MO-SAEED
Launch-ready2025

A transparent AI workspace for computational chemistry.

El-Agente interface preview: A transparent AI workspace for computational chemistry.
Role
Product design lead
Engagement
Platform design
Focus
AI product design · Scientific software · Agentic UX · Complex workflows

01 / Context

The product and its operating environment

An agentic scientific discovery platform that turns natural-language questions into traceable computational chemistry workflows.

02 / Challenge

The complexity worth solving

AI had to remain powerful without becoming unpredictable.

El-Agente is a scientific AI product for computational chemistry workflows. The challenge was turning complex agent behavior, generated outputs, and scientific data into an experience expert users could understand, inspect, edit, and trust.

03 / Product direction

From AI interaction to controlled scientific workflow
01

Understand

I structured the experience around the relationship between user intent, agent reasoning, generated work, and explicit human approval.

02

Structure

The interaction model separated what the agent was doing from what required user review or action, with clear states throughout the workflow.

03

Deliver

The resulting product direction combined natural-language interaction with structured outputs, transparent feedback, and editable scientific results.

04 / Key decisions

Three moves that shaped the product
01

Agent states made visible

Idle, reasoning, proposing, awaiting approval, complete, and failed states were surfaced explicitly so users always understood what the system was doing.

02

Approval before action

Model proposals were separated from user-approved actions, preserving expert control rather than allowing the experience to behave like an opaque autonomous chatbot.

03

Structured outputs over chat alone

Scientific results were presented through editable outputs and data visualization rather than being buried entirely inside conversation history.

05 / System

Designed for evolving AI behavior

A system built around transparency and control.

Reusable patterns for agent states, approvals, feedback, outputs, and failure handling created a foundation that could support increasingly complex AI workflows without losing transparency.

07 / Outcome

What changed
A clearer direction for an expert AI product

The product direction received stakeholder approval and moved into development with a clearer interaction model for agent behavior, scientific outputs, and human control.

08 / Reflection

What I carried forward
For expert AI products, trust comes from visibility and control, not from making the intelligence disappear.
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