AI agent workforce
Run AI as bounded, attributable workers with signed context, least-privilege tools, expiring sessions, action-level policy, human oversight, and one causal audit trail.
LendEasy treats an AI agent as a worker identity—not an invisible feature inside a screen. It receives a task, opens a bounded session, acts only through declared tools, produces attributable output, and ends in an explicit state. It never borrows a human’s identity or permission.
Control stack
tenant policy and suppression
↓
task eligibility + autonomy ceiling
↓
signed work packet + least-privilege tools
↓
AI session budget and model/prompt version
↓
tool-level authorization + execution-time compliance
↓
output review / governed action gateway
↓
quality measurement + evidence graph
Every layer can narrow authority. No layer can widen the ceiling established above it.
Session lifecycle
Start a session
curl -X POST "$BASE/v1/servicing/ai-sessions" \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: ai-session-case-hbr-col-51-next-action" \
-d '{
"caseId": "case_HBR_COL_51",
"agentProfile": "collections_assistant",
"objective": "Summarize the case and recommend the next compliant step.",
"autonomyLevel": "A2"
}'
The response pins workPacketVersion, session expiry, and effective autonomy. The requested level may be reduced by task, channel, sensitivity, jurisdiction, model qualification, or current quality policy.
Tool boundary
Reading a masked balance, drafting a message, preparing a promise, submitting an interaction, and changing a loan are different tools. Each has its own input schema, permission, allowed session profile, compliance gate, idempotency behavior, and evidence.
An agent cannot call a raw database, provider SDK, or hidden account command. Financial and contract changes go through the same public typed action path as authorized humans.
Output attribution
AI output retains agent profile, model and prompt versions, work-packet digest, citations, tool ledger, confidence, produced time, and any reviewer edits. Human approval does not relabel AI-authored content as human-authored; the graph shows both contributions.
Failure behavior
The agent returns missing_facts instead of estimating an authoritative balance, declined instead of acting outside scope, and stopped when suppression arrives. Partial text and tool calls remain evidence. A failed session never marks its task complete automatically.
Chained agents
Triage, summarization, recommendation, and drafting may form a chain, but the chain’s effective action space is the intersection of its controls. Splitting a prohibited action across agents does not make it permitted, and one agent cannot approve another agent’s high-impact action where human approval is required.
Explore Work packets & grounding, the Autonomy ladder, and Human review & kill switch.