Session Abstraction Ladder
One session shown from raw evidence to reducer output to topology to narrative without breaking trust or orientation.
Skill Proof Run
wf1 · proving that bob-wire can sketch transformation, not just static state.
Every layer below is anchored to the same selected concept: a credential-adjacent file cluster associated with outbound model traffic.
Live mode keeps the ladder attached to a moving session; new evidence can still enter the lower layers while the higher ones remain stable.
Raw Evidence Rail
14:02:07
fileRead ~/.aws/config from working session
14:02:11
networkOutbound request to api.openai.com
14:02:18
tooltool.call.open_file on config.ts
14:03:02
fileTouched .env.local and credentials cache
Reduction Layer
merged record
Credential-adjacent file cluster
Low-level reads grouped into one higher-level file concept.
flow object
Primary model traffic flow
Repeated outbound model calls collapsed into one durable session flow.
decision note
Context retained
Adjacent file touches kept because they explain the network burst.
decision note
Noise collapsed
Repeated cache reads compressed to keep the ladder scannable.
Topology Layer
Agent Cluster
cursor-agent
root actor
tool planner
reducer group
Selected File Cluster
credential-adjacent files
3 direct touches + 2 inferred references
External Flow Cluster
api.openai.com
42 requests
temporary object store
secondary context
Narrative Layer
Narrative claim 1
The session repeatedly touched credential-adjacent files before and during outbound model traffic.
Narrative claim 2
Reducer output turns low-level file noise into one meaningful cluster the user can reason about.
Selection Sync
Selected Concept
credential-adjacent file cluster
Highlighted across raw events, reducer output, topology, and narrative.
Coverage State
Direct file touches are observed; some semantic meaning is inferred from neighboring requests and reducer grouping.
Reducer Decisions
Why these records merged
They share a tight time window, a common session id, and repeated path affinity.
Why some records were dropped
High-volume repeated cache accesses added little explanatory value once grouped.