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Instrument human approval workflows, feedback loops, and escalations
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Baseado na classificação ocupacional SOC
Trace agent decision-making, tool selection, and reasoning chains
Instrument safety checks, content filters, and guardrails for agent outputs
Strategies for evaluating agents in production - sampling, baselines, and regression detection
Track prompt versions, A/B test variants, and measure prompt performance
Instrument error handling, retries, fallbacks, and failure patterns
Instrument evaluation metrics, quality scores, and feedback loops
| name | human-in-the-loop |
| description | Instrument human approval workflows, feedback loops, and escalations |
| triggers | ["human approval tracking","human in the loop","approval workflow","human feedback","escalation tracking"] |
| priority | 2 |
Instrument human intervention points to understand approval workflows and feedback.
HITL observability answers:
Agent requests permission before proceeding:
span.set_attribute("human.type", "approval")
span.set_attribute("human.action_requested", "execute_trade")
span.set_attribute("human.risk_level", "high")
span.set_attribute("human.auto_approve_eligible", False)
Agent needs more information:
span.set_attribute("human.type", "clarification")
span.set_attribute("human.question", "Which account to use?")
span.set_attribute("human.options_provided", 3)
Agent encountered unrecoverable error:
span.set_attribute("human.type", "escalation")
span.set_attribute("human.reason", "tool_failure")
span.set_attribute("human.error_type", "APIError")
span.set_attribute("human.retry_count_before_escalation", 3)
Agent requests quality feedback:
span.set_attribute("human.type", "feedback")
span.set_attribute("human.feedback_on", "response_quality")
span.set_attribute("human.optional", True)
# Request metadata (P0)
span.set_attribute("human.request_id", str(uuid4()))
span.set_attribute("human.type", "approval")
span.set_attribute("human.agent_name", "executor")
span.set_attribute("human.timestamp", datetime.utcnow().isoformat())
# Context (P1)
span.set_attribute("human.action_summary", "Delete 50 records")
span.set_attribute("human.risk_assessment", "medium")
span.set_attribute("human.reversible", True)
span.set_attribute("human.deadline", "2024-01-15T10:00:00Z")
# Routing (P2)
span.set_attribute("human.assigned_to", "team_ops")
span.set_attribute("human.priority", "high")
span.set_attribute("human.channel", "slack")
# Response metadata (P0)
span.set_attribute("human.response_id", str(uuid4()))
span.set_attribute("human.decision", "approved") # approved, rejected, modified
span.set_attribute("human.responded_at", datetime.utcnow().isoformat())
span.set_attribute("human.wait_time_ms", 45000)
# Responder (P1)
span.set_attribute("human.responder_id", "user_123") # Hashed
span.set_attribute("human.responder_role", "admin")
# Modifications (if applicable)
span.set_attribute("human.modified", True)
span.set_attribute("human.modification_summary", "Reduced to 25 records")
# Feedback (if applicable)
span.set_attribute("human.feedback_score", 4) # 1-5
span.set_attribute("human.feedback_comment_length", 150)
Track wait times for SLA monitoring:
# Request-to-response timing
span.set_attribute("human.time_to_response_ms", 45000)
span.set_attribute("human.time_to_first_view_ms", 12000)
span.set_attribute("human.sla_target_ms", 300000) # 5 min SLA
span.set_attribute("human.sla_met", True)
# Queue metrics
span.set_attribute("human.queue_position", 3)
span.set_attribute("human.queue_wait_ms", 30000)
Agent blocks until human responds:
with tracer.start_span("human.approval_sync") as span:
span.set_attribute("human.blocking", True)
span.set_attribute("human.timeout_ms", 300000)
request_id = request_approval(action)
decision = wait_for_decision(request_id, timeout=300)
span.set_attribute("human.decision", decision)
span.set_attribute("human.wait_time_ms", elapsed)
Agent proceeds with other work:
with tracer.start_span("human.approval_async") as span:
span.set_attribute("human.blocking", False)
span.set_attribute("human.callback_url", callback_url)
request_id = request_approval_async(action, callback_url)
span.set_attribute("human.request_id", request_id)
# Agent continues with other work
Low-risk actions auto-approve:
with tracer.start_span("human.auto_approval") as span:
risk = assess_risk(action)
span.set_attribute("human.risk_score", risk)
if risk < RISK_THRESHOLD:
span.set_attribute("human.auto_approved", True)
span.set_attribute("human.override_available", True)
proceed(action)
else:
span.set_attribute("human.auto_approved", False)
request_approval(action)
Track patterns over time:
# Per-agent metrics
span.set_attribute("agent.human_requests_today", 15)
span.set_attribute("agent.approval_rate", 0.85)
span.set_attribute("agent.avg_wait_time_ms", 35000)
# Per-workflow metrics
span.set_attribute("workflow.human_touchpoints", 3)
span.set_attribute("workflow.autonomous_steps", 12)
span.set_attribute("workflow.automation_rate", 0.80)
from langgraph.prebuilt import create_react_agent
from langfuse.decorators import observe
@observe(name="human.interrupt")
def human_approval_node(state):
span = get_current_span()
span.set_attribute("human.type", "approval")
span.set_attribute("human.state_summary", summarize(state))
# Request human input
decision = interrupt(state)
span.set_attribute("human.decision", decision)
return {"approved": decision == "approve"}
from crewai import Agent
from langfuse.decorators import observe
@observe(name="human.feedback")
def request_human_feedback(output: str) -> str:
span = get_current_span()
span.set_attribute("human.type", "feedback")
feedback = human_input(f"Review: {output}")
span.set_attribute("human.feedback_length", len(feedback))
return feedback
error-retry-tracking - Escalation patternsevaluation-quality - Feedback integration