I decided to shift analysis focus to include successful trace segments, not just failures, because successful traces reveal conditions, actions, and contexts that lead to desired outcomes and provide positive examples for training or debugging.
I am building tooling to automatically identify and extract “win” segments from logs, using rule‑based success criteria, tagging, segment extraction, and feature mining to feed positive examples into replay buffers or imitation‑learning pipelines.
Decision: run a targeted t^2-bench experiment to expose gaps in the observability tool’s loss coverage.
The experiment compared raw loss data from the benchmark with the tool’s observed losses, and the results were described as “really interesting.”
Decision: Capture and analyse successful trace segments rather than only failures.
Decision: Launch benchmarks and case studies once the extraction pipeline is operational.
I realized that production traces of my autonomous agents focus almost entirely on failure cases, and engineers rarely examine the segments where the agents actually succeeded.
Mostly engineers fail to look at the good parts of the traces.
To address this, I decided to explicitly extract and surface successful trace segments for analysis, because understanding what works provides complementary insights to failure analysis and lets me reinforce effective strategies.
- Trace Segmentation: parse raw logs for outcome markers (e.g., goal‑achieved flags) and split into failure and success windows.
In a pilot on 500 agents over two weeks, I identified 1,240 success windows and 3,876 failure windows, which helped raise overall task completion by several percent and cut engineer review time in half.
- Feature Extraction: for each success window, capture state‑action pairs, environmental conditions, and reward values, mirroring the existing failure pipeline.
- Visualization: build dashboards showing success and failure windows side‑by‑side to highlight recurring successful patterns.
- Feedback Loop: feed successful patterns back into training as positive examples or priors.