Building AI Agents That Improve From Production Data
Overview
Most AI agents get deployed and never get better. Teams ship v1, monitor for crashes, and move on while every production conversation sits there full of signal about what's actually breaking and how to fix it. For fast-moving AI teams, every iteration without a feedback loop means compounding quality debt in production while your users are the ones finding the bugs.
Description
This session builds the complete closed-loop reliability pipeline live: from capturing production traces and identifying systematic failures to auto-generating test suites from real conversations, optimizing prompts with measurable results, and deploying real-time guardrails. We'll take a real working agent that's already handling live traffic and wire up the entire feedback loop so it improves from its own data every week without manual review.
Agenda:
Instrumenting Your Agent for Production Visibility: Full tracing and in-line quality scoring in under few lines of code
Finding Systematic Failures in Production Data: Running structured evaluations to surface patterns, not just individual errors
Turning Real Failures Into Automated Test Suites: Creating and expanding scenario-based tests from actual production conversations
Prompt Versioning and Evaluation-Driven Optimization: Treating prompts like code with CI/CD integration and measurable A/B testing
Real-Time Guardrails as the Safety Net: Production-grade content, bias, injection, and PII screening on every request
Closing the Loop, From Detection to Fix in Hours: Connecting all five parts into one continuous improvement pipeline
Q&A and Discussion
Key Takeaways / Learning Outcomes
Attendees will walk away with the architecture for agents that get smarter every week on their own. No more manually reviewing conversations, writing test cases by hand, or guessing whether a prompt change actually helped. Ship faster, break less, measure everything.
Who Should Join???
This session is designed for agentic-era product engineering teams who have AI agents in production (or approaching deployment) and want a systematic, data-driven approach to continuously improving agent quality rather than relying on manual log reviews and ad-hoc prompt tweaking.
About Future AGI
Future AGI is a San Francisco-based advanced agent engineering & optimization platform that helps you ship reliable and self-improving AI. Its core workflow follows a Simulate → Evaluate → Optimize → Protect pipeline: it simulates real-world scenarios by generating diverse synthetic datasets including edge cases to rigorously test AI agents before deployment. It then evaluates agents across multiple modalities (text, image, audio), pinpointing errors with precision. The platform optimizes performance by automatically refining prompts, comparing agentic workflow configurations, and incorporating evaluation feedback to close the improvement loop. Finally, it protects applications in production through real-time observability, diagnostics, and built-in safety metrics that block unsafe content with minimal latency.
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