The Full Tasting Menu

Opening Scene

A full tasting menu isn’t judged course by course in isolation. Each course builds on the last, informed by everything tasted before it, working toward one coherent, extended experience. An agentic AI system with multimodal capability works this same way: perceiving and acting across genuinely mixed input types throughout a multi-step task, not just reasoning about text at each individual step.

In Plain English

Multimodal agentic systems can perceive screenshots, images, audio, or video as part of their ongoing task execution, not just process text instructions and text-based tool outputs. This meaningfully extends the agentic patterns covered in this content library’s AI agents series, letting an agent, for instance, look at a screenshot to verify a tool’s action succeeded, or listen to audio feedback as part of its decision-making process.

The Old Way

Before multimodal capability was integrated into agentic systems, agents were often limited to reasoning purely over text, even when the task genuinely involved other modalities:

• Early agentic systems typically reasoned purely over text inputs and tool outputs, even for tasks that genuinely involved visual or audio elements needing interpretation.
• Verifying that a visual action succeeded (like a button click producing an expected screen change) often required converting the result to a text description first, losing detail in the process.
• There wasn’t yet a well-established way for an agent to perceive and reason about images or audio natively as part of its own step-by-step decision-making loop.

Integrating multimodal perception directly into agentic decision loops emerged specifically once multimodal models matured enough to support this added, genuinely useful capability.

What’s Changing (and Why AI Is the Reason)

1. Agentic systems increasingly perceive images, audio, and video natively as part of their decision-making loop, extending the agentic patterns covered in this content library’s AI agents series.
2. This connects directly to the tool use patterns covered in that same series, where an agent’s tools can now return genuinely multimodal outputs, not just text.
3. Verifying task success visually — checking a screenshot after an action, for instance — has become a practical, native capability rather than a lossy text-conversion workaround.

The Metaphor, Fully Extended

The Sommelier	Multimodal Agentic Concept
Each course building on the last across an extended tasting menu	Each step building on the last across a multi-step agentic task
Perceiving flavor, aroma, and presentation throughout the meal	Perceiving text, images, audio, and video throughout task execution
One coherent experience, not isolated, disconnected courses	One coherent task, with multimodal perception at each relevant step
A palate engaged continuously, not just at the final course	Multimodal reasoning engaged continuously, not just at the final output

Quick Recap

• Multimodal agentic systems can perceive images, audio, and video natively as part of their decision-making loop.
• This meaningfully extends the agentic patterns covered in this content library’s AI agents series.
• Native multimodal perception avoids the information loss of converting everything to text first.
• This is expanding the range of tasks agentic systems can reliably verify and complete.

Where This Fits in the Series

Article 15 covered multimodal capability within agentic systems. Article 16 turns to a practical concern: the real cost of ordering the full tasting versus just a single glass.