Overview
Role
Product Designer
Industry / Product
Enterprise B2B AI Platform (NDA)
I worked on an enterprise B2B AI platform that enables developers to build, test, and deploy agent-based AI systems connected to high-risk domains such as HR and Finance.
When developers started building agent-based AI, they could tell something went wrong, but not why. So, the solution focused on building trust, explainability, and compliance.
Due to NDA, product names and visuals are abstracted. Contact me to view the presentation deck.
The Problem
Enterprise AI systems don’t fail loudly, they fail silently. As the platform evolved toward agentic AI, ensuring trust, explainability, and compliance became critical.
When we started building agent-based AI connected to HR and Finance systems, developers could tell something went wrong, but not why.
Failures felt like black-box errors:
Was the tool even called?
Which agent made the decision?
Where did the logic break?
Debugging meant guessing across chat logs, flow diagrams, and system traces.
My work focused on one critical question:
How do we make autonomous AI systems understandable enough for developers to trust?
The Strategic Decision
The strategic risk:
High Mean-Time-to-Debug
Low confidence in deploying agents to production
Slower platform adoption across enterprise teams
We explored simplifying the interface by hiding internal complexity. However, for enterprise systems connected to HR and Finance, trust and auditability outweighed surface-level simplicity.
We chose to design for transparent reasoning, despite the higher initial complexity.
My Role & Scope
My responsibility was a critical workflow within a large platform: the debugging experience inside the developer playground, where agents are tested and validated.
I partnered closely with the product manager, engineers and other designers to answer three important questions:
What reasoning should be visible?
How to expose failures without overwhelming users?
Where abstraction helped vs harmed trust?
Solution: Design Goal
Help developers understand what the agent did and why, before diving into low-level system details.
Key Design Decisions
Debugging as a trust problem
Treating failures as explainability gaps rather than tooling gaps.
Transparency over efficiency
Optimized for long-term confidence in enterprise environments.
Progressive disclosure
High-level reasoning first, deep logs when needed.
Designed for scale
Faster time to debug meant faster production time.
We designed a timeline-based debugging experience that:
Visualized agent handoffs step-by-step
Showed tool invocation and reasoning inline
Flagged failures at the exact point of breakdown
Allowed retrying from specific steps
This eliminated context-switching and made agent behavior explainable.
Impact
This work significantly improved the developer experience:
Mean-Time-to-Debug reduced by over 40%
Developer confidence increased when deploying agents
Platform adoption expanded from early beta users to multiple enterprise teams
The debugging experience became a key enabler for scaling agent-based AI across the platform.
Reflection
This project reinforced a few key lessons:
In enterprise AI, trust and explainability are prerequisites for adoption
Designing for developers (DX) directly improves end-user experiences (EX)
Clear mental models matter more than exposing every system detail
I’m happy to discuss further, feel free to connect!