I’m working on improving my AI agent setup and trying to figure out what I should be measuring. Most people seem to focus on testing with evaluation datasets using tools like langsmith or custom solutions. They also keep track of prompt modifications with things like promptlayer.
But I think there are way more factors that affect how well agents work. Things like the overall design choices - should I use context trimming, summary generation, or scratch pads? What about storing the scratch pad data in vector format? Which memory storage format works best? Then there are the models themselves and settings like temperature values.
What specific metrics and settings do you guys track when optimizing your agents? Are there any good tools for this?
Also curious if anyone knows about tools or research that can automate some of this optimization work. Kind of like how DSPy automatically optimizes prompts - is there something that could use a meta-AI system to suggest what to try next based on evaluation results and tracked parameters? Maybe something that could even pull ideas from online resources to recommend improvements.
I’ve found that tracking response time variance is crucial, especially after experiencing inconsistencies with agent responses. A simple prompt can yield varying processing times, which really detracts from the user experience. I analyze standard deviation for similar queries to pinpoint performance issues quickly. Additionally, I use a metric I refer to as the ‘context coherence score’, which measures how well agents maintain conversation threads. There have been numerous instances where agents overlook prior context despite it being available in memory. For automation, I developed some scripts that help identify failure patterns and recommend parameter adjustments. While it’s not as comprehensive as DSPy, monitoring how temperature settings impact task success has allowed me to refine optimal ranges for various scenarios. The key takeaway: meticulous logging is essential; even minor configuration changes can unpredictably affect agent performance.
Memory retrieval precision matters most in my experience. I track how often my agent grabs relevant context vs junk from vector stores - just compare what gets retrieved against what actually makes it into the final response. Temperature between 0.3-0.7 works for most stuff, but I log output variance to nail down the sweet spot. For architecture choices, measuring context window usage helps me decide between trimming vs summarization. If you’re consistently hitting 80%+ context usage, summarization beats simple trimming every time. Tool usage success rate’s worth tracking too - how often does your agent actually complete multi-step workflows without getting stuck or making bad tool calls? For automation, I haven’t seen anything as comprehensive as what you’re describing. Some teams are building custom feedback loops that tweak parameters based on user satisfaction scores and task completion metrics though.
Error handling rates reveal way more about real-world performance than most people think. I wasted months chasing accuracy scores while our agent crashed on 15% of edge cases that test datasets never caught.
I track failure recovery patterns now - when things break, does the agent retry smartly or just quit? This one metric showed me our context summarization was actually hurting performance on certain queries.
For meta optimization, nothing matches DSPy for agents yet. I built something similar with basic RL principles though. It tracks which parameter combos work for different task types and suggests experiments based on past wins.
Categorize your tasks first - that’s the key. One temperature setting can’t handle both creative writing and data analysis. I log task type with all metrics, so the system knows to suggest “lower temperature” for analytical stuff or “bigger context window” for complex reasoning.
Here’s what nobody mentions - hallucination drift in long conversations. Track how accuracy drops as chats get longer. Sometimes starting fresh every N turns beats trying to keep everything in memory.
i feel ya! tracking latency and token costs is super useful, more than just accuracy. i found that looking at completion rates can give a better sense of how the models actually perform in real situations. users def behave differently than what’s on paper.