Why do so many promising AI agents stall before reaching production? The answer often lies in one word: trust. That trust is earned through rigorous debugging. In a recent Dataiku “Let’s Talk Agents” session, “Debugging AI Agents With Precision,” Chad Covin, Senior Technical Product Marketing Specialist at Dataiku, and Martin Clark, GenAI Software Engineer II, broke down why traditional debugging tools fall short in the LLM era — and how Dataiku’s agent framework and Trace Explorer provide the transparency and control teams need to move from prototype to production with confidence.
The Rise and Risk of AI Agents
AI agents have graduated from experimental prototypes to essential business tools. According to Gartner, “By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.”* Yet despite the excitement, only 30% of AI agent pilot projects actually reach production (source). The culprit? Reliability.
Why Debugging AI Agents Is Incredibly Hard
Debugging AI agents isn't like debugging traditional software. AI models, especially those built on LLMs, introduce a unique set of challenges:
1. Hallucinations and Hidden Errors
Unlike conventional bugs that crash a system, AI agents can confidently return false but plausible information.
There is no stack trace to follow. The model might confidently make up an answer that sounds completely plausible, but is factually wrong.
— Chad Covin, Senior Technical Product Marketing Specialist at Dataiku
2. Non-Determinism and Reproducibility
Same prompt, different output — every time. Traditional debugging techniques are ineffective because you can’t reliably recreate the problem. A bug that appears once in every 10 runs is especially frustrating because you can't reliably reproduce it to fix it.
3. Lack of Transparency
LLMs often operate as black boxes. Unlike code you can step through, understanding “why” an AI agent made a decision can feel like guesswork.
Principles for Debuggable AI Agent Design
Chad Covin highlighted three design principles that simplify debugging and enhance trust:
Keep It Simple
“The most effective agents are often the least complex.”
Anthropic’s research supports this: Start with the simplest solution that works, then build complexity incrementally.
Bake in Tracing Early
“Your agent shouldn’t just log what it did, but why it made each decision.”
Structured logging from day one provides essential insights into agent behavior.
Standardize Interfaces
Consistent, well-defined formats across tools and agent components help catch and prevent data flow issues early.
The Dataiku Edge: Agent Framework + Trace Explorer
Dataiku’s agent framework brings traceability and reliability into the heart of agent design. With the flexibility to connect to any LLM — whether through code or a visual interface — Dataiku offers a platform-agnostic playground for experimentation.
But the real standout is Trace Explorer.
What Is Trace Explorer?
It’s a visual interface that allows teams to dive deep into the reasoning of their AI agents. Every call, every tool use, and every decision is captured in structured JSON with timestamps and context.
You can see your agent’s reasoning in multiple views: tree view for nested calls, timeline view for execution timing, and detailed info view for each step.
— Chad Covin, Senior Technical Product Marketing Specialist at Dataiku
A Walkthrough: From Prototype to Production
Martin Clark led attendees through an in-depth demo of building and debugging a real-world AI agent using Dataiku. The project? A clinical trial intelligence system powered by AI agents.
Step-by-Step Debugging Workflow
- Prompt Testing With Prompt Studios: Design robust prompts for summarizing trial data — complete with examples and guardrails for prompt injection.
- Trace Analysis for Accuracy and Speed: Evaluate whether caching makes sense by comparing token usage and execution time across traces.
- Multi-Retrieval Agents and Prompt Quality: See how prompt clarity and proper dataset metadata dramatically improve output reliability.
- Knowledge Bank Queries: Understand how poorly tuned retrieval settings — not the LLM — often cause query failures.
- Code Agents & Tool Integration: Use Trace Explorer to pinpoint exact steps in toolchain execution that cause breakdowns, such as a misformatted email or incorrect dataset call.
- Monitoring in Production: Attach Trace Explorer to Agent Connect to continuously monitor agent performance and resource use.
You can filter traces to see which calls are taking the most time or using the most tokens. This can highlight where a SQL query or data source may be slowing things down.
— Martin Clark, GenAI Software Engineer II
From Black Box to Blueprint
Debugging AI agents is both an art and a science. As agents grow in complexity and importance, the ability to make their behavior observable, reliable, and explainable becomes critical. With tools like Dataiku and Trace Explorer, teams no longer need to fly blind.
It’s about removing the black box. We’re helping users understand why an agent behaves the way it does — and how to make it better.
— Chad Covin, Senior Technical Product Marketing Specialist at Dataiku
*Gartner - Intelligent Agents in AI Really Can Work Alone. Here’s How. 1 October 2024, Tom Coshow. https://www.gartner.com/en/articles/intelligent-agent-in-ai