Every Agent Decision. Fully Traceable.
When your AI agent makes a decision, who's responsible? JieGou's responsibility chain provides full traceability from intent to outcome — audit logging, tool approval gates, escalation protocols, and compliance evidence export.
The Responsibility Chain
Five stages of traceability — from the moment a user makes a request to the final audit record.
User Intent
What the user asked the agent to do
Captured
Input logged with timestamp, user ID, department
Agent Reasoning
How the agent decided what to do
Captured
LLM call traced with model, tokens, reasoning
Tool Approval
Which tools were requested and approved
Captured
Approval gate with approver, decision, timestamp
Execution
What the agent actually did
Captured
Tool calls, results, duration, cost logged
Audit Trail
Complete record for compliance
Captured
Evidence export, compliance timeline, OTel trace
The Untraceable Responsibility Problem
Autonomous agents create three responsibility gaps that traditional compliance models don't address.
Agents Reason Autonomously
When an LLM decides how to approach a task, the reasoning is opaque. Who's accountable for the chain of thought that led to a decision? Without logging, the reasoning vanishes the moment the token generation ends.
Agents Select Tools Dynamically
Autonomous agents choose which tools to use at runtime — sending emails, querying databases, calling APIs. Who approved that tool access? Who verified the tool was appropriate? Without approval gates, tool selection is unauditable.
Agents Adapt Behavior
Agents that learn from context, adjust strategies, and change approach mid-task create decisions that are different every time. How do you audit a decision that never repeats? Without persistent tracing, adaptive behavior is invisible.
How JieGou Traces Every Decision
Five capabilities that create a complete, auditable responsibility chain for autonomous agents.
action types
Audit Logging
Every interaction, every decision, every API call — fire-and-forget logging that never throws. 30 action types captured with user context, timestamps, and department scope.
human-in-the-loop
Tool Approval Gates
Explicit approval before agents use sensitive tools. Multi-approver policies, escalation chains, and timeout-based fallback. No tool call happens without a governance decision.
RBAC roles
Escalation Protocols
Cascading agent hierarchy with configurable escalation rules. When an agent encounters uncertainty or risk, it escalates — never proceeds unilaterally. Every escalation is logged.
timeline view
Compliance Timeline
Visual timeline of all governance events — approvals, escalations, tool calls, cost events — in chronological order. Auditors see the complete decision history at a glance.
TSC controls
Evidence Export
Export compliance evidence structured for auditors — 17 Trust Service Criteria controls across 8 categories. OTel-compatible trace export with governance-enriched attributes.
Regulatory Alignment
How the responsibility chain maps to specific regulatory requirements.
| Regulation | Requirement | JieGou Implementation |
|---|---|---|
| EU AI Act Art. 12 | Record-Keeping | Audit logging captures every agent action with timestamps and user context |
| EU AI Act Art. 14 | Human Oversight | Escalation protocols and tool approval gates ensure human-in-the-loop |
| EU AI Act Art. 50 | Transparency | Agent disclosure and interaction logging for all AI-generated content |
| NIST Focus Area | Monitoring & Incident Response | Operations Hub, circuit breaker, DLQ, compliance timeline |
Full Traceability for Every Agent Decision
Audit logging. Tool approval gates. Escalation protocols. Evidence export. The complete responsibility chain.