Building named-dimension signal vectors to cluster and search agent traces
HanchungLee · x · 2026-09-26
annabellschfr suggests building an interpretable "embedding" of agent traces where every dimension is named — e.g. chat signals [userfrustrated, followup, correctedagent, satisfied] with values like [0.7, 0.8, 0.1, 0.0]. You can then cluster or search sessions like "frustrated users who had to correct the agent" and see exactly why each one matched; searching happy users is equally useful. HanchungLee adds that any off-the-shelf LLM can do this, no special tooling required.
More from coding & agent
- Boris Cherny's viral tweet puts TLA+ and formal methods in the agent-coding spotlight — fhuszar · 2026-09-26
- Two local AIs talk to each other with no cloud: hands-on with Braid — Scobleizer · 2026-09-26
- Open-source EvoOntology lets agents build and evolve a data ontology via MCP — TheTuringPost · 2026-09-26
- W&B's ARIA agent runs 200+ autoresearch experiments, nearly beats its best result live — AI Engineer · 2026-09-26
- Making GA4 Agent-Friendly: Building Analytics Tools for an MCP Server — DutchSEOnerd · 2026-09-26
- Loop engineering beats prompt engineering: build agent loops that self-verify — goyalshaliniuk · 2026-09-26