8 Biggest Unsolved Problems in Evaluating AI Agents Today: Trajectories, Regressions, and Production Gaps
Alternative_Duck_908 · reddit · 2026-08-26
This post explores the major challenges in evaluating and debugging AI agents running in production, including:
- Multi-step/long-running trajectories: How to measure performance over complex task chains.
- Hallucination vs. correctness: Distinguishing between actual task completion and plausible-sounding failures.
- Tool-call and workflow correctness: Verifying the accuracy of tool usage and orchestration.
- Multimodal evaluation: Assessing voice, video, and other modalities.
- Regression detection: Catching performance degradations when models, prompts, or tools change.
- Open-ended evaluation: Measuring success in scenarios without clear ground-truth answers.
- Offline-to-online gap: Bridging the disconnect between offline eval scores and real-world production outcomes.
- Debugging failures: Moving from knowing that an agent failed to understanding why.
The author seeks examples of specific pain points that existing observability tools fail to solve for practitioners operating agents in production.
Related event: Key Challenges in Evaluating AI Agents in Production(2 posts)→
More from coding & agent
- Developer automates house viewing schedule with a Tinder-style app — yacineMTB · 2026-08-26
- Developer automates house viewing schedule with a Tinder-style app — yacineMTB · 2026-08-26
- stealth-browser-mcp: undetectable browser sessions for MCP agents — tom_doerr · 2026-08-26
- Recuris: Recursive Experiential-Working Memory Evolution for Long-Horizon Agents — princetonu · 2026-08-26
- Tencent's CAFE: Self-Improving Search Agents Need Co-Evolving Feedback — Tencent-Hunyuan · 2026-08-26
- Evaluating work by token consumption treats waste as training costs — mazzaTalk · 2026-08-26