Observability
Type: index · Status: current
Observability is about recovering signals that would otherwise stay hidden: execution paths that differ from the intended one, quality drift that has not yet become a visible failure, and system state that operators need in order to debug, maintain, and improve the runtime.
Runtime visibility
- tool loop — inspectable orchestration is a precondition for seeing how a run actually progressed rather than inferring from the final artifact
- Apparent success is an unreliable health signal in framework-owned tool loops — successful outcomes can hide broken helpers and degraded execution paths, so final success alone is not a trustworthy operational signal
- Silent disambiguation is the semantic analogue of tool fallback — extends the same observability problem to underspecified specs: a useful artifact can hide that the contract did not determine the path and the runtime repaired it locally
- Traditional debugging intuitions break when tool loops can recover semantically — explains why programmers over-trust successful outcomes when semantic recovery hides the broken mechanism
Detection & Signals
- Quality signals for KB evaluation — catalog of weak signals that can make hidden quality changes visible enough to drive maintenance or learning loops
- Notes need quality scores to scale curation — compresses many weak signals into ranked note quality so curation effort goes where it matters
- Link graph plus timestamps enables make-like staleness detection — dependency-aware staleness detection turns silent drift into an explicit review queue
- Semantic review catches content errors that structural validation cannot — adversarial reading supplies visibility into content failures that deterministic validation never surfaces
Inspectable Substrate
- Inspectable substrate, not supervision, defeats the blackbox problem — the substrate choice determines whether failures and drift can be inspected, diffed, tested, and verified at all
Related Tags
- KB maintenance — maintenance consumes the signals observability exposes
- Computational model — runtime architecture determines which state transitions are inspectable
- Learning theory — soft signals, oracle strength, and inspectable artifacts explain what observability can reliably support
- LLM interpretation errors — error correction and verification theory explain how visible signals can become actionable
Other tagged notes
- Trace-derived learning techniques in related systems — Sixteen code-inspected systems compared on trace ingestion pattern, promotion target (symbolic artifacts vs weights), artifact structure spectrum, and maintenance paths