Intake triage.
A confidence-routed classifier that labels inbound message intent, resolves what it is sure of, and queues everything else for human review.
Prototype — built and exercised on real data, not running in production.
Classified as an agent: it holds a defined responsibility and chooses, recommends or routes a permitted action from context.
A model does part of the work here, under a fixed prompt. It interprets or generates; it is not what grants this entry its authority.
Objective
Establish the intent of an inbound message where it can be determined with high confidence, and route the remainder to an operator queue sized against measured reviewer capacity.
Runtime
01
Assemble
Selects the first message of each conversation thread as the intent-bearing one.
02
Classify
Labels intent under an open taxonomy, instructed to prefer precision over recall.
03
Consolidate
Maps raw labels onto a canonical set and records the confidence for each.
04
Resolve
Applies a label automatically only above the configured confidence threshold.
05
Escalate
Materialises everything below the threshold into a queue for operator judgment.
Agent anatomy
Context
- Inbound message headers and body
- Thread grouping and ordering
- Sender identity resolution with provenance
- Canonical label vocabulary
- Measured reviewer capacity
Capabilities
- Identify the first message in a thread
- Classify intent under an open taxonomy
- Consolidate raw labels to a canonical set
- Apply a label above a confidence threshold
- Queue the remainder for human review
Activation
- Batch run over a message corpus
Authority
What can it change?
- Autonomy
- Escalating
- Horizon
- Batch
Read
- Message metadata and body snippets
- Prior label assignments
- Identity match records
Write
- Canonical intent label on a classified row
- The unresolved-review queue
Conditional
- Overwrite an unresolved label only above the confidence threshold
Prohibited
- Act on any downstream business record
- Resolve a label below the threshold
- Discard an unresolved row silently
Escalate
- Confidence below threshold
- Intent returned as unknown
- Identity resolved only by weak domain fallback
Composition and verification
Made from explicit parts.
Composition
- Batch classification pipeline
- Language model under a fixed prompt
- Canonical label consolidation
- Confidence threshold gate
- Operator review queue
Verification
- Every automated decision carries its deciding rule and threshold
- Identity matches record how they were established
- Model instructed to abstain rather than guess
- Reviewer burst capacity measured before the queue was designed
- Unresolved rows are queued, never dropped
No evidence is published for this entry. The items above are our own account of its source, read before publishing — not something you can check without taking our word for it.
Next step
What you can do with this.
This configuration is not running in production. What can be discussed is its design and the real data it was exercised on — not a service in operation.
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