive nature of natural language while reducing the friction of text-based back-and-forth. When designed thoughtfully, voice agents can:

• Eliminate the context switching required for typing
• Leverage paralinguistic features (tone, pacing) for improved understanding
• Operate hands-free in environments where typing is impractical [/posts/voice-agents-future-of-interaction]

Programmatic Agents

For developers and technical users, programmatic interfaces that allow direct API calls to agent capabilities often prove far more efficient than chat-based interactions. These interfaces:

• Enable precise control over agent behavior
• Facilitate integration with existing workflows and tools
• Support automation without conversational overhead

Measuring Success: Efficiency Over Engagement

The ultimate metric for agent success isn’t how well they chat - it’s how dramatically they improve efficiency. The most valuable agents might have minimal direct interaction with users while delivering outsized productivity gains.

When evaluating agent designs, organizations should ask:

• Does this agent reduce cognitive load or add to it?
• How much human attention does the agent require to deliver value?
• Could the same outcome be achieved with less explicit interaction?

The Path Forward

As the field matures, we need to decouple our understanding of agency from conversation. The most transformative agent experiences may involve minimal dialogue, operating seamlessly in the background while delivering dramatic productivity improvements.

The next generation of agents will likely feature multimodal interfaces that adapt to context - conversational when exploration is needed, ambient when patterns are established, voice-driven when hands are occupied, and programmatic when precision is paramount.

By breaking free from the conversational paradigm, we unlock the true potential of agents: not as chat partners, but as efficiency multipliers that fundamentally transform how work gets done.