Building AI Agents That Self-Improve from Production Data

ABOUT THE WEBINAR

In this session, Rishav and Kartik walk you through what it takes to stop guessing why your AI agents are failing in production and start fixing them systematically. From instrumenting a real voice agent to auto-optimizing its prompts using production data, this is a practical, demo-first deep dive into the modern AI engineering loop.

👉 Who Should Watch

This webinar is ideal for AI engineers, ML practitioners, and product teams building or maintaining AI agents in production, especially those dealing with prompt drift, hallucinations, or inconsistent agent performance.

🎯 Why You Should Watch

This isn't just another AI demo, it's a masterclass on:

• Understanding why even the best AI agents succeed on only 24% of real-world tasks and what's actually broken

• How to instrument a production voice agent with one-line tracing to capture every conversation in real time

• Using evaluation frameworks to automatically surface failing sessions 

• Replaying production failures to generate targeted stress-test scenarios with diverse personas and edge cases

• Running AI-powered simulations that call your agent hundreds of times so you don't have to

• Applying open-source optimization algorithms (DSPy, Bayesian search) to auto-rewrite your prompts based on eval feedback

• Setting up guardrails for prompt injection defense, data privacy compliance, and content moderation

💡 Key Insight

Discover how a voice AI agent went from a 4.8 to a 5.6 customer satisfaction score in one optimization pass without any manual prompt editing by closing the loop between production monitoring, simulation, and automated prompt improvement.

Explore how Future AGI can help you auto-optimize your AI agents in production.